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October 2, 2024 06:17
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| Running benches/aggregate_kernels.rs (target/x86_64-unknown-linux-gnu/release/deps/aggregate_kernels-08a843e407128dd9) | |
| Benchmarking float32/sum nonnull | |
| Benchmarking float32/sum nonnull: Warming up for 3.0000 s | |
| Benchmarking float32/sum nonnull: Collecting 100 samples in estimated 5.0236 s (752k iterations) | |
| Benchmarking float32/sum nonnull: Analyzing | |
| float32/sum nonnull time: [6.6734 µs 6.6752 µs 6.6774 µs] | |
| thrpt: [36.562 GiB/s 36.574 GiB/s 36.584 GiB/s] | |
| change: | |
| time: [-1.9423% -1.7226% -1.4927%] (p = 0.00 < 0.05) | |
| thrpt: [+1.5153% +1.7528% +1.9807%] | |
| Performance has improved. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 1 (1.00%) high mild | |
| 11 (11.00%) high severe | |
| Benchmarking float32/min nonnull | |
| Benchmarking float32/min nonnull: Warming up for 3.0000 s | |
| Benchmarking float32/min nonnull: Collecting 100 samples in estimated 5.0386 s (232k iterations) | |
| Benchmarking float32/min nonnull: Analyzing | |
| float32/min nonnull time: [21.676 µs 21.680 µs 21.685 µs] | |
| thrpt: [11.259 GiB/s 11.261 GiB/s 11.263 GiB/s] | |
| change: | |
| time: [-2.1451% -2.0743% -2.0280%] (p = 0.00 < 0.05) | |
| thrpt: [+2.0700% +2.1182% +2.1921%] | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 3 (3.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking float32/max nonnull | |
| Benchmarking float32/max nonnull: Warming up for 3.0000 s | |
| Benchmarking float32/max nonnull: Collecting 100 samples in estimated 5.0444 s (232k iterations) | |
| Benchmarking float32/max nonnull: Analyzing | |
| float32/max nonnull time: [21.684 µs 21.687 µs 21.691 µs] | |
| thrpt: [11.256 GiB/s 11.257 GiB/s 11.259 GiB/s] | |
| change: | |
| time: [-2.7259% -2.3732% -2.1046%] (p = 0.00 < 0.05) | |
| thrpt: [+2.1498% +2.4309% +2.8023%] | |
| Performance has improved. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) high mild | |
| Benchmarking float32/sum nullable | |
| Benchmarking float32/sum nullable: Warming up for 3.0000 s | |
| Benchmarking float32/sum nullable: Collecting 100 samples in estimated 5.0234 s (318k iterations) | |
| Benchmarking float32/sum nullable: Analyzing | |
| float32/sum nullable time: [15.772 µs 15.781 µs 15.789 µs] | |
| thrpt: [15.463 GiB/s 15.471 GiB/s 15.480 GiB/s] | |
| change: | |
| time: [+0.9379% +1.2276% +1.4115%] (p = 0.00 < 0.05) | |
| thrpt: [-1.3918% -1.2127% -0.9292%] | |
| Change within noise threshold. | |
| Benchmarking float32/min nullable | |
| Benchmarking float32/min nullable: Warming up for 3.0000 s | |
| Benchmarking float32/min nullable: Collecting 100 samples in estimated 5.0766 s (141k iterations) | |
| Benchmarking float32/min nullable: Analyzing | |
| float32/min nullable time: [35.879 µs 35.907 µs 35.934 µs] | |
| thrpt: [6.7941 GiB/s 6.7992 GiB/s 6.8045 GiB/s] | |
| change: | |
| time: [+0.2244% +0.3176% +0.4148%] (p = 0.00 < 0.05) | |
| thrpt: [-0.4131% -0.3166% -0.2239%] | |
| Change within noise threshold. | |
| Found 20 outliers among 100 measurements (20.00%) | |
| 13 (13.00%) high mild | |
| 7 (7.00%) high severe | |
| Benchmarking float32/max nullable | |
| Benchmarking float32/max nullable: Warming up for 3.0000 s | |
| Benchmarking float32/max nullable: Collecting 100 samples in estimated 5.0370 s (141k iterations) | |
| Benchmarking float32/max nullable: Analyzing | |
| float32/max nullable time: [35.638 µs 35.660 µs 35.685 µs] | |
| thrpt: [6.8416 GiB/s 6.8464 GiB/s 6.8505 GiB/s] | |
| change: | |
| time: [-0.3387% -0.1007% +0.0706%] (p = 0.40 > 0.05) | |
| thrpt: [-0.0706% +0.1008% +0.3399%] | |
| No change in performance detected. | |
| Benchmarking float64/sum nonnull | |
| Benchmarking float64/sum nonnull: Warming up for 3.0000 s | |
| Benchmarking float64/sum nonnull: Collecting 100 samples in estimated 5.0377 s (374k iterations) | |
| Benchmarking float64/sum nonnull: Analyzing | |
| float64/sum nonnull time: [13.476 µs 13.479 µs 13.482 µs] | |
| thrpt: [36.217 GiB/s 36.226 GiB/s 36.233 GiB/s] | |
| change: | |
| time: [-0.2179% -0.1182% -0.0493%] (p = 0.00 < 0.05) | |
| thrpt: [+0.0493% +0.1183% +0.2183%] | |
| Change within noise threshold. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 1 (1.00%) low mild | |
| 5 (5.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking float64/min nonnull | |
| Benchmarking float64/min nonnull: Warming up for 3.0000 s | |
| Benchmarking float64/min nonnull: Collecting 100 samples in estimated 5.0989 s (71k iterations) | |
| Benchmarking float64/min nonnull: Analyzing | |
| float64/min nonnull time: [72.113 µs 72.122 µs 72.133 µs] | |
| thrpt: [6.7692 GiB/s 6.7702 GiB/s 6.7711 GiB/s] | |
| change: | |
| time: [+0.1042% +0.1668% +0.2147%] (p = 0.00 < 0.05) | |
| thrpt: [-0.2142% -0.1665% -0.1041%] | |
| Change within noise threshold. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 8 (8.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking float64/max nonnull | |
| Benchmarking float64/max nonnull: Warming up for 3.0000 s | |
| Benchmarking float64/max nonnull: Collecting 100 samples in estimated 5.1001 s (71k iterations) | |
| Benchmarking float64/max nonnull: Analyzing | |
| float64/max nonnull time: [72.140 µs 72.153 µs 72.166 µs] | |
| thrpt: [6.7661 GiB/s 6.7673 GiB/s 6.7685 GiB/s] | |
| change: | |
| time: [+0.1906% +0.2217% +0.2472%] (p = 0.00 < 0.05) | |
| thrpt: [-0.2466% -0.2212% -0.1902%] | |
| Change within noise threshold. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 9 (9.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking float64/sum nullable | |
| Benchmarking float64/sum nullable: Warming up for 3.0000 s | |
| Benchmarking float64/sum nullable: Collecting 100 samples in estimated 5.1362 s (162k iterations) | |
| Benchmarking float64/sum nullable: Analyzing | |
| float64/sum nullable time: [31.694 µs 31.713 µs 31.733 µs] | |
| thrpt: [15.387 GiB/s 15.397 GiB/s 15.406 GiB/s] | |
| change: | |
| time: [+6.2054% +6.3500% +6.4787%] (p = 0.00 < 0.05) | |
| thrpt: [-6.0845% -5.9708% -5.8428%] | |
| Performance has regressed. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 7 (7.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking float64/min nullable | |
| Benchmarking float64/min nullable: Warming up for 3.0000 s | |
| Benchmarking float64/min nullable: Collecting 100 samples in estimated 5.6785 s (40k iterations) | |
| Benchmarking float64/min nullable: Analyzing | |
| float64/min nullable time: [140.64 µs 140.72 µs 140.80 µs] | |
| thrpt: [3.4679 GiB/s 3.4699 GiB/s 3.4719 GiB/s] | |
| change: | |
| time: [-0.3452% -0.2759% -0.2101%] (p = 0.00 < 0.05) | |
| thrpt: [+0.2105% +0.2767% +0.3464%] | |
| Change within noise threshold. | |
| Benchmarking float64/max nullable | |
| Benchmarking float64/max nullable: Warming up for 3.0000 s | |
| Benchmarking float64/max nullable: Collecting 100 samples in estimated 5.6883 s (40k iterations) | |
| Benchmarking float64/max nullable: Analyzing | |
| float64/max nullable time: [140.76 µs 140.84 µs 140.91 µs] | |
| thrpt: [3.4652 GiB/s 3.4670 GiB/s 3.4688 GiB/s] | |
| change: | |
| time: [-0.2245% -0.1522% -0.0809%] (p = 0.00 < 0.05) | |
| thrpt: [+0.0810% +0.1524% +0.2250%] | |
| Change within noise threshold. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high severe | |
| Benchmarking int8/sum nonnull | |
| Benchmarking int8/sum nonnull: Warming up for 3.0000 s | |
| Benchmarking int8/sum nonnull: Collecting 100 samples in estimated 5.0031 s (7.8M iterations) | |
| Benchmarking int8/sum nonnull: Analyzing | |
| int8/sum nonnull time: [639.93 ns 640.38 ns 641.28 ns] | |
| thrpt: [95.177 GiB/s 95.310 GiB/s 95.378 GiB/s] | |
| change: | |
| time: [-42.866% -42.816% -42.776%] (p = 0.00 < 0.05) | |
| thrpt: [+74.752% +74.874% +75.028%] | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 4 (4.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking int8/min nonnull | |
| Benchmarking int8/min nonnull: Warming up for 3.0000 s | |
| Benchmarking int8/min nonnull: Collecting 100 samples in estimated 5.0097 s (2.4M iterations) | |
| Benchmarking int8/min nonnull: Analyzing | |
| int8/min nonnull time: [2.1015 µs 2.1049 µs 2.1119 µs] | |
| thrpt: [28.900 GiB/s 28.997 GiB/s 29.043 GiB/s] | |
| change: | |
| time: [+5.7467% +6.2016% +6.6239%] (p = 0.00 < 0.05) | |
| thrpt: [-6.2124% -5.8395% -5.4344%] | |
| Performance has regressed. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 1 (1.00%) low mild | |
| 5 (5.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking int8/max nonnull | |
| Benchmarking int8/max nonnull: Warming up for 3.0000 s | |
| Benchmarking int8/max nonnull: Collecting 100 samples in estimated 5.0040 s (2.4M iterations) | |
| Benchmarking int8/max nonnull: Analyzing | |
| int8/max nonnull time: [2.0990 µs 2.0992 µs 2.0995 µs] | |
| thrpt: [29.071 GiB/s 29.075 GiB/s 29.079 GiB/s] | |
| change: | |
| time: [+0.0943% +0.1214% +0.1457%] (p = 0.00 < 0.05) | |
| thrpt: [-0.1455% -0.1212% -0.0943%] | |
| Change within noise threshold. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 3 (3.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking int8/sum nullable | |
| Benchmarking int8/sum nullable: Warming up for 3.0000 s | |
| Benchmarking int8/sum nullable: Collecting 100 samples in estimated 5.0806 s (247k iterations) | |
| Benchmarking int8/sum nullable: Analyzing | |
| int8/sum nullable time: [20.515 µs 20.517 µs 20.520 µs] | |
| thrpt: [2.9744 GiB/s 2.9748 GiB/s 2.9752 GiB/s] | |
| change: | |
| time: [-1.8494% -1.8027% -1.7683%] (p = 0.00 < 0.05) | |
| thrpt: [+1.8001% +1.8358% +1.8842%] | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 5 (5.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking int8/min nullable | |
| Benchmarking int8/min nullable: Warming up for 3.0000 s | |
| Benchmarking int8/min nullable: Collecting 100 samples in estimated 5.6032 s (35k iterations) | |
| Benchmarking int8/min nullable: Analyzing | |
| int8/min nullable time: [158.38 µs 158.42 µs 158.47 µs] | |
| thrpt: [394.40 MiB/s 394.51 MiB/s 394.62 MiB/s] | |
| change: | |
| time: [+8.2868% +8.3520% +8.4139%] (p = 0.00 < 0.05) | |
| thrpt: [-7.7609% -7.7082% -7.6526%] | |
| Performance has regressed. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 3 (3.00%) low mild | |
| 1 (1.00%) high severe | |
| Benchmarking int8/max nullable | |
| Benchmarking int8/max nullable: Warming up for 3.0000 s | |
| Benchmarking int8/max nullable: Collecting 100 samples in estimated 5.6286 s (35k iterations) | |
| Benchmarking int8/max nullable: Analyzing | |
| int8/max nullable time: [159.63 µs 159.73 µs 159.83 µs] | |
| thrpt: [391.04 MiB/s 391.28 MiB/s 391.53 MiB/s] | |
| change: | |
| time: [+8.4707% +8.5566% +8.6373%] (p = 0.00 < 0.05) | |
| thrpt: [-7.9506% -7.8822% -7.8092%] | |
| Performance has regressed. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high mild | |
| Benchmarking int16/sum nonnull | |
| Benchmarking int16/sum nonnull: Warming up for 3.0000 s | |
| Benchmarking int16/sum nonnull: Collecting 100 samples in estimated 5.0029 s (4.0M iterations) | |
| Benchmarking int16/sum nonnull: Analyzing | |
| int16/sum nonnull time: [1.2628 µs 1.2632 µs 1.2640 µs] | |
| thrpt: [96.575 GiB/s 96.633 GiB/s 96.667 GiB/s] | |
| change: | |
| time: [-0.7234% -0.2544% +0.0237%] (p = 0.25 > 0.05) | |
| thrpt: [-0.0237% +0.2550% +0.7286%] | |
| No change in performance detected. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 1 (1.00%) low mild | |
| 5 (5.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking int16/min nonnull | |
| Benchmarking int16/min nonnull: Warming up for 3.0000 s | |
| Benchmarking int16/min nonnull: Collecting 100 samples in estimated 5.0039 s (3.8M iterations) | |
| Benchmarking int16/min nonnull: Analyzing | |
| int16/min nonnull time: [1.3197 µs 1.3305 µs 1.3415 µs] | |
| thrpt: [90.997 GiB/s 91.748 GiB/s 92.501 GiB/s] | |
| change: | |
| time: [+0.3809% +1.4229% +2.4801%] (p = 0.01 < 0.05) | |
| thrpt: [-2.4201% -1.4029% -0.3795%] | |
| Change within noise threshold. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 4 (4.00%) high mild | |
| Benchmarking int16/max nonnull | |
| Benchmarking int16/max nonnull: Warming up for 3.0000 s | |
| Benchmarking int16/max nonnull: Collecting 100 samples in estimated 5.0047 s (3.9M iterations) | |
| Benchmarking int16/max nonnull: Analyzing | |
| int16/max nonnull time: [1.2635 µs 1.2637 µs 1.2639 µs] | |
| thrpt: [96.582 GiB/s 96.597 GiB/s 96.609 GiB/s] | |
| change: | |
| time: [-43.458% -43.420% -43.397%] (p = 0.00 < 0.05) | |
| thrpt: [+76.669% +76.741% +76.860%] | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 5 (5.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking int16/sum nullable | |
| Benchmarking int16/sum nullable: Warming up for 3.0000 s | |
| Benchmarking int16/sum nullable: Collecting 100 samples in estimated 5.0137 s (333k iterations) | |
| Benchmarking int16/sum nullable: Analyzing | |
| int16/sum nullable time: [15.045 µs 15.048 µs 15.052 µs] | |
| thrpt: [8.1101 GiB/s 8.1119 GiB/s 8.1136 GiB/s] | |
| change: | |
| time: [-0.2731% -0.2333% -0.1954%] (p = 0.00 < 0.05) | |
| thrpt: [+0.1958% +0.2339% +0.2738%] | |
| Change within noise threshold. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 4 (4.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking int16/min nullable | |
| Benchmarking int16/min nullable: Warming up for 3.0000 s | |
| Benchmarking int16/min nullable: Collecting 100 samples in estimated 5.0430 s (308k iterations) | |
| Benchmarking int16/min nullable: Analyzing | |
| int16/min nullable time: [16.314 µs 16.324 µs 16.340 µs] | |
| thrpt: [7.4705 GiB/s 7.4780 GiB/s 7.4824 GiB/s] | |
| change: | |
| time: [-0.1477% -0.0172% +0.2363%] (p = 0.89 > 0.05) | |
| thrpt: [-0.2358% +0.0172% +0.1480%] | |
| No change in performance detected. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 3 (3.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking int16/max nullable | |
| Benchmarking int16/max nullable: Warming up for 3.0000 s | |
| Benchmarking int16/max nullable: Collecting 100 samples in estimated 5.0381 s (308k iterations) | |
| Benchmarking int16/max nullable: Analyzing | |
| int16/max nullable time: [16.319 µs 16.351 µs 16.402 µs] | |
| thrpt: [7.4424 GiB/s 7.4655 GiB/s 7.4803 GiB/s] | |
| change: | |
| time: [-0.0750% +0.0767% +0.2808%] (p = 0.60 > 0.05) | |
| thrpt: [-0.2800% -0.0767% +0.0751%] | |
| No change in performance detected. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 8 (8.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking int32/sum nonnull | |
| Benchmarking int32/sum nonnull: Warming up for 3.0000 s | |
| Benchmarking int32/sum nonnull: Collecting 100 samples in estimated 5.0059 s (1.8M iterations) | |
| Benchmarking int32/sum nonnull: Analyzing | |
| int32/sum nonnull time: [2.7448 µs 2.7453 µs 2.7459 µs] | |
| thrpt: [88.910 GiB/s 88.929 GiB/s 88.946 GiB/s] | |
| change: | |
| time: [+8.6081% +8.8125% +8.9360%] (p = 0.00 < 0.05) | |
| thrpt: [-8.2030% -8.0988% -7.9259%] | |
| Performance has regressed. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 2 (2.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking int32/min nonnull | |
| Benchmarking int32/min nonnull: Warming up for 3.0000 s | |
| Benchmarking int32/min nonnull: Collecting 100 samples in estimated 5.0095 s (606k iterations) | |
| Benchmarking int32/min nonnull: Analyzing | |
| int32/min nonnull time: [8.2652 µs 8.2668 µs 8.2688 µs] | |
| thrpt: [29.526 GiB/s 29.533 GiB/s 29.538 GiB/s] | |
| change: | |
| time: [-1.5359% -1.4852% -1.4431%] (p = 0.00 < 0.05) | |
| thrpt: [+1.4643% +1.5076% +1.5598%] | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 1 (1.00%) low severe | |
| 1 (1.00%) low mild | |
| 5 (5.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking int32/max nonnull | |
| Benchmarking int32/max nonnull: Warming up for 3.0000 s | |
| Benchmarking int32/max nonnull: Collecting 100 samples in estimated 5.0027 s (596k iterations) | |
| Benchmarking int32/max nonnull: Analyzing | |
| int32/max nonnull time: [8.3955 µs 8.3966 µs 8.3979 µs] | |
| thrpt: [29.072 GiB/s 29.076 GiB/s 29.080 GiB/s] | |
| change: | |
| time: [+0.0341% +0.0576% +0.0807%] (p = 0.00 < 0.05) | |
| thrpt: [-0.0806% -0.0575% -0.0341%] | |
| Change within noise threshold. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 2 (2.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking int32/sum nullable | |
| Benchmarking int32/sum nullable: Warming up for 3.0000 s | |
| Benchmarking int32/sum nullable: Collecting 100 samples in estimated 5.5494 s (35k iterations) | |
| Benchmarking int32/sum nullable: Analyzing | |
| int32/sum nullable time: [156.69 µs 156.74 µs 156.79 µs] | |
| thrpt: [1.5571 GiB/s 1.5576 GiB/s 1.5581 GiB/s] | |
| change: | |
| time: [+5.0815% +5.1292% +5.1783%] (p = 0.00 < 0.05) | |
| thrpt: [-4.9234% -4.8790% -4.8357%] | |
| Performance has regressed. | |
| Benchmarking int32/min nullable | |
| Benchmarking int32/min nullable: Warming up for 3.0000 s | |
| Benchmarking int32/min nullable: Collecting 100 samples in estimated 5.1133 s (222k iterations) | |
| Benchmarking int32/min nullable: Analyzing | |
| int32/min nullable time: [23.003 µs 23.007 µs 23.011 µs] | |
| thrpt: [10.610 GiB/s 10.612 GiB/s 10.613 GiB/s] | |
| change: | |
| time: [-2.8656% -2.8112% -2.7388%] (p = 0.00 < 0.05) | |
| thrpt: [+2.8160% +2.8925% +2.9502%] | |
| Performance has improved. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 4 (4.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking int32/max nullable | |
| Benchmarking int32/max nullable: Warming up for 3.0000 s | |
| Benchmarking int32/max nullable: Collecting 100 samples in estimated 5.0785 s (222k iterations) | |
| Benchmarking int32/max nullable: Analyzing | |
| int32/max nullable time: [22.844 µs 22.847 µs 22.850 µs] | |
| thrpt: [10.684 GiB/s 10.686 GiB/s 10.687 GiB/s] | |
| change: | |
| time: [-0.0527% -0.0086% +0.0291%] (p = 0.72 > 0.05) | |
| thrpt: [-0.0290% +0.0086% +0.0528%] | |
| No change in performance detected. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 1 (1.00%) low mild | |
| 4 (4.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking int64/sum nonnull | |
| Benchmarking int64/sum nonnull: Warming up for 3.0000 s | |
| Benchmarking int64/sum nonnull: Collecting 100 samples in estimated 5.0275 s (783k iterations) | |
| Benchmarking int64/sum nonnull: Analyzing | |
| int64/sum nonnull time: [6.4937 µs 6.5058 µs 6.5190 µs] | |
| thrpt: [74.901 GiB/s 75.053 GiB/s 75.194 GiB/s] | |
| change: | |
| time: [-2.5954% -2.3781% -2.1339%] (p = 0.00 < 0.05) | |
| thrpt: [+2.1804% +2.4360% +2.6646%] | |
| Performance has improved. | |
| Found 16 outliers among 100 measurements (16.00%) | |
| 4 (4.00%) high mild | |
| 12 (12.00%) high severe | |
| Benchmarking int64/min nonnull | |
| Benchmarking int64/min nonnull: Warming up for 3.0000 s | |
| Benchmarking int64/min nonnull: Collecting 100 samples in estimated 5.0856 s (116k iterations) | |
| Benchmarking int64/min nonnull: Analyzing | |
| int64/min nonnull time: [43.800 µs 43.809 µs 43.818 µs] | |
| thrpt: [11.143 GiB/s 11.146 GiB/s 11.148 GiB/s] | |
| change: | |
| time: [+1.5404% +1.6966% +1.8108%] (p = 0.00 < 0.05) | |
| thrpt: [-1.7786% -1.6683% -1.5171%] | |
| Performance has regressed. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 3 (3.00%) low mild | |
| 2 (2.00%) high mild | |
| Benchmarking int64/max nonnull | |
| Benchmarking int64/max nonnull: Warming up for 3.0000 s | |
| Benchmarking int64/max nonnull: Collecting 100 samples in estimated 5.0851 s (116k iterations) | |
| Benchmarking int64/max nonnull: Analyzing | |
| int64/max nonnull time: [43.811 µs 43.866 µs 43.956 µs] | |
| thrpt: [11.108 GiB/s 11.131 GiB/s 11.145 GiB/s] | |
| change: | |
| time: [+2.9855% +3.1650% +3.4082%] (p = 0.00 < 0.05) | |
| thrpt: [-3.2959% -3.0679% -2.8989%] | |
| Performance has regressed. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) high severe | |
| Benchmarking int64/sum nullable | |
| Benchmarking int64/sum nullable: Warming up for 3.0000 s | |
| Benchmarking int64/sum nullable: Collecting 100 samples in estimated 5.0700 s (217k iterations) | |
| Benchmarking int64/sum nullable: Analyzing | |
| int64/sum nullable time: [23.678 µs 23.683 µs 23.688 µs] | |
| thrpt: [20.613 GiB/s 20.618 GiB/s 20.622 GiB/s] | |
| change: | |
| time: [+3.0966% +3.2266% +3.3155%] (p = 0.00 < 0.05) | |
| thrpt: [-3.2091% -3.1257% -3.0036%] | |
| Performance has regressed. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 2 (2.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking int64/min nullable | |
| Benchmarking int64/min nullable: Warming up for 3.0000 s | |
| Benchmarking int64/min nullable: Collecting 100 samples in estimated 5.0696 s (182k iterations) | |
| Benchmarking int64/min nullable: Analyzing | |
| int64/min nullable time: [27.867 µs 27.872 µs 27.879 µs] | |
| thrpt: [17.514 GiB/s 17.518 GiB/s 17.522 GiB/s] | |
| change: | |
| time: [+2.2812% +2.3877% +2.4590%] (p = 0.00 < 0.05) | |
| thrpt: [-2.3999% -2.3321% -2.2303%] | |
| Performance has regressed. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 4 (4.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking int64/max nullable | |
| Benchmarking int64/max nullable: Warming up for 3.0000 s | |
| Benchmarking int64/max nullable: Collecting 100 samples in estimated 5.0461 s (177k iterations) | |
| Benchmarking int64/max nullable: Analyzing | |
| int64/max nullable time: [28.054 µs 28.061 µs 28.069 µs] | |
| thrpt: [17.396 GiB/s 17.401 GiB/s 17.405 GiB/s] | |
| change: | |
| time: [+2.9138% +3.1460% +3.3869%] (p = 0.00 < 0.05) | |
| thrpt: [-3.2760% -3.0500% -2.8313%] | |
| Performance has regressed. | |
| Found 19 outliers among 100 measurements (19.00%) | |
| 1 (1.00%) high mild | |
| 18 (18.00%) high severe | |
| Benchmarking string/min nonnull | |
| Benchmarking string/min nonnull: Warming up for 3.0000 s | |
| Benchmarking string/min nonnull: Collecting 100 samples in estimated 5.0771 s (50k iterations) | |
| Benchmarking string/min nonnull: Analyzing | |
| string/min nonnull time: [100.53 µs 100.68 µs 100.98 µs] | |
| thrpt: [649.02 Melem/s 650.96 Melem/s 651.89 Melem/s] | |
| change: | |
| time: [-57.108% -57.064% -56.999%] (p = 0.00 < 0.05) | |
| thrpt: [+132.55% +132.90% +133.14%] | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 1 (1.00%) low mild | |
| 5 (5.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking string/max nonnull | |
| Benchmarking string/max nonnull: Warming up for 3.0000 s | |
| Benchmarking string/max nonnull: Collecting 100 samples in estimated 5.4680 s (50k iterations) | |
| Benchmarking string/max nonnull: Analyzing | |
| string/max nonnull time: [107.58 µs 107.77 µs 108.20 µs] | |
| thrpt: [605.67 Melem/s 608.09 Melem/s 609.19 Melem/s] | |
| change: | |
| time: [-51.668% -51.558% -51.360%] (p = 0.00 < 0.05) | |
| thrpt: [+105.59% +106.43% +106.90%] | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 1 (1.00%) low mild | |
| 6 (6.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking string/min nullable | |
| Benchmarking string/min nullable: Warming up for 3.0000 s | |
| Benchmarking string/min nullable: Collecting 100 samples in estimated 5.3029 s (56k iterations) | |
| Benchmarking string/min nullable: Analyzing | |
| string/min nullable time: [95.342 µs 95.645 µs 96.285 µs] | |
| thrpt: [680.65 Melem/s 685.20 Melem/s 687.38 Melem/s] | |
| change: | |
| time: [-68.482% -68.440% -68.367%] (p = 0.00 < 0.05) | |
| thrpt: [+216.12% +216.85% +217.28%] | |
| Performance has improved. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 4 (4.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking string/max nullable | |
| Benchmarking string/max nullable: Warming up for 3.0000 s | |
| Benchmarking string/max nullable: Collecting 100 samples in estimated 5.3016 s (56k iterations) | |
| Benchmarking string/max nullable: Analyzing | |
| string/max nullable time: [95.283 µs 95.389 µs 95.623 µs] | |
| thrpt: [685.36 Melem/s 687.04 Melem/s 687.80 Melem/s] | |
| change: | |
| time: [-67.953% -67.867% -67.634%] (p = 0.00 < 0.05) | |
| thrpt: [+208.97% +211.20% +212.04%] | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 6 (6.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking string view/min nonnull | |
| Benchmarking string view/min nonnull: Warming up for 3.0000 s | |
| Benchmarking string view/min nonnull: Collecting 100 samples in estimated 5.4352 s (50k iterations) | |
| Benchmarking string view/min nonnull: Analyzing | |
| string view/min nonnull time: [107.59 µs 107.61 µs 107.62 µs] | |
| thrpt: [608.94 Melem/s 609.04 Melem/s 609.14 Melem/s] | |
| change: | |
| time: [-33.498% -33.203% -33.025%] (p = 0.00 < 0.05) | |
| thrpt: [+49.309% +49.707% +50.372%] | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 4 (4.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking string view/max nonnull | |
| Benchmarking string view/max nonnull: Warming up for 3.0000 s | |
| Benchmarking string view/max nonnull: Collecting 100 samples in estimated 5.4136 s (50k iterations) | |
| Benchmarking string view/max nonnull: Analyzing | |
| string view/max nonnull time: [107.19 µs 107.21 µs 107.24 µs] | |
| thrpt: [611.12 Melem/s 611.27 Melem/s 611.41 Melem/s] | |
| change: | |
| time: [-33.387% -33.346% -33.316%] (p = 0.00 < 0.05) | |
| thrpt: [+49.962% +50.029% +50.121%] | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 7 (7.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking string view/min nullable | |
| Benchmarking string view/min nullable: Warming up for 3.0000 s | |
| Benchmarking string view/min nullable: Collecting 100 samples in estimated 5.2936 s (61k iterations) | |
| Benchmarking string view/min nullable: Analyzing | |
| string view/min nullable | |
| time: [96.698 µs 99.011 µs 101.56 µs] | |
| thrpt: [645.29 Melem/s 661.91 Melem/s 677.74 Melem/s] | |
| change: | |
| time: [-40.007% -39.058% -37.878%] (p = 0.00 < 0.05) | |
| thrpt: [+60.975% +64.090% +66.685%] | |
| Performance has improved. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 8 (8.00%) high severe | |
| Benchmarking string view/max nullable | |
| Benchmarking string view/max nullable: Warming up for 3.0000 s | |
| Benchmarking string view/max nullable: Collecting 100 samples in estimated 5.2614 s (61k iterations) | |
| Benchmarking string view/max nullable: Analyzing | |
| string view/max nullable | |
| time: [86.769 µs 86.787 µs 86.807 µs] | |
| thrpt: [754.96 Melem/s 755.13 Melem/s 755.29 Melem/s] | |
| change: | |
| time: [-44.539% -44.458% -44.405%] (p = 0.00 < 0.05) | |
| thrpt: [+79.874% +80.045% +80.305%] | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 1 (1.00%) low mild | |
| 7 (7.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking bool/min nonnull mixed | |
| Benchmarking bool/min nonnull mixed: Warming up for 3.0000 s | |
| Benchmarking bool/min nonnull mixed: Collecting 100 samples in estimated 5.0000 s (1.0B iterations) | |
| Benchmarking bool/min nonnull mixed: Analyzing | |
| bool/min nonnull mixed time: [4.8877 ns 4.8913 ns 4.8991 ns] | |
| thrpt: [ 13377 Gelem/s 13398 Gelem/s 13408 Gelem/s] | |
| change: | |
| time: [-0.0572% +0.1138% +0.4003%] (p = 0.58 > 0.05) | |
| thrpt: [-0.3987% -0.1136% +0.0572%] | |
| No change in performance detected. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 2 (2.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking bool/max nonnull mixed | |
| Benchmarking bool/max nonnull mixed: Warming up for 3.0000 s | |
| Benchmarking bool/max nonnull mixed: Collecting 100 samples in estimated 5.0000 s (969M iterations) | |
| Benchmarking bool/max nonnull mixed: Analyzing | |
| bool/max nonnull mixed time: [5.1590 ns 5.1598 ns 5.1607 ns] | |
| thrpt: [ 12699 Gelem/s 12701 Gelem/s 12703 Gelem/s] | |
| change: | |
| time: [-0.0578% -0.0090% +0.0276%] (p = 0.72 > 0.05) | |
| thrpt: [-0.0276% +0.0090% +0.0578%] | |
| No change in performance detected. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 3 (3.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking bool/or nonnull mixed | |
| Benchmarking bool/or nonnull mixed: Warming up for 3.0000 s | |
| Benchmarking bool/or nonnull mixed: Collecting 100 samples in estimated 5.0000 s (969M iterations) | |
| Benchmarking bool/or nonnull mixed: Analyzing | |
| bool/or nonnull mixed time: [5.1590 ns 5.1597 ns 5.1604 ns] | |
| thrpt: [ 12700 Gelem/s 12702 Gelem/s 12703 Gelem/s] | |
| change: | |
| time: [-6.5665% -5.8276% -5.2357%] (p = 0.00 < 0.05) | |
| thrpt: [+5.5250% +6.1882% +7.0280%] | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 1 (1.00%) low mild | |
| 4 (4.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking bool/and nonnull mixed | |
| Benchmarking bool/and nonnull mixed: Warming up for 3.0000 s | |
| Benchmarking bool/and nonnull mixed: Collecting 100 samples in estimated 5.0000 s (1.0B iterations) | |
| Benchmarking bool/and nonnull mixed: Analyzing | |
| bool/and nonnull mixed time: [4.8876 ns 4.8975 ns 4.9193 ns] | |
| thrpt: [ 13322 Gelem/s 13382 Gelem/s 13409 Gelem/s] | |
| change: | |
| time: [-5.3507% -5.1933% -4.9373%] (p = 0.00 < 0.05) | |
| thrpt: [+5.1938% +5.4778% +5.6532%] | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 4 (4.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking bool/min nonnull false | |
| Benchmarking bool/min nonnull false: Warming up for 3.0000 s | |
| Benchmarking bool/min nonnull false: Collecting 100 samples in estimated 5.0000 s (1.0B iterations) | |
| Benchmarking bool/min nonnull false: Analyzing | |
| bool/min nonnull false time: [4.8877 ns 4.8969 ns 4.9171 ns] | |
| thrpt: [ 13328 Gelem/s 13383 Gelem/s 13408 Gelem/s] | |
| change: | |
| time: [-0.0777% +0.1099% +0.4213%] (p = 0.60 > 0.05) | |
| thrpt: [-0.4195% -0.1098% +0.0778%] | |
| No change in performance detected. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 2 (2.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking bool/max nonnull false | |
| Benchmarking bool/max nonnull false: Warming up for 3.0000 s | |
| Benchmarking bool/max nonnull false: Collecting 100 samples in estimated 5.0007 s (17M iterations) | |
| Benchmarking bool/max nonnull false: Analyzing | |
| bool/max nonnull false time: [290.68 ns 291.13 ns 291.65 ns] | |
| thrpt: [224.71 Gelem/s 225.11 Gelem/s 225.46 Gelem/s] | |
| change: | |
| time: [-1.5394% -0.7754% -0.1330%] (p = 0.03 < 0.05) | |
| thrpt: [+0.1332% +0.7814% +1.5634%] | |
| Change within noise threshold. | |
| Found 18 outliers among 100 measurements (18.00%) | |
| 2 (2.00%) high mild | |
| 16 (16.00%) high severe | |
| Benchmarking bool/or nonnull false | |
| Benchmarking bool/or nonnull false: Warming up for 3.0000 s | |
| Benchmarking bool/or nonnull false: Collecting 100 samples in estimated 5.0021 s (8.9M iterations) | |
| Benchmarking bool/or nonnull false: Analyzing | |
| bool/or nonnull false time: [563.96 ns 564.02 ns 564.10 ns] | |
| thrpt: [116.18 Gelem/s 116.19 Gelem/s 116.21 Gelem/s] | |
| change: | |
| time: [+92.795% +93.583% +94.070%] (p = 0.00 < 0.05) | |
| thrpt: [-48.472% -48.343% -48.131%] | |
| Performance has regressed. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 2 (2.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking bool/and nonnull false | |
| Benchmarking bool/and nonnull false: Warming up for 3.0000 s | |
| Benchmarking bool/and nonnull false: Collecting 100 samples in estimated 5.0000 s (1.0B iterations) | |
| Benchmarking bool/and nonnull false: Analyzing | |
| bool/and nonnull false time: [4.8883 ns 4.9167 ns 4.9624 ns] | |
| thrpt: [ 13207 Gelem/s 13329 Gelem/s 13407 Gelem/s] | |
| change: | |
| time: [-5.3170% -4.9732% -4.4218%] (p = 0.00 < 0.05) | |
| thrpt: [+4.6264% +5.2335% +5.6156%] | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 2 (2.00%) high mild | |
| 8 (8.00%) high severe | |
| Benchmarking bool/min nonnull true | |
| Benchmarking bool/min nonnull true: Warming up for 3.0000 s | |
| Benchmarking bool/min nonnull true: Collecting 100 samples in estimated 5.0003 s (17M iterations) | |
| Benchmarking bool/min nonnull true: Analyzing | |
| bool/min nonnull true time: [293.26 ns 294.54 ns 296.46 ns] | |
| thrpt: [221.06 Gelem/s 222.50 Gelem/s 223.48 Gelem/s] | |
| change: | |
| time: [+0.1176% +0.8628% +1.8338%] (p = 0.04 < 0.05) | |
| thrpt: [-1.8008% -0.8554% -0.1175%] | |
| Change within noise threshold. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 1 (1.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking bool/max nonnull true | |
| Benchmarking bool/max nonnull true: Warming up for 3.0000 s | |
| Benchmarking bool/max nonnull true: Collecting 100 samples in estimated 5.0000 s (966M iterations) | |
| Benchmarking bool/max nonnull true: Analyzing | |
| bool/max nonnull true time: [5.1587 ns 5.1594 ns 5.1603 ns] | |
| thrpt: [ 12700 Gelem/s 12702 Gelem/s 12704 Gelem/s] | |
| change: | |
| time: [-0.5304% -0.2453% -0.0791%] (p = 0.03 < 0.05) | |
| thrpt: [+0.0792% +0.2459% +0.5333%] | |
| Change within noise threshold. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 5 (5.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking bool/or nonnull true | |
| Benchmarking bool/or nonnull true: Warming up for 3.0000 s | |
| Benchmarking bool/or nonnull true: Collecting 100 samples in estimated 5.0000 s (969M iterations) | |
| Benchmarking bool/or nonnull true: Analyzing | |
| bool/or nonnull true time: [5.1588 ns 5.1594 ns 5.1601 ns] | |
| thrpt: [ 12700 Gelem/s 12702 Gelem/s 12704 Gelem/s] | |
| change: | |
| time: [-5.0521% -5.0242% -4.9893%] (p = 0.00 < 0.05) | |
| thrpt: [+5.2514% +5.2900% +5.3209%] | |
| Performance has improved. | |
| Found 13 outliers among 100 measurements (13.00%) | |
| 3 (3.00%) high mild | |
| 10 (10.00%) high severe | |
| Benchmarking bool/and nonnull true | |
| Benchmarking bool/and nonnull true: Warming up for 3.0000 s | |
| Benchmarking bool/and nonnull true: Collecting 100 samples in estimated 5.0009 s (8.9M iterations) | |
| Benchmarking bool/and nonnull true: Analyzing | |
| bool/and nonnull true time: [564.83 ns 565.66 ns 567.43 ns] | |
| thrpt: [115.50 Gelem/s 115.86 Gelem/s 116.03 Gelem/s] | |
| change: | |
| time: [+92.392% +92.764% +93.163%] (p = 0.00 < 0.05) | |
| thrpt: [-48.230% -48.123% -48.023%] | |
| Performance has regressed. | |
| Found 13 outliers among 100 measurements (13.00%) | |
| 1 (1.00%) low severe | |
| 3 (3.00%) high mild | |
| 9 (9.00%) high severe | |
| Benchmarking bool/min nullable mixed | |
| Benchmarking bool/min nullable mixed: Warming up for 3.0000 s | |
| Benchmarking bool/min nullable mixed: Collecting 100 samples in estimated 5.0000 s (635M iterations) | |
| Benchmarking bool/min nullable mixed: Analyzing | |
| bool/min nullable mixed time: [7.8739 ns 7.8750 ns 7.8764 ns] | |
| thrpt: [8320.6 Gelem/s 8322.0 Gelem/s 8323.2 Gelem/s] | |
| change: | |
| time: [-0.3590% -0.3133% -0.2694%] (p = 0.00 < 0.05) | |
| thrpt: [+0.2702% +0.3142% +0.3603%] | |
| Change within noise threshold. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 2 (2.00%) high mild | |
| 9 (9.00%) high severe | |
| Benchmarking bool/max nullable mixed | |
| Benchmarking bool/max nullable mixed: Warming up for 3.0000 s | |
| Benchmarking bool/max nullable mixed: Collecting 100 samples in estimated 5.0000 s (614M iterations) | |
| Benchmarking bool/max nullable mixed: Analyzing | |
| bool/max nullable mixed time: [8.1457 ns 8.1466 ns 8.1476 ns] | |
| thrpt: [8043.6 Gelem/s 8044.6 Gelem/s 8045.4 Gelem/s] | |
| change: | |
| time: [-11.787% -11.772% -11.757%] (p = 0.00 < 0.05) | |
| thrpt: [+13.323% +13.342% +13.362%] | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 5 (5.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking bool/or nullable mixed | |
| Benchmarking bool/or nullable mixed: Warming up for 3.0000 s | |
| Benchmarking bool/or nullable mixed: Collecting 100 samples in estimated 5.0000 s (594M iterations) | |
| Benchmarking bool/or nullable mixed: Analyzing | |
| bool/or nullable mixed time: [8.4176 ns 8.4188 ns 8.4202 ns] | |
| thrpt: [7783.2 Gelem/s 7784.5 Gelem/s 7785.6 Gelem/s] | |
| change: | |
| time: [-11.453% -11.434% -11.416%] (p = 0.00 < 0.05) | |
| thrpt: [+12.887% +12.910% +12.934%] | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 5 (5.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking bool/and nullable mixed | |
| Benchmarking bool/and nullable mixed: Warming up for 3.0000 s | |
| Benchmarking bool/and nullable mixed: Collecting 100 samples in estimated 5.0000 s (656M iterations) | |
| Benchmarking bool/and nullable mixed: Analyzing | |
| bool/and nullable mixed time: [7.6027 ns 7.6126 ns 7.6334 ns] | |
| thrpt: [8585.4 Gelem/s 8608.9 Gelem/s 8620.0 Gelem/s] | |
| change: | |
| time: [-6.7016% -6.6170% -6.4674%] (p = 0.00 < 0.05) | |
| thrpt: [+6.9146% +7.0859% +7.1830%] | |
| Performance has improved. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 5 (5.00%) high mild | |
| 7 (7.00%) high severe | |
| Benchmarking bool/min nullable false | |
| Benchmarking bool/min nullable false: Warming up for 3.0000 s | |
| Benchmarking bool/min nullable false: Collecting 100 samples in estimated 5.0000 s (634M iterations) | |
| Benchmarking bool/min nullable false: Analyzing | |
| bool/min nullable false time: [7.8742 ns 7.8752 ns 7.8763 ns] | |
| thrpt: [8320.7 Gelem/s 8321.8 Gelem/s 8322.8 Gelem/s] | |
| change: | |
| time: [-0.0553% -0.0092% +0.0235%] (p = 0.72 > 0.05) | |
| thrpt: [-0.0235% +0.0092% +0.0553%] | |
| No change in performance detected. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 6 (6.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking bool/max nullable false | |
| Benchmarking bool/max nullable false: Warming up for 3.0000 s | |
| Benchmarking bool/max nullable false: Collecting 100 samples in estimated 5.0020 s (5.0M iterations) | |
| Benchmarking bool/max nullable false: Analyzing | |
| bool/max nullable false time: [991.20 ns 991.32 ns 991.46 ns] | |
| thrpt: [66.100 Gelem/s 66.110 Gelem/s 66.118 Gelem/s] | |
| change: | |
| time: [+16.764% +16.794% +16.822%] (p = 0.00 < 0.05) | |
| thrpt: [-14.400% -14.379% -14.357%] | |
| Performance has regressed. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 2 (2.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking bool/or nullable false | |
| Benchmarking bool/or nullable false: Warming up for 3.0000 s | |
| Benchmarking bool/or nullable false: Collecting 100 samples in estimated 5.0017 s (5.0M iterations) | |
| Benchmarking bool/or nullable false: Analyzing | |
| bool/or nullable false time: [991.09 ns 991.22 ns 991.38 ns] | |
| thrpt: [66.106 Gelem/s 66.116 Gelem/s 66.125 Gelem/s] | |
| change: | |
| time: [+16.750% +16.779% +16.815%] (p = 0.00 < 0.05) | |
| thrpt: [-14.395% -14.368% -14.347%] | |
| Performance has regressed. | |
| Found 13 outliers among 100 measurements (13.00%) | |
| 4 (4.00%) high mild | |
| 9 (9.00%) high severe | |
| Benchmarking bool/and nullable false | |
| Benchmarking bool/and nullable false: Warming up for 3.0000 s | |
| Benchmarking bool/and nullable false: Collecting 100 samples in estimated 5.0000 s (652M iterations) | |
| Benchmarking bool/and nullable false: Analyzing | |
| bool/and nullable false time: [7.6035 ns 7.6127 ns 7.6325 ns] | |
| thrpt: [8586.4 Gelem/s 8608.8 Gelem/s 8619.1 Gelem/s] | |
| change: | |
| time: [-6.6696% -6.4600% -6.1885%] (p = 0.00 < 0.05) | |
| thrpt: [+6.5968% +6.9061% +7.1463%] | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 1 (1.00%) low mild | |
| 1 (1.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking bool/min nullable true | |
| Benchmarking bool/min nullable true: Warming up for 3.0000 s | |
| Benchmarking bool/min nullable true: Collecting 100 samples in estimated 5.0002 s (8.8M iterations) | |
| Benchmarking bool/min nullable true: Analyzing | |
| bool/min nullable true time: [569.31 ns 569.39 ns 569.47 ns] | |
| thrpt: [115.08 Gelem/s 115.10 Gelem/s 115.11 Gelem/s] | |
| change: | |
| time: [-0.0956% -0.0740% -0.0519%] (p = 0.00 < 0.05) | |
| thrpt: [+0.0519% +0.0740% +0.0957%] | |
| Change within noise threshold. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 4 (4.00%) high mild | |
| 7 (7.00%) high severe | |
| Benchmarking bool/max nullable true | |
| Benchmarking bool/max nullable true: Warming up for 3.0000 s | |
| Benchmarking bool/max nullable true: Collecting 100 samples in estimated 5.0000 s (614M iterations) | |
| Benchmarking bool/max nullable true: Analyzing | |
| bool/max nullable true time: [8.1458 ns 8.1468 ns 8.1480 ns] | |
| thrpt: [8043.2 Gelem/s 8044.4 Gelem/s 8045.4 Gelem/s] | |
| change: | |
| time: [-11.783% -11.758% -11.733%] (p = 0.00 < 0.05) | |
| thrpt: [+13.293% +13.325% +13.356%] | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 5 (5.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking bool/or nullable true | |
| Benchmarking bool/or nullable true: Warming up for 3.0000 s | |
| Benchmarking bool/or nullable true: Collecting 100 samples in estimated 5.0000 s (594M iterations) | |
| Benchmarking bool/or nullable true: Analyzing | |
| bool/or nullable true time: [8.4172 ns 8.4183 ns 8.4194 ns] | |
| thrpt: [7783.9 Gelem/s 7785.0 Gelem/s 7785.9 Gelem/s] | |
| change: | |
| time: [-11.437% -11.420% -11.401%] (p = 0.00 < 0.05) | |
| thrpt: [+12.868% +12.892% +12.914%] | |
| Performance has improved. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 3 (3.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking bool/and nullable true | |
| Benchmarking bool/and nullable true: Warming up for 3.0000 s | |
| Benchmarking bool/and nullable true: Collecting 100 samples in estimated 5.0028 s (8.8M iterations) | |
| Benchmarking bool/and nullable true: Analyzing | |
| bool/and nullable true time: [566.13 ns 566.40 ns 566.94 ns] | |
| thrpt: [115.60 Gelem/s 115.71 Gelem/s 115.76 Gelem/s] | |
| change: | |
| time: [-0.7031% -0.4633% +0.0084%] (p = 0.01 < 0.05) | |
| thrpt: [-0.0084% +0.4655% +0.7081%] | |
| Change within noise threshold. | |
| Found 15 outliers among 100 measurements (15.00%) | |
| 1 (1.00%) low mild | |
| 6 (6.00%) high mild | |
| 8 (8.00%) high severe | |
| Running benches/arithmetic_kernels.rs (target/x86_64-unknown-linux-gnu/release/deps/arithmetic_kernels-ebb4be9653c4e633) | |
| Benchmarking add(0) | |
| Benchmarking add(0): Warming up for 3.0000 s | |
| Benchmarking add(0): Collecting 100 samples in estimated 5.0091 s (591k iterations) | |
| Benchmarking add(0): Analyzing | |
| add(0) time: [8.4758 µs 8.4777 µs 8.4797 µs] | |
| change: [-1.0578% -0.5133% +0.0440%] (p = 0.07 > 0.05) | |
| No change in performance detected. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 5 (5.00%) high mild | |
| Benchmarking add_checked(0) | |
| Benchmarking add_checked(0): Warming up for 3.0000 s | |
| Benchmarking add_checked(0): Collecting 100 samples in estimated 5.0259 s (591k iterations) | |
| Benchmarking add_checked(0): Analyzing | |
| add_checked(0) time: [8.5026 µs 8.5045 µs 8.5066 µs] | |
| change: [-1.7243% -1.2176% -0.6805%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 3 (3.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking add_scalar(0) | |
| Benchmarking add_scalar(0): Warming up for 3.0000 s | |
| Benchmarking add_scalar(0): Collecting 100 samples in estimated 5.0122 s (914k iterations) | |
| Benchmarking add_scalar(0): Analyzing | |
| add_scalar(0) time: [5.4734 µs 5.4851 µs 5.5073 µs] | |
| change: [-0.3761% -0.2166% -0.0518%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 6 (6.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking subtract(0) | |
| Benchmarking subtract(0): Warming up for 3.0000 s | |
| Benchmarking subtract(0): Collecting 100 samples in estimated 5.0077 s (591k iterations) | |
| Benchmarking subtract(0): Analyzing | |
| subtract(0) time: [8.4012 µs 8.4035 µs 8.4061 µs] | |
| change: [-2.9871% -2.8683% -2.7861%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 10 (10.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking subtract_checked(0) | |
| Benchmarking subtract_checked(0): Warming up for 3.0000 s | |
| Benchmarking subtract_checked(0): Collecting 100 samples in estimated 5.0156 s (626k iterations) | |
| Benchmarking subtract_checked(0): Analyzing | |
| subtract_checked(0) time: [8.0079 µs 8.0098 µs 8.0120 µs] | |
| change: [-7.8487% -7.7235% -7.6403%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 5 (5.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking subtract_scalar(0) | |
| Benchmarking subtract_scalar(0): Warming up for 3.0000 s | |
| Benchmarking subtract_scalar(0): Collecting 100 samples in estimated 5.0144 s (944k iterations) | |
| Benchmarking subtract_scalar(0): Analyzing | |
| subtract_scalar(0) time: [5.3063 µs 5.3079 µs 5.3098 µs] | |
| change: [-14.168% -14.085% -14.025%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 4 (4.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking multiply(0) | |
| Benchmarking multiply(0): Warming up for 3.0000 s | |
| Benchmarking multiply(0): Collecting 100 samples in estimated 5.0351 s (631k iterations) | |
| Benchmarking multiply(0): Analyzing | |
| multiply(0) time: [7.9868 µs 7.9886 µs 7.9907 µs] | |
| change: [-7.5862% -7.4804% -7.4106%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 9 (9.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking multiply_checked(0) | |
| Benchmarking multiply_checked(0): Warming up for 3.0000 s | |
| Benchmarking multiply_checked(0): Collecting 100 samples in estimated 5.0025 s (626k iterations) | |
| Benchmarking multiply_checked(0): Analyzing | |
| multiply_checked(0) time: [7.9967 µs 8.0004 µs 8.0040 µs] | |
| change: [+1.6004% +1.7502% +1.8679%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 7 (7.00%) high mild | |
| Benchmarking multiply_scalar(0) | |
| Benchmarking multiply_scalar(0): Warming up for 3.0000 s | |
| Benchmarking multiply_scalar(0): Collecting 100 samples in estimated 5.0265 s (944k iterations) | |
| Benchmarking multiply_scalar(0): Analyzing | |
| multiply_scalar(0) time: [5.4185 µs 5.5303 µs 5.6419 µs] | |
| change: [-13.718% -12.986% -12.173%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 13 outliers among 100 measurements (13.00%) | |
| 1 (1.00%) high mild | |
| 12 (12.00%) high severe | |
| Benchmarking divide(0) | |
| Benchmarking divide(0): Warming up for 3.0000 s | |
| Benchmarking divide(0): Collecting 100 samples in estimated 5.0289 s (318k iterations) | |
| Benchmarking divide(0): Analyzing | |
| divide(0) time: [15.789 µs 15.793 µs 15.797 µs] | |
| change: [+1.4633% +1.5810% +1.6927%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 3 (3.00%) low mild | |
| 3 (3.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking divide_scalar(0) | |
| Benchmarking divide_scalar(0): Warming up for 3.0000 s | |
| Benchmarking divide_scalar(0): Collecting 100 samples in estimated 5.0529 s (318k iterations) | |
| Benchmarking divide_scalar(0): Analyzing | |
| divide_scalar(0) time: [15.861 µs 15.863 µs 15.866 µs] | |
| change: [+0.6262% +0.7370% +0.8372%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 13 outliers among 100 measurements (13.00%) | |
| 1 (1.00%) low severe | |
| 2 (2.00%) low mild | |
| 7 (7.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking modulo(0) | |
| Benchmarking modulo(0): Warming up for 3.0000 s | |
| Benchmarking modulo(0): Collecting 100 samples in estimated 5.4172 s (25k iterations) | |
| Benchmarking modulo(0): Analyzing | |
| modulo(0) time: [214.48 µs 214.50 µs 214.53 µs] | |
| change: [-0.2172% -0.1837% -0.1469%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 5 (5.00%) high mild | |
| 7 (7.00%) high severe | |
| Benchmarking modulo_scalar(0) | |
| Benchmarking modulo_scalar(0): Warming up for 3.0000 s | |
| Benchmarking modulo_scalar(0): Collecting 100 samples in estimated 5.8016 s (15k iterations) | |
| Benchmarking modulo_scalar(0): Analyzing | |
| modulo_scalar(0) time: [383.05 µs 383.12 µs 383.20 µs] | |
| change: [-7.3203% -7.2980% -7.2744%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 2 (2.00%) low mild | |
| 4 (4.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking add(0.1) | |
| Benchmarking add(0.1): Warming up for 3.0000 s | |
| Benchmarking add(0.1): Collecting 100 samples in estimated 5.0134 s (556k iterations) | |
| Benchmarking add(0.1): Analyzing | |
| add(0.1) time: [9.0214 µs 9.0233 µs 9.0255 µs] | |
| change: [-8.7114% -8.6196% -8.5554%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 1 (1.00%) low mild | |
| 6 (6.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking add_checked(0.1) | |
| Benchmarking add_checked(0.1): Warming up for 3.0000 s | |
| Benchmarking add_checked(0.1): Collecting 100 samples in estimated 5.0223 s (556k iterations) | |
| Benchmarking add_checked(0.1): Analyzing | |
| add_checked(0.1) time: [9.0408 µs 9.0429 µs 9.0452 µs] | |
| change: [-8.4838% -8.4068% -8.3483%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 3 (3.00%) low mild | |
| 3 (3.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking add_scalar(0.1) | |
| Benchmarking add_scalar(0.1): Warming up for 3.0000 s | |
| Benchmarking add_scalar(0.1): Collecting 100 samples in estimated 5.0141 s (909k iterations) | |
| Benchmarking add_scalar(0.1): Analyzing | |
| add_scalar(0.1) time: [5.5174 µs 5.5191 µs 5.5211 µs] | |
| change: [+3.4367% +3.6265% +3.7449%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 1 (1.00%) low mild | |
| 6 (6.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking subtract(0.1) | |
| Benchmarking subtract(0.1): Warming up for 3.0000 s | |
| Benchmarking subtract(0.1): Collecting 100 samples in estimated 5.0039 s (556k iterations) | |
| Benchmarking subtract(0.1): Analyzing | |
| subtract(0.1) time: [9.0020 µs 9.0054 µs 9.0087 µs] | |
| change: [-11.239% -11.132% -11.059%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 9 (9.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking subtract_checked(0.1) | |
| Benchmarking subtract_checked(0.1): Warming up for 3.0000 s | |
| Benchmarking subtract_checked(0.1): Collecting 100 samples in estimated 5.0057 s (556k iterations) | |
| Benchmarking subtract_checked(0.1): Analyzing | |
| subtract_checked(0.1) time: [9.0053 µs 9.0077 µs 9.0102 µs] | |
| change: [-11.084% -10.965% -10.891%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 4 (4.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking subtract_scalar(0.1) | |
| Benchmarking subtract_scalar(0.1): Warming up for 3.0000 s | |
| Benchmarking subtract_scalar(0.1): Collecting 100 samples in estimated 5.0114 s (929k iterations) | |
| Benchmarking subtract_scalar(0.1): Analyzing | |
| subtract_scalar(0.1) time: [5.3951 µs 5.4016 µs 5.4147 µs] | |
| change: [-1.0136% -0.8082% -0.5641%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 3 (3.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking multiply(0.1) | |
| Benchmarking multiply(0.1): Warming up for 3.0000 s | |
| Benchmarking multiply(0.1): Collecting 100 samples in estimated 5.0108 s (556k iterations) | |
| Benchmarking multiply(0.1): Analyzing | |
| multiply(0.1) time: [9.0138 µs 9.0164 µs 9.0191 µs] | |
| change: [-10.995% -10.868% -10.724%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 1 (1.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking multiply_checked(0.1) | |
| Benchmarking multiply_checked(0.1): Warming up for 3.0000 s | |
| Benchmarking multiply_checked(0.1): Collecting 100 samples in estimated 5.0110 s (556k iterations) | |
| Benchmarking multiply_checked(0.1): Analyzing | |
| multiply_checked(0.1) time: [9.0185 µs 9.0206 µs 9.0227 µs] | |
| change: [-10.912% -10.829% -10.770%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 2 (2.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking multiply_scalar(0.1) | |
| Benchmarking multiply_scalar(0.1): Warming up for 3.0000 s | |
| Benchmarking multiply_scalar(0.1): Collecting 100 samples in estimated 5.0046 s (924k iterations) | |
| Benchmarking multiply_scalar(0.1): Analyzing | |
| multiply_scalar(0.1) time: [5.3956 µs 5.3974 µs 5.3996 µs] | |
| change: [-1.2414% -1.0865% -0.9709%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 2 (2.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking divide(0.1) | |
| Benchmarking divide(0.1): Warming up for 3.0000 s | |
| Benchmarking divide(0.1): Collecting 100 samples in estimated 5.0239 s (308k iterations) | |
| Benchmarking divide(0.1): Analyzing | |
| divide(0.1) time: [16.327 µs 16.332 µs 16.339 µs] | |
| change: [-0.5039% -0.4238% -0.3535%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 4 (4.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking divide_scalar(0.1) | |
| Benchmarking divide_scalar(0.1): Warming up for 3.0000 s | |
| Benchmarking divide_scalar(0.1): Collecting 100 samples in estimated 5.0649 s (318k iterations) | |
| Benchmarking divide_scalar(0.1): Analyzing | |
| divide_scalar(0.1) time: [15.913 µs 15.916 µs 15.919 µs] | |
| change: [+1.0067% +1.2674% +1.5423%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 1 (1.00%) low mild | |
| 7 (7.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking modulo(0.1) | |
| Benchmarking modulo(0.1): Warming up for 3.0000 s | |
| Benchmarking modulo(0.1): Collecting 100 samples in estimated 5.2798 s (20k iterations) | |
| Benchmarking modulo(0.1): Analyzing | |
| modulo(0.1) time: [261.30 µs 261.33 µs 261.37 µs] | |
| change: [-1.2055% -1.1399% -1.0745%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 2 (2.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking modulo_scalar(0.1) | |
| Benchmarking modulo_scalar(0.1): Warming up for 3.0000 s | |
| Benchmarking modulo_scalar(0.1): Collecting 100 samples in estimated 5.5056 s (15k iterations) | |
| Benchmarking modulo_scalar(0.1): Analyzing | |
| modulo_scalar(0.1) time: [374.89 µs 374.94 µs 375.00 µs] | |
| change: [-3.9797% -3.8229% -3.6978%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 7 (7.00%) low severe | |
| 1 (1.00%) low mild | |
| 2 (2.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking add(0.5) | |
| Benchmarking add(0.5): Warming up for 3.0000 s | |
| Benchmarking add(0.5): Collecting 100 samples in estimated 5.0156 s (556k iterations) | |
| Benchmarking add(0.5): Analyzing | |
| add(0.5) time: [8.9880 µs 9.0301 µs 9.0856 µs] | |
| change: [-9.0010% -8.5562% -8.0274%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 1 (1.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking add_checked(0.5) | |
| Benchmarking add_checked(0.5): Warming up for 3.0000 s | |
| Benchmarking add_checked(0.5): Collecting 100 samples in estimated 5.0390 s (561k iterations) | |
| Benchmarking add_checked(0.5): Analyzing | |
| add_checked(0.5) time: [8.9797 µs 8.9882 µs 9.0043 µs] | |
| change: [-9.1652% -8.8776% -8.4107%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 1 (1.00%) low mild | |
| 6 (6.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking add_scalar(0.5) | |
| Benchmarking add_scalar(0.5): Warming up for 3.0000 s | |
| Benchmarking add_scalar(0.5): Collecting 100 samples in estimated 5.0036 s (909k iterations) | |
| Benchmarking add_scalar(0.5): Analyzing | |
| add_scalar(0.5) time: [5.5053 µs 5.5068 µs 5.5084 µs] | |
| change: [+3.1398% +3.3155% +3.4465%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 3 (3.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking subtract(0.5) | |
| Benchmarking subtract(0.5): Warming up for 3.0000 s | |
| Benchmarking subtract(0.5): Collecting 100 samples in estimated 5.0280 s (561k iterations) | |
| Benchmarking subtract(0.5): Analyzing | |
| subtract(0.5) time: [8.9721 µs 8.9739 µs 8.9758 µs] | |
| change: [-11.317% -11.210% -11.141%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 1 (1.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking subtract_checked(0.5) | |
| Benchmarking subtract_checked(0.5): Warming up for 3.0000 s | |
| Benchmarking subtract_checked(0.5): Collecting 100 samples in estimated 5.0246 s (561k iterations) | |
| Benchmarking subtract_checked(0.5): Analyzing | |
| subtract_checked(0.5) time: [8.9486 µs 8.9544 µs 8.9600 µs] | |
| change: [-11.546% -11.433% -11.348%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 20 outliers among 100 measurements (20.00%) | |
| 9 (9.00%) low severe | |
| 2 (2.00%) low mild | |
| 7 (7.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking subtract_scalar(0.5) | |
| Benchmarking subtract_scalar(0.5): Warming up for 3.0000 s | |
| Benchmarking subtract_scalar(0.5): Collecting 100 samples in estimated 5.0134 s (929k iterations) | |
| Benchmarking subtract_scalar(0.5): Analyzing | |
| subtract_scalar(0.5) time: [5.3934 µs 5.3945 µs 5.3957 µs] | |
| change: [-0.8308% -0.6977% -0.6062%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 1 (1.00%) low mild | |
| 5 (5.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking multiply(0.5) | |
| Benchmarking multiply(0.5): Warming up for 3.0000 s | |
| Benchmarking multiply(0.5): Collecting 100 samples in estimated 5.0033 s (556k iterations) | |
| Benchmarking multiply(0.5): Analyzing | |
| multiply(0.5) time: [9.0073 µs 9.0095 µs 9.0119 µs] | |
| change: [-11.033% -10.919% -10.800%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 14 outliers among 100 measurements (14.00%) | |
| 2 (2.00%) low severe | |
| 7 (7.00%) low mild | |
| 3 (3.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking multiply_checked(0.5) | |
| Benchmarking multiply_checked(0.5): Warming up for 3.0000 s | |
| Benchmarking multiply_checked(0.5): Collecting 100 samples in estimated 5.0441 s (561k iterations) | |
| Benchmarking multiply_checked(0.5): Analyzing | |
| multiply_checked(0.5) time: [8.9976 µs 9.0105 µs 9.0369 µs] | |
| change: [-11.194% -11.058% -10.904%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 3 (3.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking multiply_scalar(0.5) | |
| Benchmarking multiply_scalar(0.5): Warming up for 3.0000 s | |
| Benchmarking multiply_scalar(0.5): Collecting 100 samples in estimated 5.0194 s (929k iterations) | |
| Benchmarking multiply_scalar(0.5): Analyzing | |
| multiply_scalar(0.5) time: [5.4017 µs 5.4029 µs 5.4044 µs] | |
| change: [-0.9900% -0.8179% -0.6911%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 5 (5.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking divide(0.5) | |
| Benchmarking divide(0.5): Warming up for 3.0000 s | |
| Benchmarking divide(0.5): Collecting 100 samples in estimated 5.0478 s (308k iterations) | |
| Benchmarking divide(0.5): Analyzing | |
| divide(0.5) time: [16.373 µs 16.378 µs 16.384 µs] | |
| change: [-0.8002% -0.7224% -0.6579%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 4 (4.00%) high mild | |
| Benchmarking divide_scalar(0.5) | |
| Benchmarking divide_scalar(0.5): Warming up for 3.0000 s | |
| Benchmarking divide_scalar(0.5): Collecting 100 samples in estimated 5.0659 s (318k iterations) | |
| Benchmarking divide_scalar(0.5): Analyzing | |
| divide_scalar(0.5) time: [15.921 µs 15.924 µs 15.927 µs] | |
| change: [+0.6862% +0.8859% +1.0714%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 1 (1.00%) low mild | |
| 6 (6.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking modulo(0.5) | |
| Benchmarking modulo(0.5): Warming up for 3.0000 s | |
| Benchmarking modulo(0.5): Collecting 100 samples in estimated 6.7194 s (15k iterations) | |
| Benchmarking modulo(0.5): Analyzing | |
| modulo(0.5) time: [443.39 µs 443.66 µs 444.19 µs] | |
| change: [-1.7876% -1.6598% -1.4413%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 3 (3.00%) low mild | |
| 4 (4.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking modulo_scalar(0.5) | |
| Benchmarking modulo_scalar(0.5): Warming up for 3.0000 s | |
| Benchmarking modulo_scalar(0.5): Collecting 100 samples in estimated 5.7941 s (25k iterations) | |
| Benchmarking modulo_scalar(0.5): Analyzing | |
| modulo_scalar(0.5) time: [243.71 µs 243.75 µs 243.79 µs] | |
| change: [-8.8868% -8.8491% -8.8100%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 2 (2.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking add(0.9) | |
| Benchmarking add(0.9): Warming up for 3.0000 s | |
| Benchmarking add(0.9): Collecting 100 samples in estimated 5.0030 s (556k iterations) | |
| Benchmarking add(0.9): Analyzing | |
| add(0.9) time: [9.0091 µs 9.0122 µs 9.0156 µs] | |
| change: [-8.7640% -8.6668% -8.6041%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 2 (2.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking add_checked(0.9) | |
| Benchmarking add_checked(0.9): Warming up for 3.0000 s | |
| Benchmarking add_checked(0.9): Collecting 100 samples in estimated 5.0093 s (556k iterations) | |
| Benchmarking add_checked(0.9): Analyzing | |
| add_checked(0.9) time: [9.0080 µs 9.0101 µs 9.0125 µs] | |
| change: [-8.7879% -8.7136% -8.6540%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 2 (2.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking add_scalar(0.9) | |
| Benchmarking add_scalar(0.9): Warming up for 3.0000 s | |
| Benchmarking add_scalar(0.9): Collecting 100 samples in estimated 5.0012 s (904k iterations) | |
| Benchmarking add_scalar(0.9): Analyzing | |
| add_scalar(0.9) time: [5.5168 µs 5.5335 µs 5.5699 µs] | |
| change: [+4.5391% +4.8118% +5.1908%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 2 (2.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking subtract(0.9) | |
| Benchmarking subtract(0.9): Warming up for 3.0000 s | |
| Benchmarking subtract(0.9): Collecting 100 samples in estimated 5.0400 s (561k iterations) | |
| Benchmarking subtract(0.9): Analyzing | |
| subtract(0.9) time: [8.9924 µs 8.9949 µs 8.9976 µs] | |
| change: [-11.035% -10.970% -10.922%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 2 (2.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking subtract_checked(0.9) | |
| Benchmarking subtract_checked(0.9): Warming up for 3.0000 s | |
| Benchmarking subtract_checked(0.9): Collecting 100 samples in estimated 5.0367 s (561k iterations) | |
| Benchmarking subtract_checked(0.9): Analyzing | |
| subtract_checked(0.9) time: [8.9829 µs 8.9847 µs 8.9867 µs] | |
| change: [-11.149% -11.040% -10.941%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 5 (5.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking subtract_scalar(0.9) | |
| Benchmarking subtract_scalar(0.9): Warming up for 3.0000 s | |
| Benchmarking subtract_scalar(0.9): Collecting 100 samples in estimated 5.0101 s (929k iterations) | |
| Benchmarking subtract_scalar(0.9): Analyzing | |
| subtract_scalar(0.9) time: [5.3902 µs 5.3914 µs 5.3929 µs] | |
| change: [-0.9275% -0.7603% -0.6492%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 5 (5.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking multiply(0.9) | |
| Benchmarking multiply(0.9): Warming up for 3.0000 s | |
| Benchmarking multiply(0.9): Collecting 100 samples in estimated 5.0043 s (556k iterations) | |
| Benchmarking multiply(0.9): Analyzing | |
| multiply(0.9) time: [9.0074 µs 9.0100 µs 9.0126 µs] | |
| change: [-10.900% -10.828% -10.773%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 1 (1.00%) low mild | |
| 8 (8.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking multiply_checked(0.9) | |
| Benchmarking multiply_checked(0.9): Warming up for 3.0000 s | |
| Benchmarking multiply_checked(0.9): Collecting 100 samples in estimated 5.0007 s (556k iterations) | |
| Benchmarking multiply_checked(0.9): Analyzing | |
| multiply_checked(0.9) time: [8.9991 µs 9.0016 µs 9.0045 µs] | |
| change: [-11.112% -11.006% -10.932%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 5 (5.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking multiply_scalar(0.9) | |
| Benchmarking multiply_scalar(0.9): Warming up for 3.0000 s | |
| Benchmarking multiply_scalar(0.9): Collecting 100 samples in estimated 5.0125 s (929k iterations) | |
| Benchmarking multiply_scalar(0.9): Analyzing | |
| multiply_scalar(0.9) time: [5.3977 µs 5.3990 µs 5.4005 µs] | |
| change: [-1.0903% -0.9634% -0.8737%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 2 (2.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking divide(0.9) | |
| Benchmarking divide(0.9): Warming up for 3.0000 s | |
| Benchmarking divide(0.9): Collecting 100 samples in estimated 5.0655 s (308k iterations) | |
| Benchmarking divide(0.9): Analyzing | |
| divide(0.9) time: [16.490 µs 16.516 µs 16.559 µs] | |
| change: [+0.6131% +0.7470% +0.8994%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 2 (2.00%) low mild | |
| 1 (1.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking divide_scalar(0.9) | |
| Benchmarking divide_scalar(0.9): Warming up for 3.0000 s | |
| Benchmarking divide_scalar(0.9): Collecting 100 samples in estimated 5.0666 s (318k iterations) | |
| Benchmarking divide_scalar(0.9): Analyzing | |
| divide_scalar(0.9) time: [15.922 µs 15.926 µs 15.930 µs] | |
| change: [+0.9737% +1.0780% +1.1799%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 1 (1.00%) low severe | |
| 1 (1.00%) low mild | |
| 5 (5.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking modulo(0.9) | |
| Benchmarking modulo(0.9): Warming up for 3.0000 s | |
| Benchmarking modulo(0.9): Collecting 100 samples in estimated 6.1145 s (20k iterations) | |
| Benchmarking modulo(0.9): Analyzing | |
| modulo(0.9) time: [302.61 µs 302.65 µs 302.69 µs] | |
| change: [-0.3949% -0.3584% -0.3292%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 7 (7.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking modulo_scalar(0.9) | |
| Benchmarking modulo_scalar(0.9): Warming up for 3.0000 s | |
| Benchmarking modulo_scalar(0.9): Collecting 100 samples in estimated 5.2622 s (35k iterations) | |
| Benchmarking modulo_scalar(0.9): Analyzing | |
| modulo_scalar(0.9) time: [148.63 µs 148.65 µs 148.67 µs] | |
| change: [-9.4679% -9.3390% -9.2655%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 2 (2.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking add(1) | |
| Benchmarking add(1): Warming up for 3.0000 s | |
| Benchmarking add(1): Collecting 100 samples in estimated 5.0014 s (556k iterations) | |
| Benchmarking add(1): Analyzing | |
| add(1) time: [9.0095 µs 9.0141 µs 9.0209 µs] | |
| change: [-8.7729% -8.3668% -7.5040%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 5 (5.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking add_checked(1) | |
| Benchmarking add_checked(1): Warming up for 3.0000 s | |
| Benchmarking add_checked(1): Collecting 100 samples in estimated 5.0123 s (556k iterations) | |
| Benchmarking add_checked(1): Analyzing | |
| add_checked(1) time: [9.0221 µs 9.0248 µs 9.0278 µs] | |
| change: [-8.8118% -8.7231% -8.6616%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 6 (6.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking add_scalar(1) | |
| Benchmarking add_scalar(1): Warming up for 3.0000 s | |
| Benchmarking add_scalar(1): Collecting 100 samples in estimated 5.0127 s (909k iterations) | |
| Benchmarking add_scalar(1): Analyzing | |
| add_scalar(1) time: [5.5090 µs 5.5107 µs 5.5129 µs] | |
| change: [+4.0126% +4.2282% +4.4040%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 1 (1.00%) low mild | |
| 2 (2.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking subtract(1) | |
| Benchmarking subtract(1): Warming up for 3.0000 s | |
| Benchmarking subtract(1): Collecting 100 samples in estimated 5.0103 s (556k iterations) | |
| Benchmarking subtract(1): Analyzing | |
| subtract(1) time: [9.0062 µs 9.0085 µs 9.0109 µs] | |
| change: [-10.514% -10.393% -10.316%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 6 (6.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking subtract_checked(1) | |
| Benchmarking subtract_checked(1): Warming up for 3.0000 s | |
| Benchmarking subtract_checked(1): Collecting 100 samples in estimated 5.0450 s (561k iterations) | |
| Benchmarking subtract_checked(1): Analyzing | |
| subtract_checked(1) time: [8.9999 µs 9.0028 µs 9.0062 µs] | |
| change: [-10.945% -10.850% -10.777%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 4 (4.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking subtract_scalar(1) | |
| Benchmarking subtract_scalar(1): Warming up for 3.0000 s | |
| Benchmarking subtract_scalar(1): Collecting 100 samples in estimated 5.0176 s (929k iterations) | |
| Benchmarking subtract_scalar(1): Analyzing | |
| subtract_scalar(1) time: [5.3993 µs 5.4014 µs 5.4039 µs] | |
| change: [-0.8280% -0.6806% -0.5875%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 16 outliers among 100 measurements (16.00%) | |
| 1 (1.00%) low mild | |
| 6 (6.00%) high mild | |
| 9 (9.00%) high severe | |
| Benchmarking multiply(1) | |
| Benchmarking multiply(1): Warming up for 3.0000 s | |
| Benchmarking multiply(1): Collecting 100 samples in estimated 5.0262 s (556k iterations) | |
| Benchmarking multiply(1): Analyzing | |
| multiply(1) time: [9.0340 µs 9.0362 µs 9.0385 µs] | |
| change: [-9.8340% -9.7275% -9.6529%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 5 (5.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking multiply_checked(1) | |
| Benchmarking multiply_checked(1): Warming up for 3.0000 s | |
| Benchmarking multiply_checked(1): Collecting 100 samples in estimated 5.0201 s (556k iterations) | |
| Benchmarking multiply_checked(1): Analyzing | |
| multiply_checked(1) time: [9.0390 µs 9.0414 µs 9.0438 µs] | |
| change: [-9.9646% -9.8538% -9.7739%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 7 (7.00%) high mild | |
| Benchmarking multiply_scalar(1) | |
| Benchmarking multiply_scalar(1): Warming up for 3.0000 s | |
| Benchmarking multiply_scalar(1): Collecting 100 samples in estimated 5.0220 s (929k iterations) | |
| Benchmarking multiply_scalar(1): Analyzing | |
| multiply_scalar(1) time: [5.4055 µs 5.4072 µs 5.4090 µs] | |
| change: [-1.0831% -0.8852% -0.7646%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 1 (1.00%) low mild | |
| 5 (5.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking divide(1) | |
| Benchmarking divide(1): Warming up for 3.0000 s | |
| Benchmarking divide(1): Collecting 100 samples in estimated 5.0538 s (308k iterations) | |
| Benchmarking divide(1): Analyzing | |
| divide(1) time: [16.400 µs 16.404 µs 16.409 µs] | |
| change: [-0.3645% -0.2206% -0.0986%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) high mild | |
| Benchmarking divide_scalar(1) | |
| Benchmarking divide_scalar(1): Warming up for 3.0000 s | |
| Benchmarking divide_scalar(1): Collecting 100 samples in estimated 5.0640 s (318k iterations) | |
| Benchmarking divide_scalar(1): Analyzing | |
| divide_scalar(1) time: [15.914 µs 15.917 µs 15.920 µs] | |
| change: [+0.9757% +1.1085% +1.2592%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 16 outliers among 100 measurements (16.00%) | |
| 3 (3.00%) low severe | |
| 1 (1.00%) low mild | |
| 8 (8.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking modulo(1) | |
| Benchmarking modulo(1): Warming up for 3.0000 s | |
| Benchmarking modulo(1): Collecting 100 samples in estimated 5.4412 s (20k iterations) | |
| Benchmarking modulo(1): Analyzing | |
| modulo(1) time: [269.30 µs 269.37 µs 269.45 µs] | |
| change: [-0.3796% -0.2160% -0.1241%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 5 (5.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking modulo_scalar(1) | |
| Benchmarking modulo_scalar(1): Warming up for 3.0000 s | |
| Benchmarking modulo_scalar(1): Collecting 100 samples in estimated 5.4154 s (40k iterations) | |
| Benchmarking modulo_scalar(1): Analyzing | |
| modulo_scalar(1) time: [134.00 µs 134.02 µs 134.04 µs] | |
| change: [-6.2624% -6.2403% -6.2176%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 2 (2.00%) high mild | |
| 5 (5.00%) high severe | |
| Running benches/array_data_validate.rs (target/x86_64-unknown-linux-gnu/release/deps/array_data_validate-276ff357adc4bf8b) | |
| Benchmarking validate_binary_array_data 20000 | |
| Benchmarking validate_binary_array_data 20000: Warming up for 3.0000 s | |
| Benchmarking validate_binary_array_data 20000: Collecting 100 samples in estimated 5.0536 s (263k iterations) | |
| Benchmarking validate_binary_array_data 20000: Analyzing | |
| validate_binary_array_data 20000 | |
| time: [22.073 µs 22.115 µs 22.178 µs] | |
| change: [-10.914% -10.817% -10.680%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 1 (1.00%) low severe | |
| 8 (8.00%) low mild | |
| 1 (1.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking validate_utf8_array_data 20000 | |
| Benchmarking validate_utf8_array_data 20000: Warming up for 3.0000 s | |
| Benchmarking validate_utf8_array_data 20000: Collecting 100 samples in estimated 5.0053 s (126k iterations) | |
| Benchmarking validate_utf8_array_data 20000: Analyzing | |
| validate_utf8_array_data 20000 | |
| time: [39.642 µs 39.654 µs 39.668 µs] | |
| change: [+14.502% +14.564% +14.618%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 5 (5.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking byte_array_to_string_array 20000 | |
| Benchmarking byte_array_to_string_array 20000: Warming up for 3.0000 s | |
| Benchmarking byte_array_to_string_array 20000: Collecting 100 samples in estimated 5.0259 s (399k iterations) | |
| Benchmarking byte_array_to_string_array 20000: Analyzing | |
| byte_array_to_string_array 20000 | |
| time: [18.006 µs 18.010 µs 18.015 µs] | |
| change: [+37.187% +39.457% +41.413%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 18 outliers among 100 measurements (18.00%) | |
| 8 (8.00%) low severe | |
| 7 (7.00%) high mild | |
| 3 (3.00%) high severe | |
| Running benches/array_from_vec.rs (target/x86_64-unknown-linux-gnu/release/deps/array_from_vec-cf8a3cd7a353f28e) | |
| Benchmarking array_from_vec 128 | |
| Benchmarking array_from_vec 128: Warming up for 3.0000 s | |
| Benchmarking array_from_vec 128: Collecting 100 samples in estimated 5.0004 s (50M iterations) | |
| Benchmarking array_from_vec 128: Analyzing | |
| array_from_vec 128 time: [98.094 ns 98.189 ns 98.286 ns] | |
| change: [-1.5607% -1.4399% -1.3155%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking array_from_vec 256 | |
| Benchmarking array_from_vec 256: Warming up for 3.0000 s | |
| Benchmarking array_from_vec 256: Collecting 100 samples in estimated 5.0001 s (46M iterations) | |
| Benchmarking array_from_vec 256: Analyzing | |
| array_from_vec 256 time: [105.80 ns 106.01 ns 106.32 ns] | |
| change: [+0.5502% +0.7164% +0.9016%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 2 (2.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking array_from_vec 512 | |
| Benchmarking array_from_vec 512: Warming up for 3.0000 s | |
| Benchmarking array_from_vec 512: Collecting 100 samples in estimated 5.0008 s (33M iterations) | |
| Benchmarking array_from_vec 512: Analyzing | |
| array_from_vec 512 time: [149.79 ns 149.89 ns 150.00 ns] | |
| change: [+5.7626% +5.8823% +5.9959%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) high mild | |
| Benchmarking array_string_from_vec 128 | |
| Benchmarking array_string_from_vec 128: Warming up for 3.0000 s | |
| Benchmarking array_string_from_vec 128: Collecting 100 samples in estimated 5.0010 s (5.7M iterations) | |
| Benchmarking array_string_from_vec 128: Analyzing | |
| array_string_from_vec 128 | |
| time: [870.46 ns 870.79 ns 871.14 ns] | |
| change: [-17.458% -17.240% -17.039%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 1 (1.00%) low mild | |
| 2 (2.00%) high mild | |
| Benchmarking array_string_from_vec 256 | |
| Benchmarking array_string_from_vec 256: Warming up for 3.0000 s | |
| Benchmarking array_string_from_vec 256: Collecting 100 samples in estimated 5.0039 s (3.2M iterations) | |
| Benchmarking array_string_from_vec 256: Analyzing | |
| array_string_from_vec 256 | |
| time: [1.5811 µs 1.5815 µs 1.5819 µs] | |
| change: [-24.187% -24.154% -24.119%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 3 (3.00%) low mild | |
| 5 (5.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking array_string_from_vec 512 | |
| Benchmarking array_string_from_vec 512: Warming up for 3.0000 s | |
| Benchmarking array_string_from_vec 512: Collecting 100 samples in estimated 5.0004 s (1.7M iterations) | |
| Benchmarking array_string_from_vec 512: Analyzing | |
| array_string_from_vec 512 | |
| time: [2.9212 µs 2.9256 µs 2.9346 µs] | |
| change: [-24.020% -23.950% -23.841%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 2 (2.00%) low mild | |
| 2 (2.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking struct_array_from_vec 128 | |
| Benchmarking struct_array_from_vec 128: Warming up for 3.0000 s | |
| Benchmarking struct_array_from_vec 128: Collecting 100 samples in estimated 5.0054 s (3.4M iterations) | |
| Benchmarking struct_array_from_vec 128: Analyzing | |
| struct_array_from_vec 128 | |
| time: [1.4730 µs 1.4744 µs 1.4758 µs] | |
| change: [-11.364% -11.199% -11.060%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 4 (4.00%) low severe | |
| 4 (4.00%) low mild | |
| 3 (3.00%) high mild | |
| Benchmarking struct_array_from_vec 256 | |
| Benchmarking struct_array_from_vec 256: Warming up for 3.0000 s | |
| Benchmarking struct_array_from_vec 256: Collecting 100 samples in estimated 5.0015 s (2.2M iterations) | |
| Benchmarking struct_array_from_vec 256: Analyzing | |
| struct_array_from_vec 256 | |
| time: [2.2757 µs 2.2780 µs 2.2804 µs] | |
| change: [-12.099% -12.005% -11.905%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking struct_array_from_vec 512 | |
| Benchmarking struct_array_from_vec 512: Warming up for 3.0000 s | |
| Benchmarking struct_array_from_vec 512: Collecting 100 samples in estimated 5.0041 s (1.3M iterations) | |
| Benchmarking struct_array_from_vec 512: Analyzing | |
| struct_array_from_vec 512 | |
| time: [3.7934 µs 3.7961 µs 3.7993 µs] | |
| change: [-10.756% -10.646% -10.543%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking struct_array_from_vec 1024 | |
| Benchmarking struct_array_from_vec 1024: Warming up for 3.0000 s | |
| Benchmarking struct_array_from_vec 1024: Collecting 100 samples in estimated 5.0084 s (692k iterations) | |
| Benchmarking struct_array_from_vec 1024: Analyzing | |
| struct_array_from_vec 1024 | |
| time: [7.2005 µs 7.2183 µs 7.2444 µs] | |
| change: [-11.949% -11.813% -11.661%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high severe | |
| Benchmarking decimal128_array_from_vec 32768 | |
| Benchmarking decimal128_array_from_vec 32768: Warming up for 3.0000 s | |
| Benchmarking decimal128_array_from_vec 32768: Collecting 100 samples in estimated 5.4250 s (56k iterations) | |
| Benchmarking decimal128_array_from_vec 32768: Analyzing | |
| decimal128_array_from_vec 32768 | |
| time: [98.072 µs 98.112 µs 98.154 µs] | |
| change: [+40.815% +41.021% +41.185%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 1 (1.00%) low mild | |
| 5 (5.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking decimal256_array_from_vec 32768 | |
| Benchmarking decimal256_array_from_vec 32768: Warming up for 3.0000 s | |
| Benchmarking decimal256_array_from_vec 32768: Collecting 100 samples in estimated 5.0043 s (1.3M iterations) | |
| Benchmarking decimal256_array_from_vec 32768: Analyzing | |
| decimal256_array_from_vec 32768 | |
| time: [3.9888 µs 3.9917 µs 3.9948 µs] | |
| change: [+24.108% +24.200% +24.286%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high severe | |
| Running benches/array_slice.rs (target/x86_64-unknown-linux-gnu/release/deps/array_slice-8335b12ee339f727) | |
| Benchmarking array_slice 128 | |
| Benchmarking array_slice 128: Warming up for 3.0000 s | |
| Benchmarking array_slice 128: Collecting 100 samples in estimated 5.0002 s (101M iterations) | |
| Benchmarking array_slice 128: Analyzing | |
| array_slice 128 time: [48.936 ns 49.005 ns 49.076 ns] | |
| change: [-31.680% -31.609% -31.533%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking array_slice 512 | |
| Benchmarking array_slice 512: Warming up for 3.0000 s | |
| Benchmarking array_slice 512: Collecting 100 samples in estimated 5.0002 s (102M iterations) | |
| Benchmarking array_slice 512: Analyzing | |
| array_slice 512 time: [49.318 ns 49.400 ns 49.492 ns] | |
| change: [-17.262% -17.117% -16.965%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 15 outliers among 100 measurements (15.00%) | |
| 3 (3.00%) high mild | |
| 12 (12.00%) high severe | |
| Benchmarking array_slice 2048 | |
| Benchmarking array_slice 2048: Warming up for 3.0000 s | |
| Benchmarking array_slice 2048: Collecting 100 samples in estimated 5.0001 s (80M iterations) | |
| Benchmarking array_slice 2048: Analyzing | |
| array_slice 2048 time: [62.571 ns 62.654 ns 62.743 ns] | |
| change: [-16.082% -15.878% -15.660%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 3 (3.00%) high mild | |
| 1 (1.00%) high severe | |
| Running benches/bit_length_kernel.rs (target/x86_64-unknown-linux-gnu/release/deps/bit_length_kernel-e677e23d821185af) | |
| Benchmarking bit_length | |
| Benchmarking bit_length: Warming up for 3.0000 s | |
| Benchmarking bit_length: Collecting 100 samples in estimated 5.0006 s (7.1M iterations) | |
| Benchmarking bit_length: Analyzing | |
| bit_length time: [701.67 ns 701.99 ns 702.34 ns] | |
| change: [-29.347% -29.313% -29.271%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 13 outliers among 100 measurements (13.00%) | |
| 5 (5.00%) high mild | |
| 8 (8.00%) high severe | |
| Running benches/bitwise_kernel.rs (target/x86_64-unknown-linux-gnu/release/deps/bitwise_kernel-9162abd41cf75bdc) | |
| Benchmarking bench bitwise array: and/bitwise array and, no nulls | |
| Benchmarking bench bitwise array: and/bitwise array and, no nulls: Warming up for 3.0000 s | |
| Benchmarking bench bitwise array: and/bitwise array and, no nulls: Collecting 100 samples in estimated 5.0785 s (298k iterations) | |
| Benchmarking bench bitwise array: and/bitwise array and, no nulls: Analyzing | |
| bench bitwise array: and/bitwise array and, no nulls | |
| time: [17.198 µs 17.204 µs 17.210 µs] | |
| change: [+4.0683% +4.2119% +4.3630%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 2 (2.00%) low severe | |
| 6 (6.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking bench bitwise array: and/bitwise array and, 20% nulls | |
| Benchmarking bench bitwise array: and/bitwise array and, 20% nulls: Warming up for 3.0000 s | |
| Benchmarking bench bitwise array: and/bitwise array and, 20% nulls: Collecting 100 samples in estimated 5.0439 s (283k iterations) | |
| Benchmarking bench bitwise array: and/bitwise array and, 20% nulls: Analyzing | |
| bench bitwise array: and/bitwise array and, 20% nulls | |
| time: [18.113 µs 18.160 µs 18.206 µs] | |
| change: [-2.7337% -2.5470% -2.3699%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 2 (2.00%) low severe | |
| 1 (1.00%) low mild | |
| 3 (3.00%) high mild | |
| 3 (3.00%) high severe | |
| Criterion.rs ERROR: Error in Gnuplot: line 0: Bad format character | |
| Benchmarking bench bitwise: or/bitwise array or, no nulls | |
| Benchmarking bench bitwise: or/bitwise array or, no nulls: Warming up for 3.0000 s | |
| Benchmarking bench bitwise: or/bitwise array or, no nulls: Collecting 100 samples in estimated 5.0436 s (303k iterations) | |
| Benchmarking bench bitwise: or/bitwise array or, no nulls: Analyzing | |
| bench bitwise: or/bitwise array or, no nulls | |
| time: [16.963 µs 16.970 µs 16.977 µs] | |
| change: [+10.633% +10.764% +10.864%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) high mild | |
| Benchmarking bench bitwise: or/bitwise array or, 20% nulls | |
| Benchmarking bench bitwise: or/bitwise array or, 20% nulls: Warming up for 3.0000 s | |
| Benchmarking bench bitwise: or/bitwise array or, 20% nulls: Collecting 100 samples in estimated 5.0880 s (283k iterations) | |
| Benchmarking bench bitwise: or/bitwise array or, 20% nulls: Analyzing | |
| bench bitwise: or/bitwise array or, 20% nulls | |
| time: [18.006 µs 18.012 µs 18.018 µs] | |
| change: [+4.8638% +5.2221% +5.6184%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 2 (2.00%) low mild | |
| 1 (1.00%) high mild | |
| Criterion.rs ERROR: Error in Gnuplot: line 0: Bad format character | |
| Benchmarking bench bitwise: xor/bitwise array xor, no nulls | |
| Benchmarking bench bitwise: xor/bitwise array xor, no nulls: Warming up for 3.0000 s | |
| Benchmarking bench bitwise: xor/bitwise array xor, no nulls: Collecting 100 samples in estimated 5.0502 s (288k iterations) | |
| Benchmarking bench bitwise: xor/bitwise array xor, no nulls: Analyzing | |
| bench bitwise: xor/bitwise array xor, no nulls | |
| time: [17.835 µs 17.840 µs 17.845 µs] | |
| change: [+14.071% +14.648% +15.207%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 14 outliers among 100 measurements (14.00%) | |
| 1 (1.00%) low severe | |
| 2 (2.00%) low mild | |
| 7 (7.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking bench bitwise: xor/bitwise array xor, 20% nulls | |
| Benchmarking bench bitwise: xor/bitwise array xor, 20% nulls: Warming up for 3.0000 s | |
| Benchmarking bench bitwise: xor/bitwise array xor, 20% nulls: Collecting 100 samples in estimated 5.0556 s (288k iterations) | |
| Benchmarking bench bitwise: xor/bitwise array xor, 20% nulls: Analyzing | |
| bench bitwise: xor/bitwise array xor, 20% nulls | |
| time: [17.988 µs 17.997 µs 18.005 µs] | |
| change: [-4.3249% -3.5359% -2.8367%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 22 outliers among 100 measurements (22.00%) | |
| 19 (19.00%) low severe | |
| 2 (2.00%) high mild | |
| 1 (1.00%) high severe | |
| Criterion.rs ERROR: Error in Gnuplot: line 0: Bad format character | |
| Benchmarking bench bitwise: not/bitwise array not, no nulls | |
| Benchmarking bench bitwise: not/bitwise array not, no nulls: Warming up for 3.0000 s | |
| Benchmarking bench bitwise: not/bitwise array not, no nulls: Collecting 100 samples in estimated 5.0366 s (449k iterations) | |
| Benchmarking bench bitwise: not/bitwise array not, no nulls: Analyzing | |
| bench bitwise: not/bitwise array not, no nulls | |
| time: [11.289 µs 11.292 µs 11.294 µs] | |
| change: [-2.4246% -2.3021% -2.2186%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 2 (2.00%) low severe | |
| 2 (2.00%) low mild | |
| 3 (3.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking bench bitwise: not/bitwise array not, 20% nulls | |
| Benchmarking bench bitwise: not/bitwise array not, 20% nulls: Warming up for 3.0000 s | |
| Benchmarking bench bitwise: not/bitwise array not, 20% nulls: Collecting 100 samples in estimated 5.0390 s (439k iterations) | |
| Benchmarking bench bitwise: not/bitwise array not, 20% nulls: Analyzing | |
| bench bitwise: not/bitwise array not, 20% nulls | |
| time: [11.572 µs 11.574 µs 11.577 µs] | |
| change: [+11.007% +11.236% +11.377%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 18 outliers among 100 measurements (18.00%) | |
| 3 (3.00%) low severe | |
| 9 (9.00%) high mild | |
| 6 (6.00%) high severe | |
| Criterion.rs ERROR: Error in Gnuplot: line 0: Bad format character | |
| Benchmarking bench bitwise array scalar: and/bitwise array scalar and, no nulls | |
| Benchmarking bench bitwise array scalar: and/bitwise array scalar and, no nulls: Warming up for 3.0000 s | |
| Benchmarking bench bitwise array scalar: and/bitwise array scalar and, no nulls: Collecting 100 samples in estimated 5.0567 s (449k iterations) | |
| Benchmarking bench bitwise array scalar: and/bitwise array scalar and, no nulls: Analyzing | |
| bench bitwise array scalar: and/bitwise array scalar and, no nulls | |
| time: [11.379 µs 11.392 µs 11.405 µs] | |
| change: [-6.9982% -6.8559% -6.7335%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking bench bitwise array scalar: and/bitwise array and, 20% nulls | |
| Benchmarking bench bitwise array scalar: and/bitwise array and, 20% nulls: Warming up for 3.0000 s | |
| Benchmarking bench bitwise array scalar: and/bitwise array and, 20% nulls: Collecting 100 samples in estimated 5.0396 s (444k iterations) | |
| Benchmarking bench bitwise array scalar: and/bitwise array and, 20% nulls: Analyzing | |
| bench bitwise array scalar: and/bitwise array and, 20% nulls | |
| time: [11.470 µs 11.473 µs 11.477 µs] | |
| change: [-9.8720% -9.7422% -9.6227%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 1 (1.00%) high mild | |
| 1 (1.00%) high severe | |
| Criterion.rs ERROR: Error in Gnuplot: line 0: Bad format character | |
| Benchmarking bench bitwise array scalar: or/bitwise array scalar or, no nulls | |
| Benchmarking bench bitwise array scalar: or/bitwise array scalar or, no nulls: Warming up for 3.0000 s | |
| Benchmarking bench bitwise array scalar: or/bitwise array scalar or, no nulls: Collecting 100 samples in estimated 5.0272 s (444k iterations) | |
| Benchmarking bench bitwise array scalar: or/bitwise array scalar or, no nulls: Analyzing | |
| bench bitwise array scalar: or/bitwise array scalar or, no nulls | |
| time: [11.340 µs 11.343 µs 11.347 µs] | |
| change: [+2.5383% +3.3701% +4.2170%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 1 (1.00%) low mild | |
| 3 (3.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking bench bitwise array scalar: or/bitwise array scalar or, 20% nulls | |
| Benchmarking bench bitwise array scalar: or/bitwise array scalar or, 20% nulls: Warming up for 3.0000 s | |
| Benchmarking bench bitwise array scalar: or/bitwise array scalar or, 20% nulls: Collecting 100 samples in estimated 5.0102 s (439k iterations) | |
| Benchmarking bench bitwise array scalar: or/bitwise array scalar or, 20% nulls: Analyzing | |
| bench bitwise array scalar: or/bitwise array scalar or, 20% nulls | |
| time: [11.674 µs 11.676 µs 11.679 µs] | |
| change: [-7.5453% -7.4395% -7.3689%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 6 (6.00%) high mild | |
| 3 (3.00%) high severe | |
| Criterion.rs ERROR: Error in Gnuplot: line 0: Bad format character | |
| Benchmarking bench bitwise array scalar: xor/bitwise array scalar xor, no nulls | |
| Benchmarking bench bitwise array scalar: xor/bitwise array scalar xor, no nulls: Warming up for 3.0000 s | |
| Benchmarking bench bitwise array scalar: xor/bitwise array scalar xor, no nulls: Collecting 100 samples in estimated 5.0082 s (444k iterations) | |
| Benchmarking bench bitwise array scalar: xor/bitwise array scalar xor, no nulls: Analyzing | |
| bench bitwise array scalar: xor/bitwise array scalar xor, no nulls | |
| time: [11.125 µs 11.216 µs 11.297 µs] | |
| change: [-2.5633% -2.1939% -1.8866%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 5 (5.00%) low severe | |
| 3 (3.00%) low mild | |
| 1 (1.00%) high severe | |
| Benchmarking bench bitwise array scalar: xor/bitwise array scalar xor, 20% nulls | |
| Benchmarking bench bitwise array scalar: xor/bitwise array scalar xor, 20% nulls: Warming up for 3.0000 s | |
| Benchmarking bench bitwise array scalar: xor/bitwise array scalar xor, 20% nulls: Collecting 100 samples in estimated 5.0311 s (439k iterations) | |
| Benchmarking bench bitwise array scalar: xor/bitwise array scalar xor, 20% nulls: Analyzing | |
| bench bitwise array scalar: xor/bitwise array scalar xor, 20% nulls | |
| time: [11.456 µs 11.460 µs 11.464 µs] | |
| change: [-4.9597% -4.8472% -4.7649%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 1 (1.00%) low mild | |
| 1 (1.00%) high mild | |
| Criterion.rs ERROR: Error in Gnuplot: line 0: Bad format character | |
| Running benches/boolean_append_packed.rs (target/x86_64-unknown-linux-gnu/release/deps/boolean_append_packed-1b23b088ac3a9c11) | |
| Benchmarking boolean_append_packed | |
| Benchmarking boolean_append_packed: Warming up for 3.0000 s | |
| Benchmarking boolean_append_packed: Collecting 100 samples in estimated 5.0318 s (611k iterations) | |
| Benchmarking boolean_append_packed: Analyzing | |
| boolean_append_packed time: [8.2482 µs 8.2618 µs 8.2714 µs] | |
| change: [-41.564% -41.498% -41.441%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 4 (4.00%) low severe | |
| 2 (2.00%) high mild | |
| 1 (1.00%) high severe | |
| Running benches/boolean_kernels.rs (target/x86_64-unknown-linux-gnu/release/deps/boolean_kernels-3a492acaad0611e7) | |
| Benchmarking and | |
| Benchmarking and: Warming up for 3.0000 s | |
| Benchmarking and: Collecting 100 samples in estimated 5.0006 s (27M iterations) | |
| Benchmarking and: Analyzing | |
| and time: [190.41 ns 190.50 ns 190.60 ns] | |
| change: [-2.2798% -2.1405% -2.0355%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 4 (4.00%) high mild | |
| Benchmarking or | |
| Benchmarking or: Warming up for 3.0000 s | |
| Benchmarking or: Collecting 100 samples in estimated 5.0001 s (29M iterations) | |
| Benchmarking or: Analyzing | |
| or time: [173.02 ns 173.07 ns 173.14 ns] | |
| change: [-14.563% -14.514% -14.466%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 5 (5.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking not | |
| Benchmarking not: Warming up for 3.0000 s | |
| Benchmarking not: Collecting 100 samples in estimated 5.0032 s (7.3M iterations) | |
| Benchmarking not: Analyzing | |
| not time: [687.22 ns 687.36 ns 687.51 ns] | |
| change: [+239.89% +240.01% +240.13%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 2 (2.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking and_sliced | |
| Benchmarking and_sliced: Warming up for 3.0000 s | |
| Benchmarking and_sliced: Collecting 100 samples in estimated 5.0028 s (8.5M iterations) | |
| Benchmarking and_sliced: Analyzing | |
| and_sliced time: [586.31 ns 586.56 ns 586.82 ns] | |
| change: [-1.2377% -1.1591% -1.0655%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 3 (3.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking or_sliced | |
| Benchmarking or_sliced: Warming up for 3.0000 s | |
| Benchmarking or_sliced: Collecting 100 samples in estimated 5.0029 s (8.5M iterations) | |
| Benchmarking or_sliced: Analyzing | |
| or_sliced time: [587.21 ns 587.44 ns 587.68 ns] | |
| change: [+0.8366% +0.8914% +0.9454%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) high mild | |
| Benchmarking not_sliced | |
| Benchmarking not_sliced: Warming up for 3.0000 s | |
| Benchmarking not_sliced: Collecting 100 samples in estimated 5.0018 s (7.2M iterations) | |
| Benchmarking not_sliced: Analyzing | |
| not_sliced time: [697.20 ns 697.63 ns 698.11 ns] | |
| change: [+52.593% +52.786% +52.975%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 17 outliers among 100 measurements (17.00%) | |
| 5 (5.00%) high mild | |
| 12 (12.00%) high severe | |
| Running benches/buffer_bit_ops.rs (target/x86_64-unknown-linux-gnu/release/deps/buffer_bit_ops-56bda1ddd0145b41) | |
| Benchmarking buffer_binary_ops/and | |
| Benchmarking buffer_binary_ops/and: Warming up for 3.0000 s | |
| Benchmarking buffer_binary_ops/and: Collecting 100 samples in estimated 5.0008 s (29M iterations) | |
| Benchmarking buffer_binary_ops/and: Analyzing | |
| buffer_binary_ops/and time: [174.94 ns 174.99 ns 175.04 ns] | |
| thrpt: [81.724 GiB/s 81.748 GiB/s 81.770 GiB/s] | |
| change: | |
| time: [-12.279% -12.218% -12.164%] (p = 0.00 < 0.05) | |
| thrpt: [+13.848% +13.918% +13.998%] | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 2 (2.00%) low severe | |
| 2 (2.00%) low mild | |
| 2 (2.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking buffer_binary_ops/or | |
| Benchmarking buffer_binary_ops/or: Warming up for 3.0000 s | |
| Benchmarking buffer_binary_ops/or: Collecting 100 samples in estimated 5.0006 s (25M iterations) | |
| Benchmarking buffer_binary_ops/or: Analyzing | |
| buffer_binary_ops/or time: [199.15 ns 199.22 ns 199.30 ns] | |
| thrpt: [71.778 GiB/s 71.805 GiB/s 71.830 GiB/s] | |
| change: | |
| time: [+1.2392% +1.2907% +1.3388%] (p = 0.00 < 0.05) | |
| thrpt: [-1.3211% -1.2742% -1.2240%] | |
| Performance has regressed. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 1 (1.00%) low severe | |
| 1 (1.00%) low mild | |
| 4 (4.00%) high mild | |
| Benchmarking buffer_binary_ops/and_with_offset | |
| Benchmarking buffer_binary_ops/and_with_offset: Warming up for 3.0000 s | |
| Benchmarking buffer_binary_ops/and_with_offset: Collecting 100 samples in estimated 5.0026 s (7.2M iterations) | |
| Benchmarking buffer_binary_ops/and_with_offset: Analyzing | |
| buffer_binary_ops/and_with_offset | |
| time: [695.20 ns 695.49 ns 695.80 ns] | |
| thrpt: [20.559 GiB/s 20.568 GiB/s 20.577 GiB/s] | |
| change: | |
| time: [+0.1640% +0.2254% +0.2850%] (p = 0.00 < 0.05) | |
| thrpt: [-0.2842% -0.2249% -0.1638%] | |
| Change within noise threshold. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 6 (6.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking buffer_binary_ops/or_with_offset | |
| Benchmarking buffer_binary_ops/or_with_offset: Warming up for 3.0000 s | |
| Benchmarking buffer_binary_ops/or_with_offset: Collecting 100 samples in estimated 5.0001 s (7.2M iterations) | |
| Benchmarking buffer_binary_ops/or_with_offset: Analyzing | |
| buffer_binary_ops/or_with_offset | |
| time: [690.10 ns 690.42 ns 690.76 ns] | |
| thrpt: [20.709 GiB/s 20.719 GiB/s 20.729 GiB/s] | |
| change: | |
| time: [-0.6615% -0.5221% -0.4116%] (p = 0.00 < 0.05) | |
| thrpt: [+0.4133% +0.5249% +0.6659%] | |
| Change within noise threshold. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 6 (6.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking buffer_unary_ops/not | |
| Benchmarking buffer_unary_ops/not: Warming up for 3.0000 s | |
| Benchmarking buffer_unary_ops/not: Collecting 100 samples in estimated 5.0040 s (6.1M iterations) | |
| Benchmarking buffer_unary_ops/not: Analyzing | |
| buffer_unary_ops/not time: [820.78 ns 820.95 ns 821.15 ns] | |
| thrpt: [11.614 GiB/s 11.617 GiB/s 11.619 GiB/s] | |
| change: | |
| time: [+292.79% +293.08% +293.32%] (p = 0.00 < 0.05) | |
| thrpt: [-74.575% -74.560% -74.541%] | |
| Performance has regressed. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 4 (4.00%) high mild | |
| Benchmarking buffer_unary_ops/not_with_offset | |
| Benchmarking buffer_unary_ops/not_with_offset: Warming up for 3.0000 s | |
| Benchmarking buffer_unary_ops/not_with_offset: Collecting 100 samples in estimated 5.0005 s (6.0M iterations) | |
| Benchmarking buffer_unary_ops/not_with_offset: Analyzing | |
| buffer_unary_ops/not_with_offset | |
| time: [826.27 ns 826.43 ns 826.61 ns] | |
| thrpt: [11.537 GiB/s 11.540 GiB/s 11.542 GiB/s] | |
| change: | |
| time: [+45.774% +45.982% +46.124%] (p = 0.00 < 0.05) | |
| thrpt: [-31.565% -31.498% -31.401%] | |
| Performance has regressed. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 3 (3.00%) high mild | |
| 3 (3.00%) high severe | |
| Running benches/buffer_create.rs (target/x86_64-unknown-linux-gnu/release/deps/buffer_create-084600fb316ce5e4) | |
| Benchmarking mutable iter extend_from_slice | |
| Benchmarking mutable iter extend_from_slice: Warming up for 3.0000 s | |
| Benchmarking mutable iter extend_from_slice: Collecting 100 samples in estimated 5.1261 s (1700 iterations) | |
| Benchmarking mutable iter extend_from_slice: Analyzing | |
| mutable iter extend_from_slice | |
| time: [3.0075 ms 3.0086 ms 3.0098 ms] | |
| change: [+1.6951% +1.7450% +1.7944%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high mild | |
| Benchmarking mutable | |
| Benchmarking mutable: Warming up for 3.0000 s | |
| Benchmarking mutable: Collecting 100 samples in estimated 6.0904 s (20k iterations) | |
| Benchmarking mutable: Analyzing | |
| mutable time: [301.75 µs 301.79 µs 301.84 µs] | |
| change: [-2.5573% -2.2867% -2.0255%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 2 (2.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking mutable extend | |
| Benchmarking mutable extend: Warming up for 3.0000 s | |
| Benchmarking mutable extend: Collecting 100 samples in estimated 6.1381 s (10k iterations) | |
| Benchmarking mutable extend: Analyzing | |
| mutable extend time: [607.29 µs 607.57 µs 607.85 µs] | |
| change: [-1.5519% -1.4757% -1.4041%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 16 outliers among 100 measurements (16.00%) | |
| 1 (1.00%) low severe | |
| 9 (9.00%) low mild | |
| 3 (3.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking mutable prepared | |
| Benchmarking mutable prepared: Warming up for 3.0000 s | |
| Benchmarking mutable prepared: Collecting 100 samples in estimated 5.3216 s (25k iterations) | |
| Benchmarking mutable prepared: Analyzing | |
| mutable prepared time: [212.95 µs 213.00 µs 213.05 µs] | |
| change: [-0.5488% -0.3369% -0.1769%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Benchmarking from_slice | |
| Benchmarking from_slice: Warming up for 3.0000 s | |
| Benchmarking from_slice: Collecting 100 samples in estimated 5.0397 s (15k iterations) | |
| Benchmarking from_slice: Analyzing | |
| from_slice time: [334.77 µs 335.31 µs 336.03 µs] | |
| change: [+8.1235% +8.4748% +8.8308%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 14 outliers among 100 measurements (14.00%) | |
| 5 (5.00%) low severe | |
| 1 (1.00%) low mild | |
| 6 (6.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking from_slice prepared | |
| Benchmarking from_slice prepared: Warming up for 3.0000 s | |
| Benchmarking from_slice prepared: Collecting 100 samples in estimated 5.7384 s (20k iterations) | |
| Benchmarking from_slice prepared: Analyzing | |
| from_slice prepared time: [284.62 µs 284.67 µs 284.73 µs] | |
| change: [+10.281% +10.533% +10.724%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 1 (1.00%) low mild | |
| 2 (2.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking MutableBuffer iter bitset | |
| Benchmarking MutableBuffer iter bitset: Warming up for 3.0000 s | |
| Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 7.0s, or reduce sample count to 70. | |
| Benchmarking MutableBuffer iter bitset: Collecting 100 samples in estimated 7.0088 s (100 iterations) | |
| Benchmarking MutableBuffer iter bitset: Analyzing | |
| MutableBuffer iter bitset | |
| time: [69.896 ms 70.099 ms 70.327 ms] | |
| change: [-0.5335% +0.0385% +0.5572%] (p = 0.90 > 0.05) | |
| No change in performance detected. | |
| Found 16 outliers among 100 measurements (16.00%) | |
| 10 (10.00%) low mild | |
| 3 (3.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking MutableBuffer::from_iter bool | |
| Benchmarking MutableBuffer::from_iter bool: Warming up for 3.0000 s | |
| Benchmarking MutableBuffer::from_iter bool: Collecting 100 samples in estimated 5.2373 s (1200 iterations) | |
| Benchmarking MutableBuffer::from_iter bool: Analyzing | |
| MutableBuffer::from_iter bool | |
| time: [4.3867 ms 4.3880 ms 4.3892 ms] | |
| change: [+13.258% +13.300% +13.345%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 4 (4.00%) low mild | |
| 6 (6.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking Buffer::from_iter bool | |
| Benchmarking Buffer::from_iter bool: Warming up for 3.0000 s | |
| Benchmarking Buffer::from_iter bool: Collecting 100 samples in estimated 5.2946 s (1200 iterations) | |
| Benchmarking Buffer::from_iter bool: Analyzing | |
| Buffer::from_iter bool time: [4.4197 ms 4.4214 ms 4.4231 ms] | |
| change: [+13.964% +14.019% +14.068%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 13 outliers among 100 measurements (13.00%) | |
| 8 (8.00%) low severe | |
| 1 (1.00%) low mild | |
| 4 (4.00%) high mild | |
| Running benches/builder.rs (target/x86_64-unknown-linux-gnu/release/deps/builder-d01f830207a487cb) | |
| Benchmarking bench_primitive/bench_primitive | |
| Benchmarking bench_primitive/bench_primitive: Warming up for 3.0000 s | |
| Benchmarking bench_primitive/bench_primitive: Collecting 100 samples in estimated 5.0271 s (1500 iterations) | |
| Benchmarking bench_primitive/bench_primitive: Analyzing | |
| bench_primitive/bench_primitive | |
| time: [3.3479 ms 3.3490 ms 3.3502 ms] | |
| thrpt: [1.1660 GiB/s 1.1664 GiB/s 1.1668 GiB/s] | |
| change: | |
| time: [-0.0323% +0.0245% +0.0786%] (p = 0.39 > 0.05) | |
| thrpt: [-0.0786% -0.0245% +0.0323%] | |
| No change in performance detected. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 5 (5.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking bench_primitive_nulls/bench_primitive_nulls | |
| Benchmarking bench_primitive_nulls/bench_primitive_nulls: Warming up for 3.0000 s | |
| Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 8.2s, enable flat sampling, or reduce sample count to 50. | |
| Benchmarking bench_primitive_nulls/bench_primitive_nulls: Collecting 100 samples in estimated 8.2142 s (5050 iterations) | |
| Benchmarking bench_primitive_nulls/bench_primitive_nulls: Analyzing | |
| bench_primitive_nulls/bench_primitive_nulls | |
| time: [1.6261 ms 1.6264 ms 1.6267 ms] | |
| change: [-8.9688% -8.9072% -8.8024%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 16 outliers among 100 measurements (16.00%) | |
| 8 (8.00%) high mild | |
| 8 (8.00%) high severe | |
| Benchmarking bench_bool/bench_bool | |
| Benchmarking bench_bool/bench_bool: Warming up for 3.0000 s | |
| Benchmarking bench_bool/bench_bool: Collecting 100 samples in estimated 6.9607 s (10k iterations) | |
| Benchmarking bench_bool/bench_bool: Analyzing | |
| bench_bool/bench_bool time: [688.92 µs 689.08 µs 689.25 µs] | |
| thrpt: [725.42 MiB/s 725.60 MiB/s 725.77 MiB/s] | |
| change: | |
| time: [+60.394% +60.439% +60.488%] (p = 0.00 < 0.05) | |
| thrpt: [-37.690% -37.671% -37.653%] | |
| Performance has regressed. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 4 (4.00%) high mild | |
| Benchmarking bench_primitive/bench_string | |
| Benchmarking bench_primitive/bench_string: Warming up for 3.0000 s | |
| Benchmarking bench_primitive/bench_string: Collecting 100 samples in estimated 5.1816 s (1700 iterations) | |
| Benchmarking bench_primitive/bench_string: Analyzing | |
| bench_primitive/bench_string | |
| time: [2.8438 ms 2.8478 ms 2.8508 ms] | |
| thrpt: [2.2266 GiB/s 2.2289 GiB/s 2.2321 GiB/s] | |
| change: | |
| time: [-67.570% -67.509% -67.455%] (p = 0.00 < 0.05) | |
| thrpt: [+207.27% +207.77% +208.36%] | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 3 (3.00%) low severe | |
| 3 (3.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking bench_decimal128_builder | |
| Benchmarking bench_decimal128_builder: Warming up for 3.0000 s | |
| Benchmarking bench_decimal128_builder: Collecting 100 samples in estimated 5.6957 s (40k iterations) | |
| Benchmarking bench_decimal128_builder: Analyzing | |
| bench_decimal128_builder | |
| time: [140.73 µs 140.76 µs 140.80 µs] | |
| change: [-33.319% -33.288% -33.257%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) high mild | |
| Benchmarking bench_decimal128_builder #2 | |
| Benchmarking bench_decimal128_builder #2: Warming up for 3.0000 s | |
| Benchmarking bench_decimal128_builder #2: Collecting 100 samples in estimated 5.1388 s (50k iterations) | |
| Benchmarking bench_decimal128_builder #2: Analyzing | |
| bench_decimal128_builder #2 | |
| time: [101.81 µs 101.83 µs 101.85 µs] | |
| change: [-38.697% -38.676% -38.654%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 3 (3.00%) high mild | |
| 1 (1.00%) high severe | |
| Running benches/cast_kernels.rs (target/x86_64-unknown-linux-gnu/release/deps/cast_kernels-86756813a67b2377) | |
| Benchmarking cast int32 to int32 512 | |
| Benchmarking cast int32 to int32 512: Warming up for 3.0000 s | |
| Benchmarking cast int32 to int32 512: Collecting 100 samples in estimated 5.0006 s (39M iterations) | |
| Benchmarking cast int32 to int32 512: Analyzing | |
| cast int32 to int32 512 time: [128.73 ns 128.85 ns 128.96 ns] | |
| change: [-0.0327% +0.0520% +0.1359%] (p = 0.23 > 0.05) | |
| No change in performance detected. | |
| Benchmarking cast int32 to uint32 512 | |
| Benchmarking cast int32 to uint32 512: Warming up for 3.0000 s | |
| Benchmarking cast int32 to uint32 512: Collecting 100 samples in estimated 5.0007 s (3.8M iterations) | |
| Benchmarking cast int32 to uint32 512: Analyzing | |
| cast int32 to uint32 512 | |
| time: [1.3979 µs 1.3987 µs 1.3995 µs] | |
| change: [+19.966% +20.044% +20.128%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Benchmarking cast int32 to float32 512 | |
| Benchmarking cast int32 to float32 512: Warming up for 3.0000 s | |
| Benchmarking cast int32 to float32 512: Collecting 100 samples in estimated 5.0047 s (4.9M iterations) | |
| Benchmarking cast int32 to float32 512: Analyzing | |
| cast int32 to float32 512 | |
| time: [1.0215 µs 1.0219 µs 1.0224 µs] | |
| change: [-19.394% -17.077% -14.620%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 4 (4.00%) low mild | |
| 2 (2.00%) high mild | |
| Benchmarking cast int32 to float64 512 | |
| Benchmarking cast int32 to float64 512: Warming up for 3.0000 s | |
| Benchmarking cast int32 to float64 512: Collecting 100 samples in estimated 5.0022 s (5.3M iterations) | |
| Benchmarking cast int32 to float64 512: Analyzing | |
| cast int32 to float64 512 | |
| time: [952.83 ns 953.81 ns 955.05 ns] | |
| change: [-10.269% -7.7808% -5.2700%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 3 (3.00%) low mild | |
| 1 (1.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking cast int32 to int64 512 | |
| Benchmarking cast int32 to int64 512: Warming up for 3.0000 s | |
| Benchmarking cast int32 to int64 512: Collecting 100 samples in estimated 5.0030 s (4.8M iterations) | |
| Benchmarking cast int32 to int64 512: Analyzing | |
| cast int32 to int64 512 time: [1.0522 µs 1.0529 µs 1.0535 µs] | |
| change: [-25.841% -25.792% -25.738%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 2 (2.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking cast float32 to int32 512 | |
| Benchmarking cast float32 to int32 512: Warming up for 3.0000 s | |
| Benchmarking cast float32 to int32 512: Collecting 100 samples in estimated 5.0043 s (4.8M iterations) | |
| Benchmarking cast float32 to int32 512: Analyzing | |
| cast float32 to int32 512 | |
| time: [1.0362 µs 1.0406 µs 1.0474 µs] | |
| change: [-1.3704% -1.0504% -0.5687%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 1 (1.00%) low mild | |
| 2 (2.00%) high severe | |
| Benchmarking cast float64 to float32 512 | |
| Benchmarking cast float64 to float32 512: Warming up for 3.0000 s | |
| Benchmarking cast float64 to float32 512: Collecting 100 samples in estimated 5.0010 s (5.4M iterations) | |
| Benchmarking cast float64 to float32 512: Analyzing | |
| cast float64 to float32 512 | |
| time: [921.54 ns 922.03 ns 922.56 ns] | |
| change: [-21.488% -19.173% -16.660%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 3 (3.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking cast float64 to uint64 512 | |
| Benchmarking cast float64 to uint64 512: Warming up for 3.0000 s | |
| Benchmarking cast float64 to uint64 512: Collecting 100 samples in estimated 5.0039 s (3.8M iterations) | |
| Benchmarking cast float64 to uint64 512: Analyzing | |
| cast float64 to uint64 512 | |
| time: [1.3145 µs 1.3149 µs 1.3152 µs] | |
| change: [-1.9375% -1.8833% -1.8294%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 5 (5.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking cast int64 to int32 512 | |
| Benchmarking cast int64 to int32 512: Warming up for 3.0000 s | |
| Benchmarking cast int64 to int32 512: Collecting 100 samples in estimated 5.0045 s (4.3M iterations) | |
| Benchmarking cast int64 to int32 512: Analyzing | |
| cast int64 to int32 512 time: [1.2206 µs 1.2234 µs 1.2271 µs] | |
| change: [-2.7886% -2.6112% -2.4076%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 4 (4.00%) high severe | |
| Benchmarking cast date64 to date32 512 | |
| Benchmarking cast date64 to date32 512: Warming up for 3.0000 s | |
| Benchmarking cast date64 to date32 512: Collecting 100 samples in estimated 5.0006 s (12M iterations) | |
| Benchmarking cast date64 to date32 512: Analyzing | |
| cast date64 to date32 512 | |
| time: [419.03 ns 419.12 ns 419.22 ns] | |
| change: [-29.312% -29.285% -29.258%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 2 (2.00%) low mild | |
| 3 (3.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking cast date32 to date64 512 | |
| Benchmarking cast date32 to date64 512: Warming up for 3.0000 s | |
| Benchmarking cast date32 to date64 512: Collecting 100 samples in estimated 5.0012 s (18M iterations) | |
| Benchmarking cast date32 to date64 512: Analyzing | |
| cast date32 to date64 512 | |
| time: [280.71 ns 280.79 ns 280.88 ns] | |
| change: [-38.367% -38.218% -38.133%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 2 (2.00%) low mild | |
| 2 (2.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking cast time32s to time32ms 512 | |
| Benchmarking cast time32s to time32ms 512: Warming up for 3.0000 s | |
| Benchmarking cast time32s to time32ms 512: Collecting 100 samples in estimated 5.0006 s (25M iterations) | |
| Benchmarking cast time32s to time32ms 512: Analyzing | |
| cast time32s to time32ms 512 | |
| time: [205.75 ns 205.81 ns 205.87 ns] | |
| change: [-2.4938% -2.4487% -2.4034%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 2 (2.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking cast time32s to time64us 512 | |
| Benchmarking cast time32s to time64us 512: Warming up for 3.0000 s | |
| Benchmarking cast time32s to time64us 512: Collecting 100 samples in estimated 5.0005 s (18M iterations) | |
| Benchmarking cast time32s to time64us 512: Analyzing | |
| cast time32s to time64us 512 | |
| time: [281.55 ns 281.95 ns 282.73 ns] | |
| change: [-35.002% -34.926% -34.820%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 4 (4.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking cast time64ns to time32s 512 | |
| Benchmarking cast time64ns to time32s 512: Warming up for 3.0000 s | |
| Benchmarking cast time64ns to time32s 512: Collecting 100 samples in estimated 5.0019 s (12M iterations) | |
| Benchmarking cast time64ns to time32s 512: Analyzing | |
| cast time64ns to time32s 512 | |
| time: [419.94 ns 420.01 ns 420.10 ns] | |
| change: [-29.253% -29.193% -29.136%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 3 (3.00%) low mild | |
| 1 (1.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking cast timestamp_ns to timestamp_s 512 | |
| Benchmarking cast timestamp_ns to timestamp_s 512: Warming up for 3.0000 s | |
| Benchmarking cast timestamp_ns to timestamp_s 512: Collecting 100 samples in estimated 5.0005 s (38M iterations) | |
| Benchmarking cast timestamp_ns to timestamp_s 512: Analyzing | |
| cast timestamp_ns to timestamp_s 512 | |
| time: [128.34 ns 128.62 ns 128.95 ns] | |
| change: [+0.0143% +0.1990% +0.4291%] (p = 0.05 > 0.05) | |
| No change in performance detected. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 3 (3.00%) high mild | |
| Benchmarking cast timestamp_ms to timestamp_ns 512 | |
| Benchmarking cast timestamp_ms to timestamp_ns 512: Warming up for 3.0000 s | |
| Benchmarking cast timestamp_ms to timestamp_ns 512: Collecting 100 samples in estimated 5.0013 s (3.1M iterations) | |
| Benchmarking cast timestamp_ms to timestamp_ns 512: Analyzing | |
| cast timestamp_ms to timestamp_ns 512 | |
| time: [1.5937 µs 1.5941 µs 1.5944 µs] | |
| change: [-14.766% -13.501% -12.180%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 7 (7.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking cast utf8 to f32 | |
| Benchmarking cast utf8 to f32: Warming up for 3.0000 s | |
| Benchmarking cast utf8 to f32: Collecting 100 samples in estimated 5.0140 s (752k iterations) | |
| Benchmarking cast utf8 to f32: Analyzing | |
| cast utf8 to f32 time: [6.6720 µs 6.6864 µs 6.7086 µs] | |
| change: [-18.890% -18.783% -18.665%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 5 (5.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking cast i64 to string 512 | |
| Benchmarking cast i64 to string 512: Warming up for 3.0000 s | |
| Benchmarking cast i64 to string 512: Collecting 100 samples in estimated 5.0397 s (475k iterations) | |
| Benchmarking cast i64 to string 512: Analyzing | |
| cast i64 to string 512 time: [10.525 µs 10.528 µs 10.530 µs] | |
| change: [-21.274% -21.222% -21.179%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 1 (1.00%) low mild | |
| 2 (2.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking cast f32 to string 512 | |
| Benchmarking cast f32 to string 512: Warming up for 3.0000 s | |
| Benchmarking cast f32 to string 512: Collecting 100 samples in estimated 5.0180 s (409k iterations) | |
| Benchmarking cast f32 to string 512: Analyzing | |
| cast f32 to string 512 time: [12.329 µs 12.333 µs 12.337 µs] | |
| change: [-19.136% -18.958% -18.776%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 1 (1.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking cast f64 to string 512 | |
| Benchmarking cast f64 to string 512: Warming up for 3.0000 s | |
| Benchmarking cast f64 to string 512: Collecting 100 samples in estimated 5.0341 s (298k iterations) | |
| Benchmarking cast f64 to string 512: Analyzing | |
| cast f64 to string 512 time: [16.851 µs 16.856 µs 16.861 µs] | |
| change: [-12.526% -12.380% -12.270%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 3 (3.00%) low mild | |
| 3 (3.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking cast timestamp_ms to i64 512 | |
| Benchmarking cast timestamp_ms to i64 512: Warming up for 3.0000 s | |
| Benchmarking cast timestamp_ms to i64 512: Collecting 100 samples in estimated 5.0011 s (20M iterations) | |
| Benchmarking cast timestamp_ms to i64 512: Analyzing | |
| cast timestamp_ms to i64 512 | |
| time: [252.34 ns 252.77 ns 253.54 ns] | |
| change: [-3.6475% -3.5059% -3.2925%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 1 (1.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking cast utf8 to date32 512 | |
| Benchmarking cast utf8 to date32 512: Warming up for 3.0000 s | |
| Benchmarking cast utf8 to date32 512: Collecting 100 samples in estimated 5.0102 s (571k iterations) | |
| Benchmarking cast utf8 to date32 512: Analyzing | |
| cast utf8 to date32 512 time: [8.7656 µs 8.8055 µs 8.8546 µs] | |
| change: [-2.6280% -2.3068% -1.9456%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 5 (5.00%) high severe | |
| Benchmarking cast utf8 to date64 512 | |
| Benchmarking cast utf8 to date64 512: Warming up for 3.0000 s | |
| Benchmarking cast utf8 to date64 512: Collecting 100 samples in estimated 5.0290 s (162k iterations) | |
| Benchmarking cast utf8 to date64 512: Analyzing | |
| cast utf8 to date64 512 time: [30.937 µs 30.971 µs 31.031 µs] | |
| change: [-2.6380% -2.1389% -1.3636%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high severe | |
| Benchmarking cast decimal128 to decimal128 512 | |
| Benchmarking cast decimal128 to decimal128 512: Warming up for 3.0000 s | |
| Benchmarking cast decimal128 to decimal128 512: Collecting 100 samples in estimated 5.0017 s (2.1M iterations) | |
| Benchmarking cast decimal128 to decimal128 512: Analyzing | |
| cast decimal128 to decimal128 512 | |
| time: [2.3747 µs 2.3752 µs 2.3759 µs] | |
| change: [+7.7440% +7.9461% +8.1787%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 13 outliers among 100 measurements (13.00%) | |
| 4 (4.00%) high mild | |
| 9 (9.00%) high severe | |
| Benchmarking cast decimal128 to decimal256 512 | |
| Benchmarking cast decimal128 to decimal256 512: Warming up for 3.0000 s | |
| Benchmarking cast decimal128 to decimal256 512: Collecting 100 samples in estimated 5.0257 s (879k iterations) | |
| Benchmarking cast decimal128 to decimal256 512: Analyzing | |
| cast decimal128 to decimal256 512 | |
| time: [5.7295 µs 5.7357 µs 5.7424 µs] | |
| change: [-18.535% -18.451% -18.362%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 14 outliers among 100 measurements (14.00%) | |
| 8 (8.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking cast decimal256 to decimal128 512 | |
| Benchmarking cast decimal256 to decimal128 512: Warming up for 3.0000 s | |
| Benchmarking cast decimal256 to decimal128 512: Collecting 100 samples in estimated 5.0410 s (217k iterations) | |
| Benchmarking cast decimal256 to decimal128 512: Analyzing | |
| cast decimal256 to decimal128 512 | |
| time: [23.216 µs 23.224 µs 23.232 µs] | |
| change: [-3.0161% -2.9604% -2.9062%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 1 (1.00%) low severe | |
| 3 (3.00%) high mild | |
| Benchmarking cast decimal256 to decimal256 512 | |
| Benchmarking cast decimal256 to decimal256 512: Warming up for 3.0000 s | |
| Benchmarking cast decimal256 to decimal256 512: Collecting 100 samples in estimated 5.0238 s (843k iterations) | |
| Benchmarking cast decimal256 to decimal256 512: Analyzing | |
| cast decimal256 to decimal256 512 | |
| time: [5.9585 µs 5.9597 µs 5.9611 µs] | |
| change: [-15.411% -15.343% -15.281%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 6 (6.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking cast decimal128 to decimal128 512 with same scale | |
| Benchmarking cast decimal128 to decimal128 512 with same scale: Warming up for 3.0000 s | |
| Benchmarking cast decimal128 to decimal128 512 with same scale: Collecting 100 samples in estimated 5.0003 s (77M iterations) | |
| Benchmarking cast decimal128 to decimal128 512 with same scale: Analyzing | |
| cast decimal128 to decimal128 512 with same scale | |
| time: [64.417 ns 64.442 ns 64.468 ns] | |
| change: [-3.1557% -3.0843% -3.0141%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high mild | |
| Benchmarking cast decimal256 to decimal256 512 with same scale | |
| Benchmarking cast decimal256 to decimal256 512 with same scale: Warming up for 3.0000 s | |
| Benchmarking cast decimal256 to decimal256 512 with same scale: Collecting 100 samples in estimated 5.0003 s (77M iterations) | |
| Benchmarking cast decimal256 to decimal256 512 with same scale: Analyzing | |
| cast decimal256 to decimal256 512 with same scale | |
| time: [65.197 ns 65.268 ns 65.360 ns] | |
| change: [-2.2746% -2.0201% -1.5843%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 8 (8.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking cast dict to string view | |
| Benchmarking cast dict to string view: Warming up for 3.0000 s | |
| Benchmarking cast dict to string view: Collecting 100 samples in estimated 5.1687 s (141k iterations) | |
| Benchmarking cast dict to string view: Analyzing | |
| cast dict to string view | |
| time: [36.797 µs 36.805 µs 36.814 µs] | |
| change: [-23.322% -23.278% -23.235%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 6 (6.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking cast string view to dict | |
| Benchmarking cast string view to dict: Warming up for 3.0000 s | |
| Benchmarking cast string view to dict: Collecting 100 samples in estimated 5.1659 s (50k iterations) | |
| Benchmarking cast string view to dict: Analyzing | |
| cast string view to dict | |
| time: [102.19 µs 102.22 µs 102.26 µs] | |
| change: [-41.518% -41.480% -41.445%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 4 (4.00%) high mild | |
| Benchmarking cast string view to string | |
| Benchmarking cast string view to string: Warming up for 3.0000 s | |
| Benchmarking cast string view to string: Collecting 100 samples in estimated 5.1868 s (106k iterations) | |
| Benchmarking cast string view to string: Analyzing | |
| cast string view to string | |
| time: [48.141 µs 48.153 µs 48.166 µs] | |
| change: [-16.817% -15.893% -14.938%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 1 (1.00%) low severe | |
| 2 (2.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking cast string view to wide string | |
| Benchmarking cast string view to wide string: Warming up for 3.0000 s | |
| Benchmarking cast string view to wide string: Collecting 100 samples in estimated 5.0565 s (106k iterations) | |
| Benchmarking cast string view to wide string: Analyzing | |
| cast string view to wide string | |
| time: [47.820 µs 47.889 µs 48.001 µs] | |
| change: [-12.834% -12.511% -12.167%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 1 (1.00%) low mild | |
| 3 (3.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking cast binary view to string | |
| Benchmarking cast binary view to string: Warming up for 3.0000 s | |
| Benchmarking cast binary view to string: Collecting 100 samples in estimated 5.1965 s (91k iterations) | |
| Benchmarking cast binary view to string: Analyzing | |
| cast binary view to string | |
| time: [57.266 µs 57.281 µs 57.297 µs] | |
| change: [-17.196% -16.775% -16.451%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 5 (5.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking cast binary view to wide string | |
| Benchmarking cast binary view to wide string: Warming up for 3.0000 s | |
| Benchmarking cast binary view to wide string: Collecting 100 samples in estimated 5.1634 s (81k iterations) | |
| Benchmarking cast binary view to wide string: Analyzing | |
| cast binary view to wide string | |
| time: [56.639 µs 56.752 µs 56.985 µs] | |
| change: [-18.269% -18.140% -17.931%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 2 (2.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking cast string to binary view 512 | |
| Benchmarking cast string to binary view 512: Warming up for 3.0000 s | |
| Benchmarking cast string to binary view 512: Collecting 100 samples in estimated 5.0090 s (1.9M iterations) | |
| Benchmarking cast string to binary view 512: Analyzing | |
| cast string to binary view 512 | |
| time: [2.6664 µs 2.6686 µs 2.6705 µs] | |
| change: [-14.521% -14.438% -14.360%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 2 (2.00%) low mild | |
| 1 (1.00%) high severe | |
| Benchmarking cast wide string to binary view 512 | |
| Benchmarking cast wide string to binary view 512: Warming up for 3.0000 s | |
| Benchmarking cast wide string to binary view 512: Collecting 100 samples in estimated 5.0139 s (1.2M iterations) | |
| Benchmarking cast wide string to binary view 512: Analyzing | |
| cast wide string to binary view 512 | |
| time: [3.8693 µs 3.8721 µs 3.8772 µs] | |
| change: [-10.870% -10.666% -10.299%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 3 (3.00%) low mild | |
| 4 (4.00%) high severe | |
| Benchmarking cast string view to binary view | |
| Benchmarking cast string view to binary view: Warming up for 3.0000 s | |
| Benchmarking cast string view to binary view: Collecting 100 samples in estimated 5.0002 s (70M iterations) | |
| Benchmarking cast string view to binary view: Analyzing | |
| cast string view to binary view | |
| time: [71.579 ns 71.609 ns 71.640 ns] | |
| change: [+3.4350% +3.6175% +3.7531%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 1 (1.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking cast binary view to string view | |
| Benchmarking cast binary view to string view: Warming up for 3.0000 s | |
| Benchmarking cast binary view to string view: Collecting 100 samples in estimated 5.1352 s (71k iterations) | |
| Benchmarking cast binary view to string view: Analyzing | |
| cast binary view to string view | |
| time: [72.604 µs 72.620 µs 72.638 µs] | |
| change: [+1.1299% +1.1805% +1.2236%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 5 (5.00%) high mild | |
| 6 (6.00%) high severe | |
| Running benches/comparison_kernels.rs (target/x86_64-unknown-linux-gnu/release/deps/comparison_kernels-ac734e46ceaff97c) | |
| Benchmarking eq Float32 | |
| Benchmarking eq Float32: Warming up for 3.0000 s | |
| Benchmarking eq Float32: Collecting 100 samples in estimated 5.0445 s (207k iterations) | |
| Benchmarking eq Float32: Analyzing | |
| eq Float32 time: [24.222 µs 24.238 µs 24.258 µs] | |
| change: [-4.4771% -4.3472% -4.2158%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 3 (3.00%) high mild | |
| 9 (9.00%) high severe | |
| Benchmarking eq scalar Float32 | |
| Benchmarking eq scalar Float32: Warming up for 3.0000 s | |
| Benchmarking eq scalar Float32: Collecting 100 samples in estimated 5.0679 s (212k iterations) | |
| Benchmarking eq scalar Float32: Analyzing | |
| eq scalar Float32 time: [23.896 µs 23.926 µs 23.964 µs] | |
| change: [-0.7201% -0.4881% -0.1710%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high severe | |
| Benchmarking neq Float32 | |
| Benchmarking neq Float32: Warming up for 3.0000 s | |
| Benchmarking neq Float32: Collecting 100 samples in estimated 5.0335 s (207k iterations) | |
| Benchmarking neq Float32: Analyzing | |
| neq Float32 time: [24.296 µs 24.314 µs 24.332 µs] | |
| change: [-3.6711% -3.5213% -3.3748%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) high mild | |
| Benchmarking neq scalar Float32 | |
| Benchmarking neq scalar Float32: Warming up for 3.0000 s | |
| Benchmarking neq scalar Float32: Collecting 100 samples in estimated 5.0834 s (212k iterations) | |
| Benchmarking neq scalar Float32: Analyzing | |
| neq scalar Float32 time: [23.902 µs 23.926 µs 23.952 µs] | |
| change: [-0.7769% -0.6335% -0.4918%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Benchmarking lt Float32 | |
| Benchmarking lt Float32: Warming up for 3.0000 s | |
| Benchmarking lt Float32: Collecting 100 samples in estimated 5.0407 s (111k iterations) | |
| Benchmarking lt Float32: Analyzing | |
| lt Float32 time: [45.044 µs 45.272 µs 45.484 µs] | |
| change: [+3.4791% +3.8218% +4.1538%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 5 (5.00%) low severe | |
| 6 (6.00%) low mild | |
| 1 (1.00%) high mild | |
| Benchmarking lt scalar Float32 | |
| Benchmarking lt scalar Float32: Warming up for 3.0000 s | |
| Benchmarking lt scalar Float32: Collecting 100 samples in estimated 5.1559 s (146k iterations) | |
| Benchmarking lt scalar Float32: Analyzing | |
| lt scalar Float32 time: [34.703 µs 35.043 µs 35.395 µs] | |
| change: [+2.9041% +3.6208% +4.3991%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Benchmarking lt_eq Float32 | |
| Benchmarking lt_eq Float32: Warming up for 3.0000 s | |
| Benchmarking lt_eq Float32: Collecting 100 samples in estimated 5.0721 s (111k iterations) | |
| Benchmarking lt_eq Float32: Analyzing | |
| lt_eq Float32 time: [45.509 µs 45.571 µs 45.640 µs] | |
| change: [+3.4470% +3.5785% +3.7092%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 19 outliers among 100 measurements (19.00%) | |
| 7 (7.00%) low severe | |
| 2 (2.00%) high mild | |
| 10 (10.00%) high severe | |
| Benchmarking lt_eq scalar Float32 | |
| Benchmarking lt_eq scalar Float32: Warming up for 3.0000 s | |
| Benchmarking lt_eq scalar Float32: Collecting 100 samples in estimated 5.0161 s (146k iterations) | |
| Benchmarking lt_eq scalar Float32: Analyzing | |
| lt_eq scalar Float32 time: [34.195 µs 34.292 µs 34.410 µs] | |
| change: [+1.5242% +1.7637% +2.1179%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 19 outliers among 100 measurements (19.00%) | |
| 4 (4.00%) high mild | |
| 15 (15.00%) high severe | |
| Benchmarking gt Float32 | |
| Benchmarking gt Float32: Warming up for 3.0000 s | |
| Benchmarking gt Float32: Collecting 100 samples in estimated 5.0585 s (111k iterations) | |
| Benchmarking gt Float32: Analyzing | |
| gt Float32 time: [45.512 µs 45.603 µs 45.696 µs] | |
| change: [+3.9957% +4.2145% +4.4362%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) low mild | |
| Benchmarking gt scalar Float32 | |
| Benchmarking gt scalar Float32: Warming up for 3.0000 s | |
| Benchmarking gt scalar Float32: Collecting 100 samples in estimated 5.0641 s (146k iterations) | |
| Benchmarking gt scalar Float32: Analyzing | |
| gt scalar Float32 time: [34.283 µs 34.471 µs 34.669 µs] | |
| change: [+1.1235% +1.4206% +1.7564%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 1 (1.00%) high mild | |
| 9 (9.00%) high severe | |
| Benchmarking gt_eq Float32 | |
| Benchmarking gt_eq Float32: Warming up for 3.0000 s | |
| Benchmarking gt_eq Float32: Collecting 100 samples in estimated 5.0861 s (111k iterations) | |
| Benchmarking gt_eq Float32: Analyzing | |
| gt_eq Float32 time: [45.593 µs 45.660 µs 45.730 µs] | |
| change: [+3.6230% +3.9685% +4.2706%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 18 outliers among 100 measurements (18.00%) | |
| 3 (3.00%) low severe | |
| 1 (1.00%) low mild | |
| 4 (4.00%) high mild | |
| 10 (10.00%) high severe | |
| Benchmarking gt_eq scalar Float32 | |
| Benchmarking gt_eq scalar Float32: Warming up for 3.0000 s | |
| Benchmarking gt_eq scalar Float32: Collecting 100 samples in estimated 5.0703 s (141k iterations) | |
| Benchmarking gt_eq scalar Float32: Analyzing | |
| gt_eq scalar Float32 time: [35.397 µs 35.749 µs 36.086 µs] | |
| change: [+4.0348% +4.7700% +5.5000%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 21 outliers among 100 measurements (21.00%) | |
| 20 (20.00%) low severe | |
| 1 (1.00%) low mild | |
| Benchmarking eq Int32 | |
| Benchmarking eq Int32: Warming up for 3.0000 s | |
| Benchmarking eq Int32: Collecting 100 samples in estimated 5.0683 s (207k iterations) | |
| Benchmarking eq Int32: Analyzing | |
| eq Int32 time: [24.477 µs 24.487 µs 24.496 µs] | |
| change: [-6.5300% -5.8158% -5.2857%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high mild | |
| Benchmarking eq scalar Int32 | |
| Benchmarking eq scalar Int32: Warming up for 3.0000 s | |
| Benchmarking eq scalar Int32: Collecting 100 samples in estimated 5.0093 s (212k iterations) | |
| Benchmarking eq scalar Int32: Analyzing | |
| eq scalar Int32 time: [23.601 µs 23.610 µs 23.620 µs] | |
| change: [-1.5417% -1.1933% -0.9433%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 21 outliers among 100 measurements (21.00%) | |
| 1 (1.00%) low severe | |
| 1 (1.00%) low mild | |
| 5 (5.00%) high mild | |
| 14 (14.00%) high severe | |
| Benchmarking neq Int32 | |
| Benchmarking neq Int32: Warming up for 3.0000 s | |
| Benchmarking neq Int32: Collecting 100 samples in estimated 5.0509 s (207k iterations) | |
| Benchmarking neq Int32: Analyzing | |
| neq Int32 time: [24.385 µs 24.394 µs 24.403 µs] | |
| change: [-6.3442% -6.0488% -5.7620%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 20 outliers among 100 measurements (20.00%) | |
| 13 (13.00%) low mild | |
| 4 (4.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking neq scalar Int32 | |
| Benchmarking neq scalar Int32: Warming up for 3.0000 s | |
| Benchmarking neq scalar Int32: Collecting 100 samples in estimated 5.0100 s (212k iterations) | |
| Benchmarking neq scalar Int32: Analyzing | |
| neq scalar Int32 time: [23.623 µs 23.633 µs 23.641 µs] | |
| change: [-4.2311% -2.6116% -1.5009%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) low mild | |
| Benchmarking lt Int32 | |
| Benchmarking lt Int32: Warming up for 3.0000 s | |
| Benchmarking lt Int32: Collecting 100 samples in estimated 5.0919 s (207k iterations) | |
| Benchmarking lt Int32: Analyzing | |
| lt Int32 time: [24.486 µs 24.515 µs 24.546 µs] | |
| change: [-4.6892% -4.1421% -3.7856%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking lt scalar Int32 | |
| Benchmarking lt scalar Int32: Warming up for 3.0000 s | |
| Benchmarking lt scalar Int32: Collecting 100 samples in estimated 5.0354 s (207k iterations) | |
| Benchmarking lt scalar Int32: Analyzing | |
| lt scalar Int32 time: [24.244 µs 24.301 µs 24.360 µs] | |
| change: [+4.3790% +4.8518% +5.2180%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Benchmarking lt_eq Int32 | |
| Benchmarking lt_eq Int32: Warming up for 3.0000 s | |
| Benchmarking lt_eq Int32: Collecting 100 samples in estimated 5.0745 s (207k iterations) | |
| Benchmarking lt_eq Int32: Analyzing | |
| lt_eq Int32 time: [24.530 µs 24.558 µs 24.583 µs] | |
| change: [-4.2337% -4.1072% -3.9922%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking lt_eq scalar Int32 | |
| Benchmarking lt_eq scalar Int32: Warming up for 3.0000 s | |
| Benchmarking lt_eq scalar Int32: Collecting 100 samples in estimated 5.0767 s (212k iterations) | |
| Benchmarking lt_eq scalar Int32: Analyzing | |
| lt_eq scalar Int32 time: [23.879 µs 23.903 µs 23.929 µs] | |
| change: [+0.6689% +0.7731% +0.8764%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Benchmarking gt Int32 | |
| Benchmarking gt Int32: Warming up for 3.0000 s | |
| Benchmarking gt Int32: Collecting 100 samples in estimated 5.0795 s (207k iterations) | |
| Benchmarking gt Int32: Analyzing | |
| gt Int32 time: [24.478 µs 24.498 µs 24.522 µs] | |
| change: [-4.1668% -4.0516% -3.9505%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking gt scalar Int32 | |
| Benchmarking gt scalar Int32: Warming up for 3.0000 s | |
| Benchmarking gt scalar Int32: Collecting 100 samples in estimated 5.0890 s (212k iterations) | |
| Benchmarking gt scalar Int32: Analyzing | |
| gt scalar Int32 time: [23.871 µs 23.905 µs 23.952 µs] | |
| change: [+0.5718% +0.7798% +1.1053%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high severe | |
| Benchmarking gt_eq Int32 | |
| Benchmarking gt_eq Int32: Warming up for 3.0000 s | |
| Benchmarking gt_eq Int32: Collecting 100 samples in estimated 5.1065 s (207k iterations) | |
| Benchmarking gt_eq Int32: Analyzing | |
| gt_eq Int32 time: [24.568 µs 24.598 µs 24.626 µs] | |
| change: [-0.5054% -0.2213% +0.0338%] (p = 0.12 > 0.05) | |
| No change in performance detected. | |
| Found 28 outliers among 100 measurements (28.00%) | |
| 17 (17.00%) low severe | |
| 2 (2.00%) low mild | |
| 6 (6.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking gt_eq scalar Int32 | |
| Benchmarking gt_eq scalar Int32: Warming up for 3.0000 s | |
| Benchmarking gt_eq scalar Int32: Collecting 100 samples in estimated 5.0202 s (207k iterations) | |
| Benchmarking gt_eq scalar Int32: Analyzing | |
| gt_eq scalar Int32 time: [24.049 µs 24.086 µs 24.133 µs] | |
| change: [+3.0140% +3.2370% +3.4611%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 2 (2.00%) high mild | |
| 8 (8.00%) high severe | |
| Benchmarking eq MonthDayNano | |
| Benchmarking eq MonthDayNano: Warming up for 3.0000 s | |
| Benchmarking eq MonthDayNano: Collecting 100 samples in estimated 5.1232 s (86k iterations) | |
| Benchmarking eq MonthDayNano: Analyzing | |
| eq MonthDayNano time: [59.614 µs 59.644 µs 59.678 µs] | |
| change: [-0.0364% +0.0884% +0.1944%] (p = 0.14 > 0.05) | |
| No change in performance detected. | |
| Found 14 outliers among 100 measurements (14.00%) | |
| 13 (13.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking eq scalar MonthDayNano | |
| Benchmarking eq scalar MonthDayNano: Warming up for 3.0000 s | |
| Benchmarking eq scalar MonthDayNano: Collecting 100 samples in estimated 5.0361 s (121k iterations) | |
| Benchmarking eq scalar MonthDayNano: Analyzing | |
| eq scalar MonthDayNano time: [41.439 µs 41.476 µs 41.550 µs] | |
| change: [-2.3012% -2.0946% -1.7934%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 14 outliers among 100 measurements (14.00%) | |
| 9 (9.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking eq scalar StringArray | |
| Benchmarking eq scalar StringArray: Warming up for 3.0000 s | |
| Benchmarking eq scalar StringArray: Collecting 100 samples in estimated 5.7617 s (400 iterations) | |
| Benchmarking eq scalar StringArray: Analyzing | |
| eq scalar StringArray time: [14.290 ms 14.378 ms 14.476 ms] | |
| change: [-5.8046% -4.8857% -4.0281%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 5 (5.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking lt scalar StringViewArray | |
| Benchmarking lt scalar StringViewArray: Warming up for 3.0000 s | |
| Benchmarking lt scalar StringViewArray: Collecting 100 samples in estimated 5.3757 s (200 iterations) | |
| Benchmarking lt scalar StringViewArray: Analyzing | |
| lt scalar StringViewArray | |
| time: [26.869 ms 26.915 ms 26.982 ms] | |
| change: [-36.158% -35.978% -35.778%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 14 outliers among 100 measurements (14.00%) | |
| 14 (14.00%) high severe | |
| Benchmarking lt scalar StringArray | |
| Benchmarking lt scalar StringArray: Warming up for 3.0000 s | |
| Benchmarking lt scalar StringArray: Collecting 100 samples in estimated 6.6001 s (300 iterations) | |
| Benchmarking lt scalar StringArray: Analyzing | |
| lt scalar StringArray time: [21.912 ms 21.986 ms 22.075 ms] | |
| change: [-39.895% -39.601% -39.322%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 9 (9.00%) high severe | |
| Benchmarking eq scalar StringViewArray 4 bytes | |
| Benchmarking eq scalar StringViewArray 4 bytes: Warming up for 3.0000 s | |
| Benchmarking eq scalar StringViewArray 4 bytes: Collecting 100 samples in estimated 5.6300 s (700 iterations) | |
| Benchmarking eq scalar StringViewArray 4 bytes: Analyzing | |
| eq scalar StringViewArray 4 bytes | |
| time: [8.0187 ms 8.0590 ms 8.1069 ms] | |
| change: [-12.631% -11.825% -11.069%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 3 (3.00%) high mild | |
| 9 (9.00%) high severe | |
| Benchmarking eq scalar StringViewArray 6 bytes | |
| Benchmarking eq scalar StringViewArray 6 bytes: Warming up for 3.0000 s | |
| Benchmarking eq scalar StringViewArray 6 bytes: Collecting 100 samples in estimated 5.7217 s (700 iterations) | |
| Benchmarking eq scalar StringViewArray 6 bytes: Analyzing | |
| eq scalar StringViewArray 6 bytes | |
| time: [8.1176 ms 8.1690 ms 8.2326 ms] | |
| change: [-11.269% -10.395% -9.5350%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 10 (10.00%) high severe | |
| Benchmarking eq scalar StringViewArray 13 bytes | |
| Benchmarking eq scalar StringViewArray 13 bytes: Warming up for 3.0000 s | |
| Benchmarking eq scalar StringViewArray 13 bytes: Collecting 100 samples in estimated 5.7183 s (700 iterations) | |
| Benchmarking eq scalar StringViewArray 13 bytes: Analyzing | |
| eq scalar StringViewArray 13 bytes | |
| time: [8.0738 ms 8.1253 ms 8.1865 ms] | |
| change: [-9.2134% -8.3376% -7.4072%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 2 (2.00%) high mild | |
| 10 (10.00%) high severe | |
| Benchmarking eq StringArray StringArray | |
| Benchmarking eq StringArray StringArray: Warming up for 3.0000 s | |
| Benchmarking eq StringArray StringArray: Collecting 100 samples in estimated 6.5402 s (300 iterations) | |
| Benchmarking eq StringArray StringArray: Analyzing | |
| eq StringArray StringArray | |
| time: [21.648 ms 21.752 ms 21.869 ms] | |
| change: [-0.8003% -0.1353% +0.5543%] (p = 0.69 > 0.05) | |
| No change in performance detected. | |
| Found 16 outliers among 100 measurements (16.00%) | |
| 2 (2.00%) high mild | |
| 14 (14.00%) high severe | |
| Benchmarking eq StringViewArray StringViewArray | |
| Benchmarking eq StringViewArray StringViewArray: Warming up for 3.0000 s | |
| Benchmarking eq StringViewArray StringViewArray: Collecting 100 samples in estimated 5.4675 s (400 iterations) | |
| Benchmarking eq StringViewArray StringViewArray: Analyzing | |
| eq StringViewArray StringViewArray | |
| time: [13.605 ms 13.702 ms 13.820 ms] | |
| change: [-8.8239% -7.7724% -6.7450%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 12 (12.00%) high severe | |
| Benchmarking like_utf8 scalar equals | |
| Benchmarking like_utf8 scalar equals: Warming up for 3.0000 s | |
| Benchmarking like_utf8 scalar equals: Collecting 100 samples in estimated 5.2929 s (86k iterations) | |
| Benchmarking like_utf8 scalar equals: Analyzing | |
| like_utf8 scalar equals time: [61.655 µs 61.671 µs 61.687 µs] | |
| change: [-10.612% -10.565% -10.521%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 3 (3.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking like_utf8 scalar contains | |
| Benchmarking like_utf8 scalar contains: Warming up for 3.0000 s | |
| Benchmarking like_utf8 scalar contains: Collecting 100 samples in estimated 9.6982 s (10k iterations) | |
| Benchmarking like_utf8 scalar contains: Analyzing | |
| like_utf8 scalar contains | |
| time: [963.29 µs 965.65 µs 968.47 µs] | |
| change: [-13.942% -13.650% -13.282%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 14 outliers among 100 measurements (14.00%) | |
| 4 (4.00%) high mild | |
| 10 (10.00%) high severe | |
| Benchmarking like_utf8 scalar ends with | |
| Benchmarking like_utf8 scalar ends with: Warming up for 3.0000 s | |
| Benchmarking like_utf8 scalar ends with: Collecting 100 samples in estimated 5.0669 s (25k iterations) | |
| Benchmarking like_utf8 scalar ends with: Analyzing | |
| like_utf8 scalar ends with | |
| time: [201.12 µs 202.08 µs 203.26 µs] | |
| change: [+12.398% +13.452% +14.433%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 14 outliers among 100 measurements (14.00%) | |
| 1 (1.00%) high mild | |
| 13 (13.00%) high severe | |
| Benchmarking like_utf8 scalar starts with | |
| Benchmarking like_utf8 scalar starts with: Warming up for 3.0000 s | |
| Benchmarking like_utf8 scalar starts with: Collecting 100 samples in estimated 5.5006 s (35k iterations) | |
| Benchmarking like_utf8 scalar starts with: Analyzing | |
| like_utf8 scalar starts with | |
| time: [154.73 µs 155.28 µs 156.43 µs] | |
| change: [+8.0496% +9.1082% +10.146%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 3 (3.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking like_utf8 scalar complex | |
| Benchmarking like_utf8 scalar complex: Warming up for 3.0000 s | |
| Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 5.5s, enable flat sampling, or reduce sample count to 60. | |
| Benchmarking like_utf8 scalar complex: Collecting 100 samples in estimated 5.5354 s (5050 iterations) | |
| Benchmarking like_utf8 scalar complex: Analyzing | |
| like_utf8 scalar complex | |
| time: [1.0871 ms 1.0907 ms 1.0964 ms] | |
| change: [-0.0288% +0.1318% +0.3389%] (p = 0.19 > 0.05) | |
| No change in performance detected. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 1 (1.00%) low mild | |
| 4 (4.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking like_utf8view scalar equals | |
| Benchmarking like_utf8view scalar equals: Warming up for 3.0000 s | |
| Benchmarking like_utf8view scalar equals: Collecting 100 samples in estimated 6.4535 s (300 iterations) | |
| Benchmarking like_utf8view scalar equals: Analyzing | |
| like_utf8view scalar equals | |
| time: [21.513 ms 21.574 ms 21.650 ms] | |
| change: [-2.7397% -2.4012% -2.0036%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 2 (2.00%) high mild | |
| 10 (10.00%) high severe | |
| Benchmarking like_utf8view scalar contains | |
| Benchmarking like_utf8view scalar contains: Warming up for 3.0000 s | |
| Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 10.0s, or reduce sample count to 40. | |
| Benchmarking like_utf8view scalar contains: Collecting 100 samples in estimated 10.003 s (100 iterations) | |
| Benchmarking like_utf8view scalar contains: Analyzing | |
| like_utf8view scalar contains | |
| time: [99.668 ms 99.897 ms 100.19 ms] | |
| change: [-4.5798% -4.3604% -4.0864%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 5 (5.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking like_utf8view scalar ends with 4 bytes | |
| Benchmarking like_utf8view scalar ends with 4 bytes: Warming up for 3.0000 s | |
| Benchmarking like_utf8view scalar ends with 4 bytes: Collecting 100 samples in estimated 7.3704 s (200 iterations) | |
| Benchmarking like_utf8view scalar ends with 4 bytes: Analyzing | |
| like_utf8view scalar ends with 4 bytes | |
| time: [36.742 ms 36.849 ms 36.976 ms] | |
| change: [+33.112% +34.141% +35.009%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 16 outliers among 100 measurements (16.00%) | |
| 4 (4.00%) high mild | |
| 12 (12.00%) high severe | |
| Benchmarking like_utf8view scalar ends with 6 bytes | |
| Benchmarking like_utf8view scalar ends with 6 bytes: Warming up for 3.0000 s | |
| Benchmarking like_utf8view scalar ends with 6 bytes: Collecting 100 samples in estimated 7.3269 s (200 iterations) | |
| Benchmarking like_utf8view scalar ends with 6 bytes: Analyzing | |
| like_utf8view scalar ends with 6 bytes | |
| time: [36.491 ms 36.605 ms 36.745 ms] | |
| change: [+32.876% +33.954% +34.903%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 1 (1.00%) high mild | |
| 9 (9.00%) high severe | |
| Benchmarking like_utf8view scalar ends with 13 bytes | |
| Benchmarking like_utf8view scalar ends with 13 bytes: Warming up for 3.0000 s | |
| Benchmarking like_utf8view scalar ends with 13 bytes: Collecting 100 samples in estimated 7.2343 s (200 iterations) | |
| Benchmarking like_utf8view scalar ends with 13 bytes: Analyzing | |
| like_utf8view scalar ends with 13 bytes | |
| time: [35.979 ms 36.074 ms 36.193 ms] | |
| change: [+33.246% +34.551% +35.651%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 9 (9.00%) high severe | |
| Benchmarking like_utf8view scalar starts with 4 bytes | |
| Benchmarking like_utf8view scalar starts with 4 bytes: Warming up for 3.0000 s | |
| Benchmarking like_utf8view scalar starts with 4 bytes: Collecting 100 samples in estimated 5.4147 s (200 iterations) | |
| Benchmarking like_utf8view scalar starts with 4 bytes: Analyzing | |
| like_utf8view scalar starts with 4 bytes | |
| time: [27.012 ms 27.052 ms 27.100 ms] | |
| change: [+47.560% +48.229% +48.791%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 13 outliers among 100 measurements (13.00%) | |
| 3 (3.00%) high mild | |
| 10 (10.00%) high severe | |
| Benchmarking like_utf8view scalar starts with 6 bytes | |
| Benchmarking like_utf8view scalar starts with 6 bytes: Warming up for 3.0000 s | |
| Benchmarking like_utf8view scalar starts with 6 bytes: Collecting 100 samples in estimated 7.2344 s (200 iterations) | |
| Benchmarking like_utf8view scalar starts with 6 bytes: Analyzing | |
| like_utf8view scalar starts with 6 bytes | |
| time: [35.999 ms 36.097 ms 36.216 ms] | |
| change: [+31.554% +32.896% +34.028%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 13 outliers among 100 measurements (13.00%) | |
| 3 (3.00%) high mild | |
| 10 (10.00%) high severe | |
| Benchmarking like_utf8view scalar starts with 13 bytes | |
| Benchmarking like_utf8view scalar starts with 13 bytes: Warming up for 3.0000 s | |
| Benchmarking like_utf8view scalar starts with 13 bytes: Collecting 100 samples in estimated 7.0736 s (200 iterations) | |
| Benchmarking like_utf8view scalar starts with 13 bytes: Analyzing | |
| like_utf8view scalar starts with 13 bytes | |
| time: [35.179 ms 35.276 ms 35.394 ms] | |
| change: [+31.324% +32.447% +33.426%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 1 (1.00%) low severe | |
| 8 (8.00%) high severe | |
| Benchmarking like_utf8view scalar complex | |
| Benchmarking like_utf8view scalar complex: Warming up for 3.0000 s | |
| Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 14.2s, or reduce sample count to 30. | |
| Benchmarking like_utf8view scalar complex: Collecting 100 samples in estimated 14.177 s (100 iterations) | |
| Benchmarking like_utf8view scalar complex: Analyzing | |
| like_utf8view scalar complex | |
| time: [140.75 ms 140.82 ms 140.89 ms] | |
| change: [+1.9345% +2.0627% +2.1694%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 4 (4.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking nlike_utf8 scalar equals | |
| Benchmarking nlike_utf8 scalar equals: Warming up for 3.0000 s | |
| Benchmarking nlike_utf8 scalar equals: Collecting 100 samples in estimated 5.2946 s (86k iterations) | |
| Benchmarking nlike_utf8 scalar equals: Analyzing | |
| nlike_utf8 scalar equals | |
| time: [61.672 µs 61.687 µs 61.702 µs] | |
| change: [-10.890% -10.737% -10.613%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high severe | |
| Benchmarking nlike_utf8 scalar contains | |
| Benchmarking nlike_utf8 scalar contains: Warming up for 3.0000 s | |
| Benchmarking nlike_utf8 scalar contains: Collecting 100 samples in estimated 9.6804 s (10k iterations) | |
| Benchmarking nlike_utf8 scalar contains: Analyzing | |
| nlike_utf8 scalar contains | |
| time: [961.03 µs 961.20 µs 961.37 µs] | |
| change: [-14.224% -14.186% -14.152%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 6 (6.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking nlike_utf8 scalar ends with | |
| Benchmarking nlike_utf8 scalar ends with: Warming up for 3.0000 s | |
| Benchmarking nlike_utf8 scalar ends with: Collecting 100 samples in estimated 5.1698 s (25k iterations) | |
| Benchmarking nlike_utf8 scalar ends with: Analyzing | |
| nlike_utf8 scalar ends with | |
| time: [201.65 µs 203.29 µs 205.23 µs] | |
| change: [+12.718% +13.616% +14.506%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Benchmarking nlike_utf8 scalar starts with | |
| Benchmarking nlike_utf8 scalar starts with: Warming up for 3.0000 s | |
| Benchmarking nlike_utf8 scalar starts with: Collecting 100 samples in estimated 5.4827 s (35k iterations) | |
| Benchmarking nlike_utf8 scalar starts with: Analyzing | |
| nlike_utf8 scalar starts with | |
| time: [159.62 µs 161.37 µs 162.86 µs] | |
| change: [+3.2711% +4.0931% +4.8425%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 23 outliers among 100 measurements (23.00%) | |
| 1 (1.00%) high mild | |
| 22 (22.00%) high severe | |
| Benchmarking nlike_utf8 scalar complex | |
| Benchmarking nlike_utf8 scalar complex: Warming up for 3.0000 s | |
| Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 5.5s, enable flat sampling, or reduce sample count to 60. | |
| Benchmarking nlike_utf8 scalar complex: Collecting 100 samples in estimated 5.4845 s (5050 iterations) | |
| Benchmarking nlike_utf8 scalar complex: Analyzing | |
| nlike_utf8 scalar complex | |
| time: [1.0874 ms 1.0923 ms 1.0997 ms] | |
| change: [+0.2265% +0.7317% +1.4031%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 3 (3.00%) high mild | |
| 8 (8.00%) high severe | |
| Benchmarking ilike_utf8 scalar equals | |
| Benchmarking ilike_utf8 scalar equals: Warming up for 3.0000 s | |
| Benchmarking ilike_utf8 scalar equals: Collecting 100 samples in estimated 5.1645 s (10k iterations) | |
| Benchmarking ilike_utf8 scalar equals: Analyzing | |
| ilike_utf8 scalar equals | |
| time: [511.70 µs 511.83 µs 511.99 µs] | |
| change: [-5.6016% -5.2143% -4.8451%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 6 (6.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking ilike_utf8 scalar contains | |
| Benchmarking ilike_utf8 scalar contains: Warming up for 3.0000 s | |
| Benchmarking ilike_utf8 scalar contains: Collecting 100 samples in estimated 5.0111 s (1800 iterations) | |
| Benchmarking ilike_utf8 scalar contains: Analyzing | |
| ilike_utf8 scalar contains | |
| time: [2.7296 ms 2.7305 ms 2.7315 ms] | |
| change: [-2.8830% -2.8403% -2.7995%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 1 (1.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking ilike_utf8 scalar ends with | |
| Benchmarking ilike_utf8 scalar ends with: Warming up for 3.0000 s | |
| Benchmarking ilike_utf8 scalar ends with: Collecting 100 samples in estimated 6.9656 s (10k iterations) | |
| Benchmarking ilike_utf8 scalar ends with: Analyzing | |
| ilike_utf8 scalar ends with | |
| time: [689.86 µs 691.06 µs 692.77 µs] | |
| change: [-2.7864% -2.6100% -2.4543%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 3 (3.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking ilike_utf8 scalar starts with | |
| Benchmarking ilike_utf8 scalar starts with: Warming up for 3.0000 s | |
| Benchmarking ilike_utf8 scalar starts with: Collecting 100 samples in estimated 6.5139 s (10k iterations) | |
| Benchmarking ilike_utf8 scalar starts with: Analyzing | |
| ilike_utf8 scalar starts with | |
| time: [644.25 µs 644.43 µs 644.63 µs] | |
| change: [-7.4361% -7.3405% -7.2631%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 1 (1.00%) low mild | |
| 5 (5.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking ilike_utf8 scalar complex | |
| Benchmarking ilike_utf8 scalar complex: Warming up for 3.0000 s | |
| Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 8.0s, enable flat sampling, or reduce sample count to 50. | |
| Benchmarking ilike_utf8 scalar complex: Collecting 100 samples in estimated 7.9514 s (5050 iterations) | |
| Benchmarking ilike_utf8 scalar complex: Analyzing | |
| ilike_utf8 scalar complex | |
| time: [1.5786 ms 1.5806 ms 1.5829 ms] | |
| change: [+0.4518% +0.7054% +0.9486%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Benchmarking nilike_utf8 scalar equals | |
| Benchmarking nilike_utf8 scalar equals: Warming up for 3.0000 s | |
| Benchmarking nilike_utf8 scalar equals: Collecting 100 samples in estimated 5.1605 s (10k iterations) | |
| Benchmarking nilike_utf8 scalar equals: Analyzing | |
| nilike_utf8 scalar equals | |
| time: [513.06 µs 515.86 µs 518.76 µs] | |
| change: [-4.4932% -4.2013% -3.8764%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 13 outliers among 100 measurements (13.00%) | |
| 4 (4.00%) high mild | |
| 9 (9.00%) high severe | |
| Benchmarking nilike_utf8 scalar contains | |
| Benchmarking nilike_utf8 scalar contains: Warming up for 3.0000 s | |
| Benchmarking nilike_utf8 scalar contains: Collecting 100 samples in estimated 5.0178 s (1800 iterations) | |
| Benchmarking nilike_utf8 scalar contains: Analyzing | |
| nilike_utf8 scalar contains | |
| time: [2.7286 ms 2.7293 ms 2.7301 ms] | |
| change: [-2.9912% -2.9138% -2.8450%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 4 (4.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking nilike_utf8 scalar ends with | |
| Benchmarking nilike_utf8 scalar ends with: Warming up for 3.0000 s | |
| Benchmarking nilike_utf8 scalar ends with: Collecting 100 samples in estimated 6.9621 s (10k iterations) | |
| Benchmarking nilike_utf8 scalar ends with: Analyzing | |
| nilike_utf8 scalar ends with | |
| time: [689.17 µs 690.50 µs 693.13 µs] | |
| change: [+2.6422% +2.8190% +3.0356%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 6 (6.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking nilike_utf8 scalar starts with | |
| Benchmarking nilike_utf8 scalar starts with: Warming up for 3.0000 s | |
| Benchmarking nilike_utf8 scalar starts with: Collecting 100 samples in estimated 6.5159 s (10k iterations) | |
| Benchmarking nilike_utf8 scalar starts with: Analyzing | |
| nilike_utf8 scalar starts with | |
| time: [644.10 µs 644.23 µs 644.37 µs] | |
| change: [-3.5120% -3.2423% -3.0012%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 7 (7.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking nilike_utf8 scalar complex | |
| Benchmarking nilike_utf8 scalar complex: Warming up for 3.0000 s | |
| Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 8.0s, enable flat sampling, or reduce sample count to 50. | |
| Benchmarking nilike_utf8 scalar complex: Collecting 100 samples in estimated 8.0113 s (5050 iterations) | |
| Benchmarking nilike_utf8 scalar complex: Analyzing | |
| nilike_utf8 scalar complex | |
| time: [1.5797 ms 1.5815 ms 1.5846 ms] | |
| change: [+0.0898% +0.3620% +0.6004%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 3 (3.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking StringArray: regexp_matches_utf8 scalar benchmarks/regexp_matches_utf8 scalar starts with | |
| Benchmarking StringArray: regexp_matches_utf8 scalar benchmarks/regexp_matches_utf8 scalar starts with: Warming up for 3.0000 s | |
| Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 5.5s, enable flat sampling, or reduce sample count to 60. | |
| Benchmarking StringArray: regexp_matches_utf8 scalar benchmarks/regexp_matches_utf8 scalar starts with: Collecting 100 samples in estimated 5.4979 s (5050 iterations) | |
| Benchmarking StringArray: regexp_matches_utf8 scalar benchmarks/regexp_matches_utf8 scalar starts with: Analyzing | |
| StringArray: regexp_matches_utf8 scalar benchmarks/regexp_matches_utf8 scalar starts with | |
| time: [1.0440 ms 1.0461 ms 1.0492 ms] | |
| change: [-14.379% -13.920% -13.445%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high severe | |
| Benchmarking StringArray: regexp_matches_utf8 scalar benchmarks/regexp_matches_utf8 scalar contains | |
| Benchmarking StringArray: regexp_matches_utf8 scalar benchmarks/regexp_matches_utf8 scalar contains: Warming up for 3.0000 s | |
| Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 8.6s, enable flat sampling, or reduce sample count to 50. | |
| Benchmarking StringArray: regexp_matches_utf8 scalar benchmarks/regexp_matches_utf8 scalar contains: Collecting 100 samples in estimated 8.6262 s (5050 iterations) | |
| Benchmarking StringArray: regexp_matches_utf8 scalar benchmarks/regexp_matches_utf8 scalar contains: Analyzing | |
| StringArray: regexp_matches_utf8 scalar benchmarks/regexp_matches_utf8 scalar contains | |
| time: [1.7110 ms 1.7113 ms 1.7117 ms] | |
| change: [-14.959% -14.881% -14.807%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 2 (2.00%) low mild | |
| 2 (2.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking StringArray: regexp_matches_utf8 scalar benchmarks/regexp_matches_utf8 scalar ends with | |
| Benchmarking StringArray: regexp_matches_utf8 scalar benchmarks/regexp_matches_utf8 scalar ends with: Warming up for 3.0000 s | |
| Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 5.3s, enable flat sampling, or reduce sample count to 60. | |
| Benchmarking StringArray: regexp_matches_utf8 scalar benchmarks/regexp_matches_utf8 scalar ends with: Collecting 100 samples in estimated 5.3475 s (5050 iterations) | |
| Benchmarking StringArray: regexp_matches_utf8 scalar benchmarks/regexp_matches_utf8 scalar ends with: Analyzing | |
| StringArray: regexp_matches_utf8 scalar benchmarks/regexp_matches_utf8 scalar ends with | |
| time: [1.0591 ms 1.0595 ms 1.0599 ms] | |
| change: [-9.9322% -9.7830% -9.6581%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 2 (2.00%) low mild | |
| 1 (1.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking StringArray: regexp_matches_utf8 scalar benchmarks/regexp_matches_utf8 scalar complex | |
| Benchmarking StringArray: regexp_matches_utf8 scalar benchmarks/regexp_matches_utf8 scalar complex: Warming up for 3.0000 s | |
| Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 8.9s, enable flat sampling, or reduce sample count to 50. | |
| Benchmarking StringArray: regexp_matches_utf8 scalar benchmarks/regexp_matches_utf8 scalar complex: Collecting 100 samples in estimated 8.9349 s (5050 iterations) | |
| Benchmarking StringArray: regexp_matches_utf8 scalar benchmarks/regexp_matches_utf8 scalar complex: Analyzing | |
| StringArray: regexp_matches_utf8 scalar benchmarks/regexp_matches_utf8 scalar complex | |
| time: [1.7682 ms 1.7687 ms 1.7692 ms] | |
| change: [-10.994% -10.921% -10.849%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 4 (4.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking StringViewArray: regexp_matches_utf8view scalar benchmarks/regexp_matches_utf8view scalar starts wit... | |
| Benchmarking StringViewArray: regexp_matches_utf8view scalar benchmarks/regexp_matches_utf8view scalar starts wit...: Warming up for 3.0000 s | |
| Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 5.6s, enable flat sampling, or reduce sample count to 60. | |
| Benchmarking StringViewArray: regexp_matches_utf8view scalar benchmarks/regexp_matches_utf8view scalar starts wit...: Collecting 100 samples in estimated 5.6049 s (5050 iterations) | |
| Benchmarking StringViewArray: regexp_matches_utf8view scalar benchmarks/regexp_matches_utf8view scalar starts wit...: Analyzing | |
| StringViewArray: regexp_matches_utf8view scalar benchmarks/regexp_matches_utf8view scalar starts wit... | |
| time: [1.1134 ms 1.1224 ms 1.1360 ms] | |
| change: [-11.900% -11.383% -10.535%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 5 (5.00%) low mild | |
| 4 (4.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking StringViewArray: regexp_matches_utf8view scalar benchmarks/regexp_matches_utf8view scalar contains | |
| Benchmarking StringViewArray: regexp_matches_utf8view scalar benchmarks/regexp_matches_utf8view scalar contains: Warming up for 3.0000 s | |
| Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 9.0s, enable flat sampling, or reduce sample count to 50. | |
| Benchmarking StringViewArray: regexp_matches_utf8view scalar benchmarks/regexp_matches_utf8view scalar contains: Collecting 100 samples in estimated 9.0442 s (5050 iterations) | |
| Benchmarking StringViewArray: regexp_matches_utf8view scalar benchmarks/regexp_matches_utf8view scalar contains: Analyzing | |
| StringViewArray: regexp_matches_utf8view scalar benchmarks/regexp_matches_utf8view scalar contains | |
| time: [1.7846 ms 1.7849 ms 1.7854 ms] | |
| change: [-12.050% -11.691% -11.445%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 1 (1.00%) low mild | |
| 3 (3.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking StringViewArray: regexp_matches_utf8view scalar benchmarks/regexp_matches_utf8view scalar ends with | |
| Benchmarking StringViewArray: regexp_matches_utf8view scalar benchmarks/regexp_matches_utf8view scalar ends with: Warming up for 3.0000 s | |
| Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 5.9s, enable flat sampling, or reduce sample count to 60. | |
| Benchmarking StringViewArray: regexp_matches_utf8view scalar benchmarks/regexp_matches_utf8view scalar ends with: Collecting 100 samples in estimated 5.8537 s (5050 iterations) | |
| Benchmarking StringViewArray: regexp_matches_utf8view scalar benchmarks/regexp_matches_utf8view scalar ends with: Analyzing | |
| StringViewArray: regexp_matches_utf8view scalar benchmarks/regexp_matches_utf8view scalar ends with | |
| time: [1.1722 ms 1.1743 ms 1.1775 ms] | |
| change: [-3.9348% -3.6239% -3.3383%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 1 (1.00%) low mild | |
| 2 (2.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking StringViewArray: regexp_matches_utf8view scalar benchmarks/regexp_matches_utf8view scalar complex | |
| Benchmarking StringViewArray: regexp_matches_utf8view scalar benchmarks/regexp_matches_utf8view scalar complex: Warming up for 3.0000 s | |
| Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 9.2s, enable flat sampling, or reduce sample count to 50. | |
| Benchmarking StringViewArray: regexp_matches_utf8view scalar benchmarks/regexp_matches_utf8view scalar complex: Collecting 100 samples in estimated 9.2486 s (5050 iterations) | |
| Benchmarking StringViewArray: regexp_matches_utf8view scalar benchmarks/regexp_matches_utf8view scalar complex: Analyzing | |
| StringViewArray: regexp_matches_utf8view scalar benchmarks/regexp_matches_utf8view scalar complex | |
| time: [1.8306 ms 1.8314 ms 1.8322 ms] | |
| change: [-9.3370% -9.2533% -9.1753%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 3 (3.00%) high mild | |
| Benchmarking eq_dyn_utf8_scalar dictionary[10] string[4]) | |
| Benchmarking eq_dyn_utf8_scalar dictionary[10] string[4]): Warming up for 3.0000 s | |
| Benchmarking eq_dyn_utf8_scalar dictionary[10] string[4]): Collecting 100 samples in estimated 5.0843 s (136k iterations) | |
| Benchmarking eq_dyn_utf8_scalar dictionary[10] string[4]): Analyzing | |
| eq_dyn_utf8_scalar dictionary[10] string[4]) | |
| time: [37.272 µs 37.278 µs 37.285 µs] | |
| change: [+1.2997% +1.9080% +2.3884%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 13 outliers among 100 measurements (13.00%) | |
| 1 (1.00%) low severe | |
| 2 (2.00%) low mild | |
| 6 (6.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking gt_eq_dyn_utf8_scalar scalar dictionary[10] string[4]) | |
| Benchmarking gt_eq_dyn_utf8_scalar scalar dictionary[10] string[4]): Warming up for 3.0000 s | |
| Benchmarking gt_eq_dyn_utf8_scalar scalar dictionary[10] string[4]): Collecting 100 samples in estimated 5.2410 s (66k iterations) | |
| Benchmarking gt_eq_dyn_utf8_scalar scalar dictionary[10] string[4]): Analyzing | |
| gt_eq_dyn_utf8_scalar scalar dictionary[10] string[4]) | |
| time: [79.657 µs 79.684 µs 79.716 µs] | |
| change: [-8.9363% -8.7340% -8.6176%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 3 (3.00%) high mild | |
| Benchmarking like_utf8_scalar_dyn dictionary[10] string[4]) | |
| Benchmarking like_utf8_scalar_dyn dictionary[10] string[4]): Warming up for 3.0000 s | |
| Benchmarking like_utf8_scalar_dyn dictionary[10] string[4]): Collecting 100 samples in estimated 5.1088 s (136k iterations) | |
| Benchmarking like_utf8_scalar_dyn dictionary[10] string[4]): Analyzing | |
| like_utf8_scalar_dyn dictionary[10] string[4]) | |
| time: [37.415 µs 37.422 µs 37.429 µs] | |
| change: [+2.0697% +2.1077% +2.1399%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 15 outliers among 100 measurements (15.00%) | |
| 3 (3.00%) low severe | |
| 2 (2.00%) low mild | |
| 6 (6.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking ilike_utf8_scalar_dyn dictionary[10] string[4]) | |
| Benchmarking ilike_utf8_scalar_dyn dictionary[10] string[4]): Warming up for 3.0000 s | |
| Benchmarking ilike_utf8_scalar_dyn dictionary[10] string[4]): Collecting 100 samples in estimated 5.1286 s (136k iterations) | |
| Benchmarking ilike_utf8_scalar_dyn dictionary[10] string[4]): Analyzing | |
| ilike_utf8_scalar_dyn dictionary[10] string[4]) | |
| time: [37.712 µs 37.727 µs 37.739 µs] | |
| change: [+2.2482% +2.2964% +2.3412%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Benchmarking eq dictionary[10] string[4]) | |
| Benchmarking eq dictionary[10] string[4]): Warming up for 3.0000 s | |
| Benchmarking eq dictionary[10] string[4]): Collecting 100 samples in estimated 7.7344 s (10k iterations) | |
| Benchmarking eq dictionary[10] string[4]): Analyzing | |
| eq dictionary[10] string[4]) | |
| time: [765.76 µs 765.86 µs 765.95 µs] | |
| change: [+2.5485% +2.5777% +2.6101%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 1 (1.00%) low mild | |
| 5 (5.00%) high mild | |
| 5 (5.00%) high severe | |
| Running benches/concatenate_kernel.rs (target/x86_64-unknown-linux-gnu/release/deps/concatenate_kernel-c41c87454308923a) | |
| Benchmarking concat i32 1024 | |
| Benchmarking concat i32 1024: Warming up for 3.0000 s | |
| Benchmarking concat i32 1024: Collecting 100 samples in estimated 5.0020 s (9.1M iterations) | |
| Benchmarking concat i32 1024: Analyzing | |
| concat i32 1024 time: [543.13 ns 543.31 ns 543.51 ns] | |
| change: [-1.6409% -1.5577% -1.4768%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 1 (1.00%) low mild | |
| 4 (4.00%) high mild | |
| Benchmarking concat i32 nulls 1024 | |
| Benchmarking concat i32 nulls 1024: Warming up for 3.0000 s | |
| Benchmarking concat i32 nulls 1024: Collecting 100 samples in estimated 5.0007 s (5.9M iterations) | |
| Benchmarking concat i32 nulls 1024: Analyzing | |
| concat i32 nulls 1024 time: [843.17 ns 843.32 ns 843.49 ns] | |
| change: [-0.6269% -0.5884% -0.5501%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 1 (1.00%) low severe | |
| 4 (4.00%) low mild | |
| 1 (1.00%) high mild | |
| Benchmarking concat 1024 arrays i32 4 | |
| Benchmarking concat 1024 arrays i32 4: Warming up for 3.0000 s | |
| Benchmarking concat 1024 arrays i32 4: Collecting 100 samples in estimated 5.3798 s (40k iterations) | |
| Benchmarking concat 1024 arrays i32 4: Analyzing | |
| concat 1024 arrays i32 4 | |
| time: [136.40 µs 136.52 µs 136.62 µs] | |
| change: [-4.7559% -4.6246% -4.4969%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 1 (1.00%) low mild | |
| 3 (3.00%) high mild | |
| Benchmarking concat str 1024 | |
| Benchmarking concat str 1024: Warming up for 3.0000 s | |
| Benchmarking concat str 1024: Collecting 100 samples in estimated 5.0145 s (439k iterations) | |
| Benchmarking concat str 1024: Analyzing | |
| concat str 1024 time: [11.393 µs 11.398 µs 11.402 µs] | |
| change: [-1.7266% -1.5325% -1.4085%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 1 (1.00%) low mild | |
| 2 (2.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking concat str nulls 1024 | |
| Benchmarking concat str nulls 1024: Warming up for 3.0000 s | |
| Benchmarking concat str nulls 1024: Collecting 100 samples in estimated 5.0132 s (707k iterations) | |
| Benchmarking concat str nulls 1024: Analyzing | |
| concat str nulls 1024 time: [7.0905 µs 7.0928 µs 7.0952 µs] | |
| change: [-0.8813% -0.7710% -0.6590%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) high mild | |
| Benchmarking concat str_dict 1024 | |
| Benchmarking concat str_dict 1024: Warming up for 3.0000 s | |
| Benchmarking concat str_dict 1024: Collecting 100 samples in estimated 5.0097 s (1.6M iterations) | |
| Benchmarking concat str_dict 1024: Analyzing | |
| concat str_dict 1024 time: [3.0182 µs 3.0189 µs 3.0197 µs] | |
| change: [+1.3543% +1.3934% +1.4407%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 1 (1.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking concat str_dict_sparse 1024 | |
| Benchmarking concat str_dict_sparse 1024: Warming up for 3.0000 s | |
| Benchmarking concat str_dict_sparse 1024: Collecting 100 samples in estimated 5.0131 s (853k iterations) | |
| Benchmarking concat str_dict_sparse 1024: Analyzing | |
| concat str_dict_sparse 1024 | |
| time: [5.8451 µs 5.8477 µs 5.8505 µs] | |
| change: [-2.2761% -2.2300% -2.1827%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 6 (6.00%) high mild | |
| Benchmarking concat str nulls 1024 #2 | |
| Benchmarking concat str nulls 1024 #2: Warming up for 3.0000 s | |
| Benchmarking concat str nulls 1024 #2: Collecting 100 samples in estimated 5.0166 s (697k iterations) | |
| Benchmarking concat str nulls 1024 #2: Analyzing | |
| concat str nulls 1024 #2 | |
| time: [7.2015 µs 7.2442 µs 7.2932 µs] | |
| change: [+1.0894% +1.3204% +1.5831%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 1 (1.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking concat fixed size lists | |
| Benchmarking concat fixed size lists: Warming up for 3.0000 s | |
| Benchmarking concat fixed size lists: Collecting 100 samples in estimated 5.9223 s (30k iterations) | |
| Benchmarking concat fixed size lists: Analyzing | |
| concat fixed size lists time: [194.91 µs 194.98 µs 195.07 µs] | |
| change: [+2.4657% +2.7964% +3.0202%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 7 (7.00%) high mild | |
| 3 (3.00%) high severe | |
| Running benches/csv_reader.rs (target/x86_64-unknown-linux-gnu/release/deps/csv_reader-5bfd310486363cdf) | |
| Benchmarking 4096 i32_small(0) - 128 | |
| Benchmarking 4096 i32_small(0) - 128: Warming up for 3.0000 s | |
| Benchmarking 4096 i32_small(0) - 128: Collecting 100 samples in estimated 5.2527 s (45k iterations) | |
| Benchmarking 4096 i32_small(0) - 128: Analyzing | |
| 4096 i32_small(0) - 128 time: [113.73 µs 113.76 µs 113.79 µs] | |
| change: [-16.959% -16.906% -16.850%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 4 (4.00%) high severe | |
| Benchmarking 4096 i32_small(0) - 1024 | |
| Benchmarking 4096 i32_small(0) - 1024: Warming up for 3.0000 s | |
| Benchmarking 4096 i32_small(0) - 1024: Collecting 100 samples in estimated 5.0227 s (50k iterations) | |
| Benchmarking 4096 i32_small(0) - 1024: Analyzing | |
| 4096 i32_small(0) - 1024 | |
| time: [99.391 µs 99.405 µs 99.421 µs] | |
| change: [-18.954% -18.936% -18.917%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 4 (4.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking 4096 i32_small(0) - 4096 | |
| Benchmarking 4096 i32_small(0) - 4096: Warming up for 3.0000 s | |
| Benchmarking 4096 i32_small(0) - 4096: Collecting 100 samples in estimated 5.4226 s (56k iterations) | |
| Benchmarking 4096 i32_small(0) - 4096: Analyzing | |
| 4096 i32_small(0) - 4096 | |
| time: [97.579 µs 97.595 µs 97.614 µs] | |
| change: [-18.764% -18.700% -18.660%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 5 (5.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking 4096 i32(0) - 128 | |
| Benchmarking 4096 i32(0) - 128: Warming up for 3.0000 s | |
| Benchmarking 4096 i32(0) - 128: Collecting 100 samples in estimated 5.8328 s (30k iterations) | |
| Benchmarking 4096 i32(0) - 128: Analyzing | |
| 4096 i32(0) - 128 time: [192.54 µs 192.76 µs 192.94 µs] | |
| change: [-9.6472% -9.5806% -9.5101%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 3 (3.00%) low severe | |
| 4 (4.00%) high mild | |
| Benchmarking 4096 i32(0) - 1024 | |
| Benchmarking 4096 i32(0) - 1024: Warming up for 3.0000 s | |
| Benchmarking 4096 i32(0) - 1024: Collecting 100 samples in estimated 5.2707 s (30k iterations) | |
| Benchmarking 4096 i32(0) - 1024: Analyzing | |
| 4096 i32(0) - 1024 time: [173.48 µs 174.31 µs 175.39 µs] | |
| change: [-11.157% -10.775% -10.282%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 16 outliers among 100 measurements (16.00%) | |
| 5 (5.00%) low severe | |
| 8 (8.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking 4096 i32(0) - 4096 | |
| Benchmarking 4096 i32(0) - 4096: Warming up for 3.0000 s | |
| Benchmarking 4096 i32(0) - 4096: Collecting 100 samples in estimated 5.1811 s (30k iterations) | |
| Benchmarking 4096 i32(0) - 4096: Analyzing | |
| 4096 i32(0) - 4096 time: [170.65 µs 171.05 µs 171.60 µs] | |
| change: [-11.948% -11.511% -11.010%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 15 outliers among 100 measurements (15.00%) | |
| 9 (9.00%) low severe | |
| 3 (3.00%) low mild | |
| 3 (3.00%) high severe | |
| Benchmarking 4096 u64_small(0) - 128 | |
| Benchmarking 4096 u64_small(0) - 128: Warming up for 3.0000 s | |
| Benchmarking 4096 u64_small(0) - 128: Collecting 100 samples in estimated 5.3985 s (45k iterations) | |
| Benchmarking 4096 u64_small(0) - 128: Analyzing | |
| 4096 u64_small(0) - 128 time: [118.28 µs 118.83 µs 119.54 µs] | |
| change: [-13.311% -12.986% -12.615%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 4 (4.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking 4096 u64_small(0) - 1024 | |
| Benchmarking 4096 u64_small(0) - 1024: Warming up for 3.0000 s | |
| Benchmarking 4096 u64_small(0) - 1024: Collecting 100 samples in estimated 5.1226 s (50k iterations) | |
| Benchmarking 4096 u64_small(0) - 1024: Analyzing | |
| 4096 u64_small(0) - 1024 | |
| time: [100.83 µs 101.16 µs 101.71 µs] | |
| change: [-15.782% -15.512% -15.104%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 1 (1.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking 4096 u64_small(0) - 4096 | |
| Benchmarking 4096 u64_small(0) - 4096: Warming up for 3.0000 s | |
| Benchmarking 4096 u64_small(0) - 4096: Collecting 100 samples in estimated 5.0298 s (50k iterations) | |
| Benchmarking 4096 u64_small(0) - 4096: Analyzing | |
| 4096 u64_small(0) - 4096 | |
| time: [99.105 µs 99.126 µs 99.146 µs] | |
| change: [-16.371% -16.097% -15.935%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 4 (4.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking 4096 u64(0) - 128 | |
| Benchmarking 4096 u64(0) - 128: Warming up for 3.0000 s | |
| Benchmarking 4096 u64(0) - 128: Collecting 100 samples in estimated 6.0200 s (25k iterations) | |
| Benchmarking 4096 u64(0) - 128: Analyzing | |
| 4096 u64(0) - 128 time: [237.42 µs 237.87 µs 238.83 µs] | |
| change: [-4.6209% -4.5349% -4.3474%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 2 (2.00%) low mild | |
| 3 (3.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking 4096 u64(0) - 1024 | |
| Benchmarking 4096 u64(0) - 1024: Warming up for 3.0000 s | |
| Benchmarking 4096 u64(0) - 1024: Collecting 100 samples in estimated 5.5188 s (25k iterations) | |
| Benchmarking 4096 u64(0) - 1024: Analyzing | |
| 4096 u64(0) - 1024 time: [217.68 µs 218.87 µs 220.34 µs] | |
| change: [-4.6117% -4.2131% -3.5814%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 3 (3.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking 4096 u64(0) - 4096 | |
| Benchmarking 4096 u64(0) - 4096: Warming up for 3.0000 s | |
| Benchmarking 4096 u64(0) - 4096: Collecting 100 samples in estimated 5.4514 s (25k iterations) | |
| Benchmarking 4096 u64(0) - 4096: Analyzing | |
| 4096 u64(0) - 4096 time: [215.48 µs 216.38 µs 217.71 µs] | |
| change: [-6.8594% -5.6804% -4.1184%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 14 outliers among 100 measurements (14.00%) | |
| 1 (1.00%) low mild | |
| 5 (5.00%) high mild | |
| 8 (8.00%) high severe | |
| Benchmarking 4096 i64_small(0) - 128 | |
| Benchmarking 4096 i64_small(0) - 128: Warming up for 3.0000 s | |
| Benchmarking 4096 i64_small(0) - 128: Collecting 100 samples in estimated 5.6476 s (40k iterations) | |
| Benchmarking 4096 i64_small(0) - 128: Analyzing | |
| 4096 i64_small(0) - 128 time: [139.56 µs 139.88 µs 140.19 µs] | |
| change: [-16.642% -16.513% -16.343%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 1 (1.00%) low severe | |
| 2 (2.00%) low mild | |
| 1 (1.00%) high severe | |
| Benchmarking 4096 i64_small(0) - 1024 | |
| Benchmarking 4096 i64_small(0) - 1024: Warming up for 3.0000 s | |
| Benchmarking 4096 i64_small(0) - 1024: Collecting 100 samples in estimated 5.5196 s (45k iterations) | |
| Benchmarking 4096 i64_small(0) - 1024: Analyzing | |
| 4096 i64_small(0) - 1024 | |
| time: [120.40 µs 120.84 µs 121.46 µs] | |
| change: [-19.298% -19.144% -18.958%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 1 (1.00%) low severe | |
| 2 (2.00%) low mild | |
| 1 (1.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking 4096 i64_small(0) - 4096 | |
| Benchmarking 4096 i64_small(0) - 4096: Warming up for 3.0000 s | |
| Benchmarking 4096 i64_small(0) - 4096: Collecting 100 samples in estimated 5.3857 s (45k iterations) | |
| Benchmarking 4096 i64_small(0) - 4096: Analyzing | |
| 4096 i64_small(0) - 4096 | |
| time: [117.01 µs 117.42 µs 117.97 µs] | |
| change: [-20.869% -20.407% -19.819%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 1 (1.00%) low mild | |
| 1 (1.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking 4096 i64(0) - 128 | |
| Benchmarking 4096 i64(0) - 128: Warming up for 3.0000 s | |
| Benchmarking 4096 i64(0) - 128: Collecting 100 samples in estimated 6.1970 s (25k iterations) | |
| Benchmarking 4096 i64(0) - 128: Analyzing | |
| 4096 i64(0) - 128 time: [243.97 µs 244.57 µs 245.39 µs] | |
| change: [-4.4751% -4.1236% -3.6484%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 5 (5.00%) high severe | |
| Benchmarking 4096 i64(0) - 1024 | |
| Benchmarking 4096 i64(0) - 1024: Warming up for 3.0000 s | |
| Benchmarking 4096 i64(0) - 1024: Collecting 100 samples in estimated 5.6399 s (25k iterations) | |
| Benchmarking 4096 i64(0) - 1024: Analyzing | |
| 4096 i64(0) - 1024 time: [223.71 µs 223.79 µs 223.87 µs] | |
| change: [-4.9539% -4.9226% -4.8883%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking 4096 i64(0) - 4096 | |
| Benchmarking 4096 i64(0) - 4096: Warming up for 3.0000 s | |
| Benchmarking 4096 i64(0) - 4096: Collecting 100 samples in estimated 5.6291 s (25k iterations) | |
| Benchmarking 4096 i64(0) - 4096: Analyzing | |
| 4096 i64(0) - 4096 time: [223.06 µs 223.10 µs 223.14 µs] | |
| change: [-4.2353% -4.2114% -4.1874%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 1 (1.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking 4096 f32_small(0) - 128 | |
| Benchmarking 4096 f32_small(0) - 128: Warming up for 3.0000 s | |
| Benchmarking 4096 f32_small(0) - 128: Collecting 100 samples in estimated 5.5776 s (35k iterations) | |
| Benchmarking 4096 f32_small(0) - 128: Analyzing | |
| 4096 f32_small(0) - 128 time: [158.70 µs 158.74 µs 158.77 µs] | |
| change: [-13.112% -13.085% -13.058%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 4 (4.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking 4096 f32_small(0) - 1024 | |
| Benchmarking 4096 f32_small(0) - 1024: Warming up for 3.0000 s | |
| Benchmarking 4096 f32_small(0) - 1024: Collecting 100 samples in estimated 5.7085 s (40k iterations) | |
| Benchmarking 4096 f32_small(0) - 1024: Analyzing | |
| 4096 f32_small(0) - 1024 | |
| time: [142.50 µs 142.55 µs 142.60 µs] | |
| change: [-14.505% -14.136% -13.375%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) high severe | |
| Benchmarking 4096 f32_small(0) - 4096 | |
| Benchmarking 4096 f32_small(0) - 4096: Warming up for 3.0000 s | |
| Benchmarking 4096 f32_small(0) - 4096: Collecting 100 samples in estimated 5.6212 s (40k iterations) | |
| Benchmarking 4096 f32_small(0) - 4096: Analyzing | |
| 4096 f32_small(0) - 4096 | |
| time: [139.82 µs 139.89 µs 139.94 µs] | |
| change: [-15.332% -15.306% -15.281%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) low mild | |
| Benchmarking 4096 f32(0) - 128 | |
| Benchmarking 4096 f32(0) - 128: Warming up for 3.0000 s | |
| Benchmarking 4096 f32(0) - 128: Collecting 100 samples in estimated 5.7818 s (30k iterations) | |
| Benchmarking 4096 f32(0) - 128: Analyzing | |
| 4096 f32(0) - 128 time: [192.73 µs 192.80 µs 192.87 µs] | |
| change: [-13.160% -13.102% -13.047%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 9 (9.00%) low mild | |
| 1 (1.00%) high severe | |
| Benchmarking 4096 f32(0) - 1024 | |
| Benchmarking 4096 f32(0) - 1024: Warming up for 3.0000 s | |
| Benchmarking 4096 f32(0) - 1024: Collecting 100 samples in estimated 5.2392 s (30k iterations) | |
| Benchmarking 4096 f32(0) - 1024: Analyzing | |
| 4096 f32(0) - 1024 time: [174.31 µs 174.39 µs 174.45 µs] | |
| change: [-13.911% -13.882% -13.858%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 4 (4.00%) low severe | |
| 1 (1.00%) low mild | |
| 2 (2.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking 4096 f32(0) - 4096 | |
| Benchmarking 4096 f32(0) - 4096: Warming up for 3.0000 s | |
| Benchmarking 4096 f32(0) - 4096: Collecting 100 samples in estimated 5.2185 s (30k iterations) | |
| Benchmarking 4096 f32(0) - 4096: Analyzing | |
| 4096 f32(0) - 4096 time: [172.26 µs 172.37 µs 172.48 µs] | |
| change: [-14.207% -14.164% -14.122%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 19 outliers among 100 measurements (19.00%) | |
| 17 (17.00%) low mild | |
| 2 (2.00%) high mild | |
| Benchmarking 4096 f64_small(0) - 128 | |
| Benchmarking 4096 f64_small(0) - 128: Warming up for 3.0000 s | |
| Benchmarking 4096 f64_small(0) - 128: Collecting 100 samples in estimated 5.6355 s (35k iterations) | |
| Benchmarking 4096 f64_small(0) - 128: Analyzing | |
| 4096 f64_small(0) - 128 time: [159.37 µs 159.41 µs 159.47 µs] | |
| change: [-15.675% -15.644% -15.612%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high mild | |
| Benchmarking 4096 f64_small(0) - 1024 | |
| Benchmarking 4096 f64_small(0) - 1024: Warming up for 3.0000 s | |
| Benchmarking 4096 f64_small(0) - 1024: Collecting 100 samples in estimated 5.0066 s (35k iterations) | |
| Benchmarking 4096 f64_small(0) - 1024: Analyzing | |
| 4096 f64_small(0) - 1024 | |
| time: [141.72 µs 141.79 µs 141.86 µs] | |
| change: [-16.014% -15.985% -15.959%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 10 (10.00%) high mild | |
| Benchmarking 4096 f64_small(0) - 4096 | |
| Benchmarking 4096 f64_small(0) - 4096: Warming up for 3.0000 s | |
| Benchmarking 4096 f64_small(0) - 4096: Collecting 100 samples in estimated 5.6364 s (40k iterations) | |
| Benchmarking 4096 f64_small(0) - 4096: Analyzing | |
| 4096 f64_small(0) - 4096 | |
| time: [139.44 µs 139.46 µs 139.49 µs] | |
| change: [-16.512% -16.485% -16.459%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high mild | |
| Benchmarking 4096 f64(0) - 128 | |
| Benchmarking 4096 f64(0) - 128: Warming up for 3.0000 s | |
| Benchmarking 4096 f64(0) - 128: Collecting 100 samples in estimated 5.4846 s (25k iterations) | |
| Benchmarking 4096 f64(0) - 128: Analyzing | |
| 4096 f64(0) - 128 time: [216.59 µs 216.66 µs 216.73 µs] | |
| change: [-12.505% -12.466% -12.423%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 1 (1.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking 4096 f64(0) - 1024 | |
| Benchmarking 4096 f64(0) - 1024: Warming up for 3.0000 s | |
| Benchmarking 4096 f64(0) - 1024: Collecting 100 samples in estimated 5.9892 s (30k iterations) | |
| Benchmarking 4096 f64(0) - 1024: Analyzing | |
| 4096 f64(0) - 1024 time: [197.64 µs 197.93 µs 198.21 µs] | |
| change: [-12.914% -12.861% -12.798%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 16 outliers among 100 measurements (16.00%) | |
| 1 (1.00%) high mild | |
| 15 (15.00%) high severe | |
| Benchmarking 4096 f64(0) - 4096 | |
| Benchmarking 4096 f64(0) - 4096: Warming up for 3.0000 s | |
| Benchmarking 4096 f64(0) - 4096: Collecting 100 samples in estimated 5.0168 s (25k iterations) | |
| Benchmarking 4096 f64(0) - 4096: Analyzing | |
| 4096 f64(0) - 4096 time: [199.36 µs 199.56 µs 199.73 µs] | |
| change: [-12.703% -12.153% -11.839%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking 4096 string(10, 0) - 128 | |
| Benchmarking 4096 string(10, 0) - 128: Warming up for 3.0000 s | |
| Benchmarking 4096 string(10, 0) - 128: Collecting 100 samples in estimated 5.5955 s (45k iterations) | |
| Benchmarking 4096 string(10, 0) - 128: Analyzing | |
| 4096 string(10, 0) - 128 | |
| time: [124.04 µs 124.06 µs 124.09 µs] | |
| change: [+3.2456% +3.8262% +4.1861%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high severe | |
| Benchmarking 4096 string(10, 0) - 1024 | |
| Benchmarking 4096 string(10, 0) - 1024: Warming up for 3.0000 s | |
| Benchmarking 4096 string(10, 0) - 1024: Collecting 100 samples in estimated 5.0010 s (45k iterations) | |
| Benchmarking 4096 string(10, 0) - 1024: Analyzing | |
| 4096 string(10, 0) - 1024 | |
| time: [110.84 µs 110.86 µs 110.88 µs] | |
| change: [+4.5205% +4.5412% +4.5634%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 8 (8.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking 4096 string(10, 0) - 4096 | |
| Benchmarking 4096 string(10, 0) - 4096: Warming up for 3.0000 s | |
| Benchmarking 4096 string(10, 0) - 4096: Collecting 100 samples in estimated 5.4910 s (50k iterations) | |
| Benchmarking 4096 string(10, 0) - 4096: Analyzing | |
| 4096 string(10, 0) - 4096 | |
| time: [109.54 µs 109.56 µs 109.59 µs] | |
| change: [+4.3718% +4.9496% +5.4029%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 4 (4.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking 4096 string(30, 0) - 128 | |
| Benchmarking 4096 string(30, 0) - 128: Warming up for 3.0000 s | |
| Benchmarking 4096 string(30, 0) - 128: Collecting 100 samples in estimated 5.6453 s (30k iterations) | |
| Benchmarking 4096 string(30, 0) - 128: Analyzing | |
| 4096 string(30, 0) - 128 | |
| time: [187.04 µs 187.07 µs 187.09 µs] | |
| change: [+10.893% +11.262% +11.466%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 6 (6.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking 4096 string(30, 0) - 1024 | |
| Benchmarking 4096 string(30, 0) - 1024: Warming up for 3.0000 s | |
| Benchmarking 4096 string(30, 0) - 1024: Collecting 100 samples in estimated 5.2251 s (30k iterations) | |
| Benchmarking 4096 string(30, 0) - 1024: Analyzing | |
| 4096 string(30, 0) - 1024 | |
| time: [173.12 µs 173.40 µs 173.97 µs] | |
| change: [+14.834% +15.014% +15.189%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 4 (4.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking 4096 string(30, 0) - 4096 | |
| Benchmarking 4096 string(30, 0) - 4096: Warming up for 3.0000 s | |
| Benchmarking 4096 string(30, 0) - 4096: Collecting 100 samples in estimated 5.1833 s (30k iterations) | |
| Benchmarking 4096 string(30, 0) - 4096: Analyzing | |
| 4096 string(30, 0) - 4096 | |
| time: [171.80 µs 171.84 µs 171.89 µs] | |
| change: [+13.052% +13.399% +13.591%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 3 (3.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking 4096 string(100, 0) - 128 | |
| Benchmarking 4096 string(100, 0) - 128: Warming up for 3.0000 s | |
| Benchmarking 4096 string(100, 0) - 128: Collecting 100 samples in estimated 6.7192 s (15k iterations) | |
| Benchmarking 4096 string(100, 0) - 128: Analyzing | |
| 4096 string(100, 0) - 128 | |
| time: [444.57 µs 444.63 µs 444.69 µs] | |
| change: [+16.027% +16.078% +16.119%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 5 (5.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking 4096 string(100, 0) - 1024 | |
| Benchmarking 4096 string(100, 0) - 1024: Warming up for 3.0000 s | |
| Benchmarking 4096 string(100, 0) - 1024: Collecting 100 samples in estimated 6.4638 s (15k iterations) | |
| Benchmarking 4096 string(100, 0) - 1024: Analyzing | |
| 4096 string(100, 0) - 1024 | |
| time: [427.72 µs 427.78 µs 427.84 µs] | |
| change: [+16.623% +16.749% +16.827%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 4 (4.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking 4096 string(100, 0) - 4096 | |
| Benchmarking 4096 string(100, 0) - 4096: Warming up for 3.0000 s | |
| Benchmarking 4096 string(100, 0) - 4096: Collecting 100 samples in estimated 6.5790 s (15k iterations) | |
| Benchmarking 4096 string(100, 0) - 4096: Analyzing | |
| 4096 string(100, 0) - 4096 | |
| time: [435.23 µs 435.50 µs 436.03 µs] | |
| change: [+16.407% +16.633% +16.882%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 1 (1.00%) low mild | |
| 4 (4.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking 4096 string(100, 0.5) - 128 | |
| Benchmarking 4096 string(100, 0.5) - 128: Warming up for 3.0000 s | |
| Benchmarking 4096 string(100, 0.5) - 128: Collecting 100 samples in estimated 6.0520 s (20k iterations) | |
| Benchmarking 4096 string(100, 0.5) - 128: Analyzing | |
| 4096 string(100, 0.5) - 128 | |
| time: [300.97 µs 301.04 µs 301.12 µs] | |
| change: [+8.6453% +8.6770% +8.7107%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 3 (3.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking 4096 string(100, 0.5) - 1024 | |
| Benchmarking 4096 string(100, 0.5) - 1024: Warming up for 3.0000 s | |
| Benchmarking 4096 string(100, 0.5) - 1024: Collecting 100 samples in estimated 5.6818 s (20k iterations) | |
| Benchmarking 4096 string(100, 0.5) - 1024: Analyzing | |
| 4096 string(100, 0.5) - 1024 | |
| time: [281.11 µs 281.20 µs 281.30 µs] | |
| change: [+8.3267% +8.4042% +8.4697%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) high mild | |
| Benchmarking 4096 string(100, 0.5) - 4096 | |
| Benchmarking 4096 string(100, 0.5) - 4096: Warming up for 3.0000 s | |
| Benchmarking 4096 string(100, 0.5) - 4096: Collecting 100 samples in estimated 5.6894 s (20k iterations) | |
| Benchmarking 4096 string(100, 0.5) - 4096: Analyzing | |
| 4096 string(100, 0.5) - 4096 | |
| time: [285.36 µs 285.68 µs 285.96 µs] | |
| change: [+9.5463% +9.7219% +9.8988%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Benchmarking 4096 StringView(10, 0) - 128 | |
| Benchmarking 4096 StringView(10, 0) - 128: Warming up for 3.0000 s | |
| Benchmarking 4096 StringView(10, 0) - 128: Collecting 100 samples in estimated 5.4184 s (40k iterations) | |
| Benchmarking 4096 StringView(10, 0) - 128: Analyzing | |
| 4096 StringView(10, 0) - 128 | |
| time: [133.76 µs 133.78 µs 133.80 µs] | |
| change: [+2.5098% +2.5400% +2.5669%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 6 (6.00%) high mild | |
| Benchmarking 4096 StringView(10, 0) - 1024 | |
| Benchmarking 4096 StringView(10, 0) - 1024: Warming up for 3.0000 s | |
| Benchmarking 4096 StringView(10, 0) - 1024: Collecting 100 samples in estimated 5.0207 s (40k iterations) | |
| Benchmarking 4096 StringView(10, 0) - 1024: Analyzing | |
| 4096 StringView(10, 0) - 1024 | |
| time: [122.93 µs 122.95 µs 122.97 µs] | |
| change: [+2.8347% +2.8670% +2.8963%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 2 (2.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking 4096 StringView(10, 0) - 4096 | |
| Benchmarking 4096 StringView(10, 0) - 4096: Warming up for 3.0000 s | |
| Benchmarking 4096 StringView(10, 0) - 4096: Collecting 100 samples in estimated 5.0145 s (40k iterations) | |
| Benchmarking 4096 StringView(10, 0) - 4096: Analyzing | |
| 4096 StringView(10, 0) - 4096 | |
| time: [122.83 µs 122.85 µs 122.86 µs] | |
| change: [+4.9844% +5.0371% +5.0854%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 6 (6.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking 4096 StringView(30, 0) - 128 | |
| Benchmarking 4096 StringView(30, 0) - 128: Warming up for 3.0000 s | |
| Benchmarking 4096 StringView(30, 0) - 128: Collecting 100 samples in estimated 5.5020 s (30k iterations) | |
| Benchmarking 4096 StringView(30, 0) - 128: Analyzing | |
| 4096 StringView(30, 0) - 128 | |
| time: [180.37 µs 180.41 µs 180.44 µs] | |
| change: [+10.306% +10.347% +10.385%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 1 (1.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking 4096 StringView(30, 0) - 1024 | |
| Benchmarking 4096 StringView(30, 0) - 1024: Warming up for 3.0000 s | |
| Benchmarking 4096 StringView(30, 0) - 1024: Collecting 100 samples in estimated 5.1828 s (30k iterations) | |
| Benchmarking 4096 StringView(30, 0) - 1024: Analyzing | |
| 4096 StringView(30, 0) - 1024 | |
| time: [170.09 µs 170.12 µs 170.14 µs] | |
| change: [+10.450% +10.489% +10.530%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 7 (7.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking 4096 StringView(30, 0) - 4096 | |
| Benchmarking 4096 StringView(30, 0) - 4096: Warming up for 3.0000 s | |
| Benchmarking 4096 StringView(30, 0) - 4096: Collecting 100 samples in estimated 5.1785 s (30k iterations) | |
| Benchmarking 4096 StringView(30, 0) - 4096: Analyzing | |
| 4096 StringView(30, 0) - 4096 | |
| time: [169.72 µs 169.74 µs 169.77 µs] | |
| change: [+12.361% +12.398% +12.429%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 14 outliers among 100 measurements (14.00%) | |
| 10 (10.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking 4096 StringView(100, 0) - 128 | |
| Benchmarking 4096 StringView(100, 0) - 128: Warming up for 3.0000 s | |
| Benchmarking 4096 StringView(100, 0) - 128: Collecting 100 samples in estimated 6.5567 s (15k iterations) | |
| Benchmarking 4096 StringView(100, 0) - 128: Analyzing | |
| 4096 StringView(100, 0) - 128 | |
| time: [431.63 µs 431.69 µs 431.75 µs] | |
| change: [+19.089% +19.115% +19.140%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 5 (5.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking 4096 StringView(100, 0) - 1024 | |
| Benchmarking 4096 StringView(100, 0) - 1024: Warming up for 3.0000 s | |
| Benchmarking 4096 StringView(100, 0) - 1024: Collecting 100 samples in estimated 6.3839 s (15k iterations) | |
| Benchmarking 4096 StringView(100, 0) - 1024: Analyzing | |
| 4096 StringView(100, 0) - 1024 | |
| time: [420.67 µs 421.07 µs 421.90 µs] | |
| change: [+17.684% +17.782% +17.942%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 1 (1.00%) low mild | |
| 4 (4.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking 4096 StringView(100, 0) - 4096 | |
| Benchmarking 4096 StringView(100, 0) - 4096: Warming up for 3.0000 s | |
| Benchmarking 4096 StringView(100, 0) - 4096: Collecting 100 samples in estimated 6.4346 s (15k iterations) | |
| Benchmarking 4096 StringView(100, 0) - 4096: Analyzing | |
| 4096 StringView(100, 0) - 4096 | |
| time: [424.05 µs 424.10 µs 424.16 µs] | |
| change: [+16.770% +16.812% +16.846%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 6 (6.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking 4096 StringView(100, 0.5) - 128 | |
| Benchmarking 4096 StringView(100, 0.5) - 128: Warming up for 3.0000 s | |
| Benchmarking 4096 StringView(100, 0.5) - 128: Collecting 100 samples in estimated 5.8601 s (20k iterations) | |
| Benchmarking 4096 StringView(100, 0.5) - 128: Analyzing | |
| 4096 StringView(100, 0.5) - 128 | |
| time: [289.59 µs 289.71 µs 289.83 µs] | |
| change: [+8.9409% +9.0151% +9.0846%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 1 (1.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking 4096 StringView(100, 0.5) - 1024 | |
| Benchmarking 4096 StringView(100, 0.5) - 1024: Warming up for 3.0000 s | |
| Benchmarking 4096 StringView(100, 0.5) - 1024: Collecting 100 samples in estimated 5.6149 s (20k iterations) | |
| Benchmarking 4096 StringView(100, 0.5) - 1024: Analyzing | |
| 4096 StringView(100, 0.5) - 1024 | |
| time: [276.45 µs 277.01 µs 277.91 µs] | |
| change: [+8.3721% +8.8587% +9.5762%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 19 outliers among 100 measurements (19.00%) | |
| 7 (7.00%) low severe | |
| 4 (4.00%) low mild | |
| 4 (4.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking 4096 StringView(100, 0.5) - 4096 | |
| Benchmarking 4096 StringView(100, 0.5) - 4096: Warming up for 3.0000 s | |
| Benchmarking 4096 StringView(100, 0.5) - 4096: Collecting 100 samples in estimated 5.5654 s (20k iterations) | |
| Benchmarking 4096 StringView(100, 0.5) - 4096: Analyzing | |
| 4096 StringView(100, 0.5) - 4096 | |
| time: [272.36 µs 272.59 µs 272.83 µs] | |
| change: [+6.0795% +6.2136% +6.3386%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 15 outliers among 100 measurements (15.00%) | |
| 5 (5.00%) low mild | |
| 8 (8.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking 4096 string(20, 0.5), string(30, 0), string(100, 0), i64(0) - 128 | |
| Benchmarking 4096 string(20, 0.5), string(30, 0), string(100, 0), i64(0) - 128: Warming up for 3.0000 s | |
| Benchmarking 4096 string(20, 0.5), string(30, 0), string(100, 0), i64(0) - 128: Collecting 100 samples in estimated 9.3706 s (10k iterations) | |
| Benchmarking 4096 string(20, 0.5), string(30, 0), string(100, 0), i64(0) - 128: Analyzing | |
| 4096 string(20, 0.5), string(30, 0), string(100, 0), i64(0) - 128 | |
| time: [951.23 µs 953.61 µs 955.46 µs] | |
| change: [+9.8939% +10.427% +10.966%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Benchmarking 4096 string(20, 0.5), string(30, 0), string(100, 0), i64(0) - 1024 | |
| Benchmarking 4096 string(20, 0.5), string(30, 0), string(100, 0), i64(0) - 1024: Warming up for 3.0000 s | |
| Benchmarking 4096 string(20, 0.5), string(30, 0), string(100, 0), i64(0) - 1024: Collecting 100 samples in estimated 8.6871 s (10k iterations) | |
| Benchmarking 4096 string(20, 0.5), string(30, 0), string(100, 0), i64(0) - 1024: Analyzing | |
| 4096 string(20, 0.5), string(30, 0), string(100, 0), i64(0) - 1024 | |
| time: [863.62 µs 864.46 µs 865.88 µs] | |
| change: [+10.137% +10.225% +10.354%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 1 (1.00%) low mild | |
| 1 (1.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking 4096 string(20, 0.5), string(30, 0), string(100, 0), i64(0) - 4096 | |
| Benchmarking 4096 string(20, 0.5), string(30, 0), string(100, 0), i64(0) - 4096: Warming up for 3.0000 s | |
| Benchmarking 4096 string(20, 0.5), string(30, 0), string(100, 0), i64(0) - 4096: Collecting 100 samples in estimated 8.8162 s (10k iterations) | |
| Benchmarking 4096 string(20, 0.5), string(30, 0), string(100, 0), i64(0) - 4096: Analyzing | |
| 4096 string(20, 0.5), string(30, 0), string(100, 0), i64(0) - 4096 | |
| time: [876.08 µs 876.26 µs 876.47 µs] | |
| change: [+10.562% +10.597% +10.627%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 1 (1.00%) low mild | |
| 5 (5.00%) high mild | |
| Benchmarking 4096 string(20, 0.5), string(30, 0), f64(0), i64(0) - 128 | |
| Benchmarking 4096 string(20, 0.5), string(30, 0), f64(0), i64(0) - 128: Warming up for 3.0000 s | |
| Benchmarking 4096 string(20, 0.5), string(30, 0), f64(0), i64(0) - 128: Collecting 100 samples in estimated 7.1355 s (10k iterations) | |
| Benchmarking 4096 string(20, 0.5), string(30, 0), f64(0), i64(0) - 128: Analyzing | |
| 4096 string(20, 0.5), string(30, 0), f64(0), i64(0) - 128 | |
| time: [738.38 µs 745.34 µs 751.19 µs] | |
| change: [+2.9301% +3.6833% +4.3749%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Benchmarking 4096 string(20, 0.5), string(30, 0), f64(0), i64(0) - 1024 | |
| Benchmarking 4096 string(20, 0.5), string(30, 0), f64(0), i64(0) - 1024: Warming up for 3.0000 s | |
| Benchmarking 4096 string(20, 0.5), string(30, 0), f64(0), i64(0) - 1024: Collecting 100 samples in estimated 6.3808 s (10k iterations) | |
| Benchmarking 4096 string(20, 0.5), string(30, 0), f64(0), i64(0) - 1024: Analyzing | |
| 4096 string(20, 0.5), string(30, 0), f64(0), i64(0) - 1024 | |
| time: [649.05 µs 651.41 µs 653.23 µs] | |
| change: [-0.2689% +0.4153% +1.0250%] (p = 0.22 > 0.05) | |
| No change in performance detected. | |
| Benchmarking 4096 string(20, 0.5), string(30, 0), f64(0), i64(0) - 4096 | |
| Benchmarking 4096 string(20, 0.5), string(30, 0), f64(0), i64(0) - 4096: Warming up for 3.0000 s | |
| Benchmarking 4096 string(20, 0.5), string(30, 0), f64(0), i64(0) - 4096: Collecting 100 samples in estimated 6.3150 s (10k iterations) | |
| Benchmarking 4096 string(20, 0.5), string(30, 0), f64(0), i64(0) - 4096: Analyzing | |
| 4096 string(20, 0.5), string(30, 0), f64(0), i64(0) - 4096 | |
| time: [627.81 µs 628.06 µs 628.27 µs] | |
| change: [-1.0177% -0.4612% -0.0451%] (p = 0.05 > 0.05) | |
| No change in performance detected. | |
| Benchmarking 4096 StringView(20, 0.5), StringView(30, 0), StringView(100, 0), i64(0) - 128 | |
| Benchmarking 4096 StringView(20, 0.5), StringView(30, 0), StringView(100, 0), i64(0) - 128: Warming up for 3.0000 s | |
| Benchmarking 4096 StringView(20, 0.5), StringView(30, 0), StringView(100, 0), i64(0) - 128: Collecting 100 samples in estimated 9.1039 s (10k iterations) | |
| Benchmarking 4096 StringView(20, 0.5), StringView(30, 0), StringView(100, 0), i64(0) - 128: Analyzing | |
| 4096 StringView(20, 0.5), StringView(30, 0), StringView(100, 0), i64(0) - 128 | |
| time: [899.85 µs 900.05 µs 900.28 µs] | |
| change: [+8.9703% +9.4127% +9.8264%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 3 (3.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking 4096 StringView(20, 0.5), StringView(30, 0), StringView(100, 0), i64(0) - 1024 | |
| Benchmarking 4096 StringView(20, 0.5), StringView(30, 0), StringView(100, 0), i64(0) - 1024: Warming up for 3.0000 s | |
| Benchmarking 4096 StringView(20, 0.5), StringView(30, 0), StringView(100, 0), i64(0) - 1024: Collecting 100 samples in estimated 8.6584 s (10k iterations) | |
| Benchmarking 4096 StringView(20, 0.5), StringView(30, 0), StringView(100, 0), i64(0) - 1024: Analyzing | |
| 4096 StringView(20, 0.5), StringView(30, 0), StringView(100, 0), i64(0) - 1024 | |
| time: [869.83 µs 873.59 µs 876.78 µs] | |
| change: [+11.414% +11.654% +11.994%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 26 outliers among 100 measurements (26.00%) | |
| 2 (2.00%) low mild | |
| 24 (24.00%) high severe | |
| Benchmarking 4096 StringView(20, 0.5), StringView(30, 0), StringView(100, 0), i64(0) - 4096 | |
| Benchmarking 4096 StringView(20, 0.5), StringView(30, 0), StringView(100, 0), i64(0) - 4096: Warming up for 3.0000 s | |
| Benchmarking 4096 StringView(20, 0.5), StringView(30, 0), StringView(100, 0), i64(0) - 4096: Collecting 100 samples in estimated 8.6315 s (10k iterations) | |
| Benchmarking 4096 StringView(20, 0.5), StringView(30, 0), StringView(100, 0), i64(0) - 4096: Analyzing | |
| 4096 StringView(20, 0.5), StringView(30, 0), StringView(100, 0), i64(0) - 4096 | |
| time: [859.20 µs 863.40 µs 867.32 µs] | |
| change: [+8.3097% +8.5449% +8.7879%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 16 outliers among 100 measurements (16.00%) | |
| 2 (2.00%) high mild | |
| 14 (14.00%) high severe | |
| Benchmarking 4096 StringView(20, 0.5), StringView(30, 0), f64(0), i64(0) - 128 | |
| Benchmarking 4096 StringView(20, 0.5), StringView(30, 0), f64(0), i64(0) - 128: Warming up for 3.0000 s | |
| Benchmarking 4096 StringView(20, 0.5), StringView(30, 0), f64(0), i64(0) - 128: Collecting 100 samples in estimated 6.9860 s (10k iterations) | |
| Benchmarking 4096 StringView(20, 0.5), StringView(30, 0), f64(0), i64(0) - 128: Analyzing | |
| 4096 StringView(20, 0.5), StringView(30, 0), f64(0), i64(0) - 128 | |
| time: [687.42 µs 687.72 µs 688.03 µs] | |
| change: [-0.9985% -0.6821% -0.4393%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 3 (3.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking 4096 StringView(20, 0.5), StringView(30, 0), f64(0), i64(0) - 1024 | |
| Benchmarking 4096 StringView(20, 0.5), StringView(30, 0), f64(0), i64(0) - 1024: Warming up for 3.0000 s | |
| Benchmarking 4096 StringView(20, 0.5), StringView(30, 0), f64(0), i64(0) - 1024: Collecting 100 samples in estimated 6.4048 s (10k iterations) | |
| Benchmarking 4096 StringView(20, 0.5), StringView(30, 0), f64(0), i64(0) - 1024: Analyzing | |
| 4096 StringView(20, 0.5), StringView(30, 0), f64(0), i64(0) - 1024 | |
| time: [632.38 µs 632.59 µs 632.81 µs] | |
| change: [+0.0293% +0.0801% +0.1333%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 1 (1.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking 4096 StringView(20, 0.5), StringView(30, 0), f64(0), i64(0) - 4096 | |
| Benchmarking 4096 StringView(20, 0.5), StringView(30, 0), f64(0), i64(0) - 4096: Warming up for 3.0000 s | |
| Benchmarking 4096 StringView(20, 0.5), StringView(30, 0), f64(0), i64(0) - 4096: Collecting 100 samples in estimated 6.3998 s (10k iterations) | |
| Benchmarking 4096 StringView(20, 0.5), StringView(30, 0), f64(0), i64(0) - 4096: Analyzing | |
| 4096 StringView(20, 0.5), StringView(30, 0), f64(0), i64(0) - 4096 | |
| time: [632.72 µs 633.32 µs 634.40 µs] | |
| change: [-0.7725% -0.1744% +0.3417%] (p = 0.56 > 0.05) | |
| No change in performance detected. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high severe | |
| Running benches/csv_writer.rs (target/x86_64-unknown-linux-gnu/release/deps/csv_writer-0ab6d52e0a9516ff) | |
| Benchmarking record_batches_to_csv | |
| Benchmarking record_batches_to_csv: Warming up for 3.0000 s | |
| Benchmarking record_batches_to_csv: Collecting 100 samples in estimated 5.0065 s (273k iterations) | |
| Benchmarking record_batches_to_csv: Analyzing | |
| record_batches_to_csv time: [18.136 µs 18.142 µs 18.150 µs] | |
| change: [+5.7450% +5.8320% +5.9200%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 3 (3.00%) high mild | |
| 2 (2.00%) high severe | |
| Running benches/decimal_validate.rs (target/x86_64-unknown-linux-gnu/release/deps/decimal_validate-75df1df04791ee90) | |
| Benchmarking validate_decimal128_array 20000 | |
| Benchmarking validate_decimal128_array 20000: Warming up for 3.0000 s | |
| Benchmarking validate_decimal128_array 20000: Collecting 100 samples in estimated 5.0001 s (90M iterations) | |
| Benchmarking validate_decimal128_array 20000: Analyzing | |
| validate_decimal128_array 20000 | |
| time: [58.655 ns 58.719 ns 58.784 ns] | |
| change: [+3.7218% +3.8882% +4.0510%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 1 (1.00%) low mild | |
| 2 (2.00%) high mild | |
| Benchmarking validate_decimal256_array 20000 | |
| Benchmarking validate_decimal256_array 20000: Warming up for 3.0000 s | |
| Benchmarking validate_decimal256_array 20000: Collecting 100 samples in estimated 5.0001 s (89M iterations) | |
| Benchmarking validate_decimal256_array 20000: Analyzing | |
| validate_decimal256_array 20000 | |
| time: [58.926 ns 58.978 ns 59.032 ns] | |
| change: [+3.0895% +3.7163% +4.2027%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Running benches/equal.rs (target/x86_64-unknown-linux-gnu/release/deps/equal-7866e889336ed768) | |
| Benchmarking equal_512 | |
| Benchmarking equal_512: Warming up for 3.0000 s | |
| Benchmarking equal_512: Collecting 100 samples in estimated 5.0006 s (36M iterations) | |
| Benchmarking equal_512: Analyzing | |
| equal_512 time: [140.52 ns 140.63 ns 140.75 ns] | |
| change: [-11.048% -10.969% -10.886%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) high mild | |
| Benchmarking equal_nulls_512 | |
| Benchmarking equal_nulls_512: Warming up for 3.0000 s | |
| Benchmarking equal_nulls_512: Collecting 100 samples in estimated 5.0001 s (5.0M iterations) | |
| Benchmarking equal_nulls_512: Analyzing | |
| equal_nulls_512 time: [1.0075 µs 1.0085 µs 1.0096 µs] | |
| change: [-11.957% -11.888% -11.790%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 13 outliers among 100 measurements (13.00%) | |
| 4 (4.00%) high mild | |
| 9 (9.00%) high severe | |
| Benchmarking equal_51200 | |
| Benchmarking equal_51200: Warming up for 3.0000 s | |
| Benchmarking equal_51200: Collecting 100 samples in estimated 5.1253 s (101k iterations) | |
| Benchmarking equal_51200: Analyzing | |
| equal_51200 time: [57.771 µs 57.971 µs 58.159 µs] | |
| change: [-7.6875% -6.9601% -6.2523%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking equal_string_512 | |
| Benchmarking equal_string_512: Warming up for 3.0000 s | |
| Benchmarking equal_string_512: Collecting 100 samples in estimated 5.0026 s (4.1M iterations) | |
| Benchmarking equal_string_512: Analyzing | |
| equal_string_512 time: [1.2502 µs 1.2569 µs 1.2639 µs] | |
| change: [+0.9804% +1.6664% +2.3173%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Benchmarking equal_string_nulls_512 | |
| Benchmarking equal_string_nulls_512: Warming up for 3.0000 s | |
| Benchmarking equal_string_nulls_512: Collecting 100 samples in estimated 5.0049 s (2.0M iterations) | |
| Benchmarking equal_string_nulls_512: Analyzing | |
| equal_string_nulls_512 time: [2.5026 µs 2.5034 µs 2.5043 µs] | |
| change: [-7.2339% -7.1917% -7.1431%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 2 (2.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking equal_bool_512 | |
| Benchmarking equal_bool_512: Warming up for 3.0000 s | |
| Benchmarking equal_bool_512: Collecting 100 samples in estimated 5.0001 s (43M iterations) | |
| Benchmarking equal_bool_512: Analyzing | |
| equal_bool_512 time: [124.92 ns 125.09 ns 125.28 ns] | |
| change: [-10.378% -9.3585% -8.5628%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 3 (3.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking equal_bool_513 | |
| Benchmarking equal_bool_513: Warming up for 3.0000 s | |
| Benchmarking equal_bool_513: Collecting 100 samples in estimated 5.0002 s (39M iterations) | |
| Benchmarking equal_bool_513: Analyzing | |
| equal_bool_513 time: [134.91 ns 135.04 ns 135.17 ns] | |
| change: [-6.3527% -6.2575% -6.1528%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 3 (3.00%) high mild | |
| Running benches/filter_kernels.rs (target/x86_64-unknown-linux-gnu/release/deps/filter_kernels-8b31f79e7dc8d5f0) | |
| Benchmarking filter optimize (kept 1/2) | |
| Benchmarking filter optimize (kept 1/2): Warming up for 3.0000 s | |
| Benchmarking filter optimize (kept 1/2): Collecting 100 samples in estimated 5.1952 s (61k iterations) | |
| Benchmarking filter optimize (kept 1/2): Analyzing | |
| filter optimize (kept 1/2) | |
| time: [85.840 µs 85.981 µs 86.267 µs] | |
| change: [-10.653% -10.509% -10.320%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 6 (6.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking filter optimize high selectivity (kept 1023/1024) | |
| Benchmarking filter optimize high selectivity (kept 1023/1024): Warming up for 3.0000 s | |
| Benchmarking filter optimize high selectivity (kept 1023/1024): Collecting 100 samples in estimated 5.0021 s (2.7M iterations) | |
| Benchmarking filter optimize high selectivity (kept 1023/1024): Analyzing | |
| filter optimize high selectivity (kept 1023/1024) | |
| time: [1.8134 µs 1.8145 µs 1.8157 µs] | |
| change: [-9.9464% -9.3957% -8.9160%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking filter optimize low selectivity (kept 1/1024) | |
| Benchmarking filter optimize low selectivity (kept 1/1024): Warming up for 3.0000 s | |
| Benchmarking filter optimize low selectivity (kept 1/1024): Collecting 100 samples in estimated 5.0039 s (3.5M iterations) | |
| Benchmarking filter optimize low selectivity (kept 1/1024): Analyzing | |
| filter optimize low selectivity (kept 1/1024) | |
| time: [1.4472 µs 1.4487 µs 1.4503 µs] | |
| change: [-24.759% -24.516% -24.332%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 2 (2.00%) low severe | |
| 2 (2.00%) low mild | |
| 4 (4.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking filter u8 (kept 1/2) | |
| Benchmarking filter u8 (kept 1/2): Warming up for 3.0000 s | |
| Benchmarking filter u8 (kept 1/2): Collecting 100 samples in estimated 5.0217 s (61k iterations) | |
| Benchmarking filter u8 (kept 1/2): Analyzing | |
| filter u8 (kept 1/2) time: [86.122 µs 86.141 µs 86.162 µs] | |
| change: [+22.640% +22.665% +22.690%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 6 (6.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking filter u8 high selectivity (kept 1023/1024) | |
| Benchmarking filter u8 high selectivity (kept 1023/1024): Warming up for 3.0000 s | |
| Benchmarking filter u8 high selectivity (kept 1023/1024): Collecting 100 samples in estimated 5.0047 s (1.6M iterations) | |
| Benchmarking filter u8 high selectivity (kept 1023/1024): Analyzing | |
| filter u8 high selectivity (kept 1023/1024) | |
| time: [3.0420 µs 3.0430 µs 3.0439 µs] | |
| change: [-3.6952% -3.5571% -3.4260%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) high mild | |
| Benchmarking filter u8 low selectivity (kept 1/1024) | |
| Benchmarking filter u8 low selectivity (kept 1/1024): Warming up for 3.0000 s | |
| Benchmarking filter u8 low selectivity (kept 1/1024): Collecting 100 samples in estimated 5.0021 s (3.3M iterations) | |
| Benchmarking filter u8 low selectivity (kept 1/1024): Analyzing | |
| filter u8 low selectivity (kept 1/1024) | |
| time: [1.5064 µs 1.5070 µs 1.5076 µs] | |
| change: [-28.504% -28.080% -27.743%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high mild | |
| Benchmarking filter context u8 (kept 1/2) | |
| Benchmarking filter context u8 (kept 1/2): Warming up for 3.0000 s | |
| Benchmarking filter context u8 (kept 1/2): Collecting 100 samples in estimated 5.0027 s (404k iterations) | |
| Benchmarking filter context u8 (kept 1/2): Analyzing | |
| filter context u8 (kept 1/2) | |
| time: [12.379 µs 12.383 µs 12.387 µs] | |
| change: [-1.5048% -1.1714% -0.9301%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 3 (3.00%) high mild | |
| 9 (9.00%) high severe | |
| Benchmarking filter context u8 high selectivity (kept 1023/1024) | |
| Benchmarking filter context u8 high selectivity (kept 1023/1024): Warming up for 3.0000 s | |
| Benchmarking filter context u8 high selectivity (kept 1023/1024): Collecting 100 samples in estimated 5.0048 s (3.1M iterations) | |
| Benchmarking filter context u8 high selectivity (kept 1023/1024): Analyzing | |
| filter context u8 high selectivity (kept 1023/1024) | |
| time: [1.6041 µs 1.6044 µs 1.6047 µs] | |
| change: [+4.7723% +5.0936% +5.3275%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 13 outliers among 100 measurements (13.00%) | |
| 5 (5.00%) high mild | |
| 8 (8.00%) high severe | |
| Benchmarking filter context u8 low selectivity (kept 1/1024) | |
| Benchmarking filter context u8 low selectivity (kept 1/1024): Warming up for 3.0000 s | |
| Benchmarking filter context u8 low selectivity (kept 1/1024): Collecting 100 samples in estimated 5.0006 s (32M iterations) | |
| Benchmarking filter context u8 low selectivity (kept 1/1024): Analyzing | |
| filter context u8 low selectivity (kept 1/1024) | |
| time: [172.38 ns 172.46 ns 172.55 ns] | |
| change: [+6.1472% +6.2153% +6.2771%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) high mild | |
| Benchmarking filter i32 (kept 1/2) | |
| Benchmarking filter i32 (kept 1/2): Warming up for 3.0000 s | |
| Benchmarking filter i32 (kept 1/2): Collecting 100 samples in estimated 5.2331 s (61k iterations) | |
| Benchmarking filter i32 (kept 1/2): Analyzing | |
| filter i32 (kept 1/2) time: [86.432 µs 86.450 µs 86.470 µs] | |
| change: [-10.206% -10.174% -10.128%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 7 (7.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking filter i32 high selectivity (kept 1023/1024) | |
| Benchmarking filter i32 high selectivity (kept 1023/1024): Warming up for 3.0000 s | |
| Benchmarking filter i32 high selectivity (kept 1023/1024): Collecting 100 samples in estimated 5.0105 s (636k iterations) | |
| Benchmarking filter i32 high selectivity (kept 1023/1024): Analyzing | |
| filter i32 high selectivity (kept 1023/1024) | |
| time: [8.0188 µs 8.0314 µs 8.0414 µs] | |
| change: [+4.1564% +4.3912% +4.6083%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Benchmarking filter i32 low selectivity (kept 1/1024) | |
| Benchmarking filter i32 low selectivity (kept 1/1024): Warming up for 3.0000 s | |
| Benchmarking filter i32 low selectivity (kept 1/1024): Collecting 100 samples in estimated 5.0030 s (3.3M iterations) | |
| Benchmarking filter i32 low selectivity (kept 1/1024): Analyzing | |
| filter i32 low selectivity (kept 1/1024) | |
| time: [1.5034 µs 1.5041 µs 1.5048 µs] | |
| change: [-28.440% -28.155% -27.945%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 3 (3.00%) high mild | |
| Benchmarking filter context i32 (kept 1/2) | |
| Benchmarking filter context i32 (kept 1/2): Warming up for 3.0000 s | |
| Benchmarking filter context i32 (kept 1/2): Collecting 100 samples in estimated 5.0613 s (258k iterations) | |
| Benchmarking filter context i32 (kept 1/2): Analyzing | |
| filter context i32 (kept 1/2) | |
| time: [18.826 µs 19.033 µs 19.278 µs] | |
| change: [+48.314% +49.366% +50.460%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 32 outliers among 100 measurements (32.00%) | |
| 21 (21.00%) low severe | |
| 6 (6.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking filter context i32 high selectivity (kept 1023/1024) | |
| Benchmarking filter context i32 high selectivity (kept 1023/1024): Warming up for 3.0000 s | |
| Benchmarking filter context i32 high selectivity (kept 1023/1024): Collecting 100 samples in estimated 5.0203 s (833k iterations) | |
| Benchmarking filter context i32 high selectivity (kept 1023/1024): Analyzing | |
| filter context i32 high selectivity (kept 1023/1024) | |
| time: [6.1089 µs 6.1143 µs 6.1190 µs] | |
| change: [+5.1899% +5.3356% +5.4392%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 13 outliers among 100 measurements (13.00%) | |
| 7 (7.00%) low severe | |
| 1 (1.00%) low mild | |
| 4 (4.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking filter context i32 low selectivity (kept 1/1024) | |
| Benchmarking filter context i32 low selectivity (kept 1/1024): Warming up for 3.0000 s | |
| Benchmarking filter context i32 low selectivity (kept 1/1024): Collecting 100 samples in estimated 5.0009 s (24M iterations) | |
| Benchmarking filter context i32 low selectivity (kept 1/1024): Analyzing | |
| filter context i32 low selectivity (kept 1/1024) | |
| time: [225.51 ns 225.56 ns 225.62 ns] | |
| change: [+3.7577% +3.8041% +3.8555%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 2 (2.00%) low mild | |
| 5 (5.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking filter context i32 w NULLs (kept 1/2) | |
| Benchmarking filter context i32 w NULLs (kept 1/2): Warming up for 3.0000 s | |
| Benchmarking filter context i32 w NULLs (kept 1/2): Collecting 100 samples in estimated 5.0177 s (71k iterations) | |
| Benchmarking filter context i32 w NULLs (kept 1/2): Analyzing | |
| filter context i32 w NULLs (kept 1/2) | |
| time: [70.991 µs 71.017 µs 71.047 µs] | |
| change: [+73.500% +73.644% +73.759%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 5 (5.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking filter context i32 w NULLs high selectivity (kept 1023/1024) | |
| Benchmarking filter context i32 w NULLs high selectivity (kept 1023/1024): Warming up for 3.0000 s | |
| Benchmarking filter context i32 w NULLs high selectivity (kept 1023/1024): Collecting 100 samples in estimated 5.0151 s (500k iterations) | |
| Benchmarking filter context i32 w NULLs high selectivity (kept 1023/1024): Analyzing | |
| filter context i32 w NULLs high selectivity (kept 1023/1024) | |
| time: [10.057 µs 10.071 µs 10.097 µs] | |
| change: [-9.9982% -9.8507% -9.6754%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 2 (2.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking filter context i32 w NULLs low selectivity (kept 1/1024) | |
| Benchmarking filter context i32 w NULLs low selectivity (kept 1/1024): Warming up for 3.0000 s | |
| Benchmarking filter context i32 w NULLs low selectivity (kept 1/1024): Collecting 100 samples in estimated 5.0011 s (11M iterations) | |
| Benchmarking filter context i32 w NULLs low selectivity (kept 1/1024): Analyzing | |
| filter context i32 w NULLs low selectivity (kept 1/1024) | |
| time: [474.21 ns 474.37 ns 474.55 ns] | |
| change: [+6.3118% +6.8310% +7.1158%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 1 (1.00%) low mild | |
| 4 (4.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking filter context u8 w NULLs (kept 1/2) | |
| Benchmarking filter context u8 w NULLs (kept 1/2): Warming up for 3.0000 s | |
| Benchmarking filter context u8 w NULLs (kept 1/2): Collecting 100 samples in estimated 5.2396 s (81k iterations) | |
| Benchmarking filter context u8 w NULLs (kept 1/2): Analyzing | |
| filter context u8 w NULLs (kept 1/2) | |
| time: [64.864 µs 64.876 µs 64.891 µs] | |
| change: [+61.128% +61.249% +61.332%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 4 (4.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking filter context u8 w NULLs high selectivity (kept 1023/1024) | |
| Benchmarking filter context u8 w NULLs high selectivity (kept 1023/1024): Warming up for 3.0000 s | |
| Benchmarking filter context u8 w NULLs high selectivity (kept 1023/1024): Collecting 100 samples in estimated 5.0232 s (934k iterations) | |
| Benchmarking filter context u8 w NULLs high selectivity (kept 1023/1024): Analyzing | |
| filter context u8 w NULLs high selectivity (kept 1023/1024) | |
| time: [5.4047 µs 5.4072 µs 5.4098 µs] | |
| change: [-19.568% -19.527% -19.478%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 2 (2.00%) low mild | |
| 3 (3.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking filter context u8 w NULLs low selectivity (kept 1/1024) | |
| Benchmarking filter context u8 w NULLs low selectivity (kept 1/1024): Warming up for 3.0000 s | |
| Benchmarking filter context u8 w NULLs low selectivity (kept 1/1024): Collecting 100 samples in estimated 5.0002 s (11M iterations) | |
| Benchmarking filter context u8 w NULLs low selectivity (kept 1/1024): Analyzing | |
| filter context u8 w NULLs low selectivity (kept 1/1024) | |
| time: [472.95 ns 473.30 ns 473.90 ns] | |
| change: [+9.3178% +9.5324% +9.8865%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 1 (1.00%) low severe | |
| 2 (2.00%) low mild | |
| 3 (3.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking filter f32 (kept 1/2) | |
| Benchmarking filter f32 (kept 1/2): Warming up for 3.0000 s | |
| Benchmarking filter f32 (kept 1/2): Collecting 100 samples in estimated 5.6346 s (35k iterations) | |
| Benchmarking filter f32 (kept 1/2): Analyzing | |
| filter f32 (kept 1/2) time: [163.46 µs 166.45 µs 169.30 µs] | |
| change: [-3.2623% -2.4238% -1.5301%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 25 outliers among 100 measurements (25.00%) | |
| 15 (15.00%) low severe | |
| 2 (2.00%) high mild | |
| 8 (8.00%) high severe | |
| Benchmarking filter context f32 (kept 1/2) | |
| Benchmarking filter context f32 (kept 1/2): Warming up for 3.0000 s | |
| Benchmarking filter context f32 (kept 1/2): Collecting 100 samples in estimated 5.0147 s (71k iterations) | |
| Benchmarking filter context f32 (kept 1/2): Analyzing | |
| filter context f32 (kept 1/2) | |
| time: [70.910 µs 70.927 µs 70.949 µs] | |
| change: [+68.147% +68.252% +68.339%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 14 outliers among 100 measurements (14.00%) | |
| 7 (7.00%) high mild | |
| 7 (7.00%) high severe | |
| Benchmarking filter context f32 high selectivity (kept 1023/1024) | |
| Benchmarking filter context f32 high selectivity (kept 1023/1024): Warming up for 3.0000 s | |
| Benchmarking filter context f32 high selectivity (kept 1023/1024): Collecting 100 samples in estimated 5.0111 s (485k iterations) | |
| Benchmarking filter context f32 high selectivity (kept 1023/1024): Analyzing | |
| filter context f32 high selectivity (kept 1023/1024) | |
| time: [10.341 µs 10.345 µs 10.349 µs] | |
| change: [-6.7130% -6.5947% -6.5020%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 2 (2.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking filter context f32 low selectivity (kept 1/1024) | |
| Benchmarking filter context f32 low selectivity (kept 1/1024): Warming up for 3.0000 s | |
| Benchmarking filter context f32 low selectivity (kept 1/1024): Collecting 100 samples in estimated 5.0009 s (11M iterations) | |
| Benchmarking filter context f32 low selectivity (kept 1/1024): Analyzing | |
| filter context f32 low selectivity (kept 1/1024) | |
| time: [452.21 ns 452.33 ns 452.45 ns] | |
| change: [+2.4653% +2.5168% +2.5693%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 4 (4.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking filter decimal128 (kept 1/2) | |
| Benchmarking filter decimal128 (kept 1/2): Warming up for 3.0000 s | |
| Benchmarking filter decimal128 (kept 1/2): Collecting 100 samples in estimated 5.4426 s (61k iterations) | |
| Benchmarking filter decimal128 (kept 1/2): Analyzing | |
| filter decimal128 (kept 1/2) | |
| time: [90.006 µs 90.022 µs 90.038 µs] | |
| change: [-7.3176% -7.2737% -7.2365%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 13 outliers among 100 measurements (13.00%) | |
| 8 (8.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking filter decimal128 high selectivity (kept 1023/1024) | |
| Benchmarking filter decimal128 high selectivity (kept 1023/1024): Warming up for 3.0000 s | |
| Benchmarking filter decimal128 high selectivity (kept 1023/1024): Collecting 100 samples in estimated 5.1148 s (192k iterations) | |
| Benchmarking filter decimal128 high selectivity (kept 1023/1024): Analyzing | |
| filter decimal128 high selectivity (kept 1023/1024) | |
| time: [26.862 µs 26.869 µs 26.877 µs] | |
| change: [+1.8342% +2.2074% +2.5913%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 3 (3.00%) high mild | |
| Benchmarking filter decimal128 low selectivity (kept 1/1024) | |
| Benchmarking filter decimal128 low selectivity (kept 1/1024): Warming up for 3.0000 s | |
| Benchmarking filter decimal128 low selectivity (kept 1/1024): Collecting 100 samples in estimated 5.0046 s (3.1M iterations) | |
| Benchmarking filter decimal128 low selectivity (kept 1/1024): Analyzing | |
| filter decimal128 low selectivity (kept 1/1024) | |
| time: [1.5823 µs 1.5857 µs 1.5910 µs] | |
| change: [-24.482% -24.264% -23.924%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 2 (2.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking filter context decimal128 (kept 1/2) | |
| Benchmarking filter context decimal128 (kept 1/2): Warming up for 3.0000 s | |
| Benchmarking filter context decimal128 (kept 1/2): Collecting 100 samples in estimated 5.0508 s (258k iterations) | |
| Benchmarking filter context decimal128 (kept 1/2): Analyzing | |
| filter context decimal128 (kept 1/2) | |
| time: [19.501 µs 19.599 µs 19.732 µs] | |
| change: [-7.6254% -6.5399% -4.8366%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 3 (3.00%) high mild | |
| 8 (8.00%) high severe | |
| Benchmarking filter context decimal128 high selectivity (kept 1023/1024) | |
| Benchmarking filter context decimal128 high selectivity (kept 1023/1024): Warming up for 3.0000 s | |
| Benchmarking filter context decimal128 high selectivity (kept 1023/1024): Collecting 100 samples in estimated 5.1143 s (212k iterations) | |
| Benchmarking filter context decimal128 high selectivity (kept 1023/1024): Analyzing | |
| filter context decimal128 high selectivity (kept 1023/1024) | |
| time: [24.714 µs 24.720 µs 24.727 µs] | |
| change: [+0.1053% +0.1840% +0.2489%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 1 (1.00%) low mild | |
| 3 (3.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking filter context decimal128 low selectivity (kept 1/1024) | |
| Benchmarking filter context decimal128 low selectivity (kept 1/1024): Warming up for 3.0000 s | |
| Benchmarking filter context decimal128 low selectivity (kept 1/1024): Collecting 100 samples in estimated 5.0011 s (21M iterations) | |
| Benchmarking filter context decimal128 low selectivity (kept 1/1024): Analyzing | |
| filter context decimal128 low selectivity (kept 1/1024) | |
| time: [243.10 ns 243.15 ns 243.20 ns] | |
| change: [+39.485% +39.672% +39.788%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Benchmarking filter context string (kept 1/2) | |
| Benchmarking filter context string (kept 1/2): Warming up for 3.0000 s | |
| Benchmarking filter context string (kept 1/2): Collecting 100 samples in estimated 7.3457 s (15k iterations) | |
| Benchmarking filter context string (kept 1/2): Analyzing | |
| filter context string (kept 1/2) | |
| time: [484.45 µs 484.54 µs 484.64 µs] | |
| change: [+1.0498% +1.1840% +1.2888%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 4 (4.00%) low severe | |
| 6 (6.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking filter context string high selectivity (kept 1023/1024) | |
| Benchmarking filter context string high selectivity (kept 1023/1024): Warming up for 3.0000 s | |
| Benchmarking filter context string high selectivity (kept 1023/1024): Collecting 100 samples in estimated 6.0217 s (10k iterations) | |
| Benchmarking filter context string high selectivity (kept 1023/1024): Analyzing | |
| filter context string high selectivity (kept 1023/1024) | |
| time: [598.50 µs 600.56 µs 603.24 µs] | |
| change: [+1.1538% +1.7026% +2.2482%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 18 outliers among 100 measurements (18.00%) | |
| 6 (6.00%) high mild | |
| 12 (12.00%) high severe | |
| Benchmarking filter context string low selectivity (kept 1/1024) | |
| Benchmarking filter context string low selectivity (kept 1/1024): Warming up for 3.0000 s | |
| Benchmarking filter context string low selectivity (kept 1/1024): Collecting 100 samples in estimated 5.0062 s (3.9M iterations) | |
| Benchmarking filter context string low selectivity (kept 1/1024): Analyzing | |
| filter context string low selectivity (kept 1/1024) | |
| time: [1.2906 µs 1.2908 µs 1.2910 µs] | |
| change: [+3.9520% +3.9821% +4.0129%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 2 (2.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking filter context string dictionary (kept 1/2) | |
| Benchmarking filter context string dictionary (kept 1/2): Warming up for 3.0000 s | |
| Benchmarking filter context string dictionary (kept 1/2): Collecting 100 samples in estimated 5.0184 s (268k iterations) | |
| Benchmarking filter context string dictionary (kept 1/2): Analyzing | |
| filter context string dictionary (kept 1/2) | |
| time: [18.768 µs 18.772 µs 18.776 µs] | |
| change: [+32.693% +33.213% +33.754%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 8 (8.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking filter context string dictionary high selectivity (kept 1023/1024) | |
| Benchmarking filter context string dictionary high selectivity (kept 1023/1024): Warming up for 3.0000 s | |
| Benchmarking filter context string dictionary high selectivity (kept 1023/1024): Collecting 100 samples in estimated 5.0135 s (808k iterations) | |
| Benchmarking filter context string dictionary high selectivity (kept 1023/1024): Analyzing | |
| filter context string dictionary high selectivity (kept 1023/1024) | |
| time: [6.2341 µs 6.2450 µs 6.2667 µs] | |
| change: [-0.8150% -0.6559% -0.4976%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 2 (2.00%) high mild | |
| 8 (8.00%) high severe | |
| Benchmarking filter context string dictionary low selectivity (kept 1/1024) | |
| Benchmarking filter context string dictionary low selectivity (kept 1/1024): Warming up for 3.0000 s | |
| Benchmarking filter context string dictionary low selectivity (kept 1/1024): Collecting 100 samples in estimated 5.0021 s (8.5M iterations) | |
| Benchmarking filter context string dictionary low selectivity (kept 1/1024): Analyzing | |
| filter context string dictionary low selectivity (kept 1/1024) | |
| time: [634.43 ns 634.55 ns 634.68 ns] | |
| change: [+3.9105% +3.9538% +4.0022%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 1 (1.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking filter context string dictionary w NULLs (kept 1/2) | |
| Benchmarking filter context string dictionary w NULLs (kept 1/2): Warming up for 3.0000 s | |
| Benchmarking filter context string dictionary w NULLs (kept 1/2): Collecting 100 samples in estimated 5.0568 s (71k iterations) | |
| Benchmarking filter context string dictionary w NULLs (kept 1/2): Analyzing | |
| filter context string dictionary w NULLs (kept 1/2) | |
| time: [71.552 µs 71.561 µs 71.572 µs] | |
| change: [+67.614% +67.727% +67.811%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 8 (8.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking filter context string dictionary w NULLs high selectivity (kept 1023/1024) | |
| Benchmarking filter context string dictionary w NULLs high selectivity (kept 1023/1024): Warming up for 3.0000 s | |
| Benchmarking filter context string dictionary w NULLs high selectivity (kept 1023/1024): Collecting 100 samples in estimated 5.0470 s (485k iterations) | |
| Benchmarking filter context string dictionary w NULLs high selectivity (kept 1023/1024): Analyzing | |
| filter context string dictionary w NULLs high selectivity (kept 1023/1024) | |
| time: [10.473 µs 10.476 µs 10.479 µs] | |
| change: [-16.619% -16.325% -16.099%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 4 (4.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking filter context string dictionary w NULLs low selectivity (kept 1/1024) | |
| Benchmarking filter context string dictionary w NULLs low selectivity (kept 1/1024): Warming up for 3.0000 s | |
| Benchmarking filter context string dictionary w NULLs low selectivity (kept 1/1024): Collecting 100 samples in estimated 5.0031 s (6.6M iterations) | |
| Benchmarking filter context string dictionary w NULLs low selectivity (kept 1/1024): Analyzing | |
| filter context string dictionary w NULLs low selectivity (kept 1/1024) | |
| time: [805.48 ns 806.28 ns 807.92 ns] | |
| change: [+5.5156% +5.7521% +5.9972%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) high severe | |
| Benchmarking filter fsb with value length 5 (kept 1/2) | |
| Benchmarking filter fsb with value length 5 (kept 1/2): Warming up for 3.0000 s | |
| Benchmarking filter fsb with value length 5 (kept 1/2): Collecting 100 samples in estimated 5.5972 s (30k iterations) | |
| Benchmarking filter fsb with value length 5 (kept 1/2): Analyzing | |
| filter fsb with value length 5 (kept 1/2) | |
| time: [185.86 µs 185.88 µs 185.91 µs] | |
| change: [+14.019% +14.594% +15.151%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 1 (1.00%) low mild | |
| 6 (6.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking filter fsb with value length 5 high selectivity (kept 1023/1024) | |
| Benchmarking filter fsb with value length 5 high selectivity (kept 1023/1024): Warming up for 3.0000 s | |
| Benchmarking filter fsb with value length 5 high selectivity (kept 1023/1024): Collecting 100 samples in estimated 5.0007 s (535k iterations) | |
| Benchmarking filter fsb with value length 5 high selectivity (kept 1023/1024): Analyzing | |
| filter fsb with value length 5 high selectivity (kept 1023/1024) | |
| time: [9.7810 µs 9.8397 µs 9.8857 µs] | |
| change: [-6.2276% -5.6460% -5.0754%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking filter fsb with value length 5 low selectivity (kept 1/1024) | |
| Benchmarking filter fsb with value length 5 low selectivity (kept 1/1024): Warming up for 3.0000 s | |
| Benchmarking filter fsb with value length 5 low selectivity (kept 1/1024): Collecting 100 samples in estimated 5.0008 s (3.1M iterations) | |
| Benchmarking filter fsb with value length 5 low selectivity (kept 1/1024): Analyzing | |
| filter fsb with value length 5 low selectivity (kept 1/1024) | |
| time: [1.6205 µs 1.6220 µs 1.6235 µs] | |
| change: [-27.186% -27.058% -26.944%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 2 (2.00%) low mild | |
| 5 (5.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking filter context fsb with value length 5 (kept 1/2) | |
| Benchmarking filter context fsb with value length 5 (kept 1/2): Warming up for 3.0000 s | |
| Benchmarking filter context fsb with value length 5 (kept 1/2): Collecting 100 samples in estimated 5.0094 s (96k iterations) | |
| Benchmarking filter context fsb with value length 5 (kept 1/2): Analyzing | |
| filter context fsb with value length 5 (kept 1/2) | |
| time: [52.212 µs 52.280 µs 52.425 µs] | |
| change: [+89.878% +90.075% +90.372%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 5 (5.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking filter context fsb with value length 5 high selectivity (kept 1023/1024) | |
| Benchmarking filter context fsb with value length 5 high selectivity (kept 1023/1024): Warming up for 3.0000 s | |
| Benchmarking filter context fsb with value length 5 high selectivity (kept 1023/1024): Collecting 100 samples in estimated 5.0090 s (96k iterations) | |
| Benchmarking filter context fsb with value length 5 high selectivity (kept 1023/1024): Analyzing | |
| filter context fsb with value length 5 high selectivity (kept 1023/1024) | |
| time: [52.204 µs 52.211 µs 52.219 µs] | |
| change: [+89.856% +89.911% +89.961%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 15 outliers among 100 measurements (15.00%) | |
| 6 (6.00%) high mild | |
| 9 (9.00%) high severe | |
| Benchmarking filter context fsb with value length 5 low selectivity (kept 1/1024) | |
| Benchmarking filter context fsb with value length 5 low selectivity (kept 1/1024): Warming up for 3.0000 s | |
| Benchmarking filter context fsb with value length 5 low selectivity (kept 1/1024): Collecting 100 samples in estimated 5.0091 s (96k iterations) | |
| Benchmarking filter context fsb with value length 5 low selectivity (kept 1/1024): Analyzing | |
| filter context fsb with value length 5 low selectivity (kept 1/1024) | |
| time: [52.201 µs 52.208 µs 52.216 µs] | |
| change: [+86.438% +88.238% +89.560%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 7 (7.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking filter fsb with value length 20 (kept 1/2) | |
| Benchmarking filter fsb with value length 20 (kept 1/2): Warming up for 3.0000 s | |
| Benchmarking filter fsb with value length 20 (kept 1/2): Collecting 100 samples in estimated 5.3787 s (30k iterations) | |
| Benchmarking filter fsb with value length 20 (kept 1/2): Analyzing | |
| filter fsb with value length 20 (kept 1/2) | |
| time: [177.76 µs 177.78 µs 177.81 µs] | |
| change: [+10.077% +10.140% +10.181%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 1 (1.00%) low mild | |
| 8 (8.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking filter fsb with value length 20 high selectivity (kept 1023/1024) | |
| Benchmarking filter fsb with value length 20 high selectivity (kept 1023/1024): Warming up for 3.0000 s | |
| Benchmarking filter fsb with value length 20 high selectivity (kept 1023/1024): Collecting 100 samples in estimated 5.0957 s (162k iterations) | |
| Benchmarking filter fsb with value length 20 high selectivity (kept 1023/1024): Analyzing | |
| filter fsb with value length 20 high selectivity (kept 1023/1024) | |
| time: [31.615 µs 31.707 µs 31.829 µs] | |
| change: [-2.0388% -1.7141% -1.3556%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 17 outliers among 100 measurements (17.00%) | |
| 17 (17.00%) high severe | |
| Benchmarking filter fsb with value length 20 low selectivity (kept 1/1024) | |
| Benchmarking filter fsb with value length 20 low selectivity (kept 1/1024): Warming up for 3.0000 s | |
| Benchmarking filter fsb with value length 20 low selectivity (kept 1/1024): Collecting 100 samples in estimated 5.0018 s (3.0M iterations) | |
| Benchmarking filter fsb with value length 20 low selectivity (kept 1/1024): Analyzing | |
| filter fsb with value length 20 low selectivity (kept 1/1024) | |
| time: [1.6468 µs 1.6486 µs 1.6507 µs] | |
| change: [-26.662% -26.394% -26.203%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) high severe | |
| Benchmarking filter context fsb with value length 20 (kept 1/2) | |
| Benchmarking filter context fsb with value length 20 (kept 1/2): Warming up for 3.0000 s | |
| Benchmarking filter context fsb with value length 20 (kept 1/2): Collecting 100 samples in estimated 5.0099 s (96k iterations) | |
| Benchmarking filter context fsb with value length 20 (kept 1/2): Analyzing | |
| filter context fsb with value length 20 (kept 1/2) | |
| time: [52.218 µs 52.224 µs 52.232 µs] | |
| change: [+88.877% +89.524% +89.981%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 14 outliers among 100 measurements (14.00%) | |
| 4 (4.00%) high mild | |
| 10 (10.00%) high severe | |
| Benchmarking filter context fsb with value length 20 high selectivity (kept 1023/1024) | |
| Benchmarking filter context fsb with value length 20 high selectivity (kept 1023/1024): Warming up for 3.0000 s | |
| Benchmarking filter context fsb with value length 20 high selectivity (kept 1023/1024): Collecting 100 samples in estimated 5.0095 s (96k iterations) | |
| Benchmarking filter context fsb with value length 20 high selectivity (kept 1023/1024): Analyzing | |
| filter context fsb with value length 20 high selectivity (kept 1023/1024) | |
| time: [52.212 µs 52.218 µs 52.225 µs] | |
| change: [+89.799% +89.869% +89.922%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 4 (4.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking filter context fsb with value length 20 low selectivity (kept 1/1024) | |
| Benchmarking filter context fsb with value length 20 low selectivity (kept 1/1024): Warming up for 3.0000 s | |
| Benchmarking filter context fsb with value length 20 low selectivity (kept 1/1024): Collecting 100 samples in estimated 5.0104 s (96k iterations) | |
| Benchmarking filter context fsb with value length 20 low selectivity (kept 1/1024): Analyzing | |
| filter context fsb with value length 20 low selectivity (kept 1/1024) | |
| time: [52.202 µs 52.281 µs 52.450 µs] | |
| change: [+89.891% +90.042% +90.377%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 7 (7.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking filter fsb with value length 50 (kept 1/2) | |
| Benchmarking filter fsb with value length 50 (kept 1/2): Warming up for 3.0000 s | |
| Benchmarking filter fsb with value length 50 (kept 1/2): Collecting 100 samples in estimated 5.2499 s (30k iterations) | |
| Benchmarking filter fsb with value length 50 (kept 1/2): Analyzing | |
| filter fsb with value length 50 (kept 1/2) | |
| time: [175.74 µs 176.00 µs 176.15 µs] | |
| change: [+13.148% +13.432% +13.628%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 2 (2.00%) low severe | |
| 8 (8.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking filter fsb with value length 50 high selectivity (kept 1023/1024) | |
| Benchmarking filter fsb with value length 50 high selectivity (kept 1023/1024): Warming up for 3.0000 s | |
| Benchmarking filter fsb with value length 50 high selectivity (kept 1023/1024): Collecting 100 samples in estimated 5.3310 s (71k iterations) | |
| Benchmarking filter fsb with value length 50 high selectivity (kept 1023/1024): Analyzing | |
| filter fsb with value length 50 high selectivity (kept 1023/1024) | |
| time: [75.378 µs 75.400 µs 75.424 µs] | |
| change: [-4.0288% -3.9558% -3.8739%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 1 (1.00%) low mild | |
| 7 (7.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking filter fsb with value length 50 low selectivity (kept 1/1024) | |
| Benchmarking filter fsb with value length 50 low selectivity (kept 1/1024): Warming up for 3.0000 s | |
| Benchmarking filter fsb with value length 50 low selectivity (kept 1/1024): Collecting 100 samples in estimated 5.0063 s (3.0M iterations) | |
| Benchmarking filter fsb with value length 50 low selectivity (kept 1/1024): Analyzing | |
| filter fsb with value length 50 low selectivity (kept 1/1024) | |
| time: [1.6384 µs 1.6407 µs 1.6430 µs] | |
| change: [-27.175% -27.079% -26.994%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking filter context fsb with value length 50 (kept 1/2) | |
| Benchmarking filter context fsb with value length 50 (kept 1/2): Warming up for 3.0000 s | |
| Benchmarking filter context fsb with value length 50 (kept 1/2): Collecting 100 samples in estimated 5.0103 s (96k iterations) | |
| Benchmarking filter context fsb with value length 50 (kept 1/2): Analyzing | |
| filter context fsb with value length 50 (kept 1/2) | |
| time: [52.214 µs 52.277 µs 52.409 µs] | |
| change: [+89.901% +90.087% +90.400%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 6 (6.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking filter context fsb with value length 50 high selectivity (kept 1023/1024) | |
| Benchmarking filter context fsb with value length 50 high selectivity (kept 1023/1024): Warming up for 3.0000 s | |
| Benchmarking filter context fsb with value length 50 high selectivity (kept 1023/1024): Collecting 100 samples in estimated 5.0092 s (96k iterations) | |
| Benchmarking filter context fsb with value length 50 high selectivity (kept 1023/1024): Analyzing | |
| filter context fsb with value length 50 high selectivity (kept 1023/1024) | |
| time: [52.208 µs 52.215 µs 52.223 µs] | |
| change: [+87.953% +88.961% +89.743%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 4 (4.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking filter context fsb with value length 50 low selectivity (kept 1/1024) | |
| Benchmarking filter context fsb with value length 50 low selectivity (kept 1/1024): Warming up for 3.0000 s | |
| Benchmarking filter context fsb with value length 50 low selectivity (kept 1/1024): Collecting 100 samples in estimated 5.0083 s (96k iterations) | |
| Benchmarking filter context fsb with value length 50 low selectivity (kept 1/1024): Analyzing | |
| filter context fsb with value length 50 low selectivity (kept 1/1024) | |
| time: [52.193 µs 52.199 µs 52.206 µs] | |
| change: [+89.432% +89.763% +89.951%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 8 (8.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking filter single record batch | |
| Benchmarking filter single record batch: Warming up for 3.0000 s | |
| Benchmarking filter single record batch: Collecting 100 samples in estimated 5.3112 s (86k iterations) | |
| Benchmarking filter single record batch: Analyzing | |
| filter single record batch | |
| time: [61.992 µs 62.002 µs 62.014 µs] | |
| change: [-10.735% -8.0842% -5.5179%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 14 outliers among 100 measurements (14.00%) | |
| 4 (4.00%) high mild | |
| 10 (10.00%) high severe | |
| Benchmarking filter context short string view (kept 1/2) | |
| Benchmarking filter context short string view (kept 1/2): Warming up for 3.0000 s | |
| Benchmarking filter context short string view (kept 1/2): Collecting 100 samples in estimated 5.1135 s (71k iterations) | |
| Benchmarking filter context short string view (kept 1/2): Analyzing | |
| filter context short string view (kept 1/2) | |
| time: [72.236 µs 72.332 µs 72.523 µs] | |
| change: [+45.048% +45.257% +45.558%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 3 (3.00%) low mild | |
| 4 (4.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking filter context short string view high selectivity (kept 1023/1024) | |
| Benchmarking filter context short string view high selectivity (kept 1023/1024): Warming up for 3.0000 s | |
| Benchmarking filter context short string view high selectivity (kept 1023/1024): Collecting 100 samples in estimated 5.0803 s (182k iterations) | |
| Benchmarking filter context short string view high selectivity (kept 1023/1024): Analyzing | |
| filter context short string view high selectivity (kept 1023/1024) | |
| time: [28.493 µs 28.618 µs 28.720 µs] | |
| change: [-7.5675% -7.2608% -6.9459%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking filter context short string view low selectivity (kept 1/1024) | |
| Benchmarking filter context short string view low selectivity (kept 1/1024): Warming up for 3.0000 s | |
| Benchmarking filter context short string view low selectivity (kept 1/1024): Collecting 100 samples in estimated 5.0017 s (12M iterations) | |
| Benchmarking filter context short string view low selectivity (kept 1/1024): Analyzing | |
| filter context short string view low selectivity (kept 1/1024) | |
| time: [410.32 ns 410.41 ns 410.50 ns] | |
| change: [+24.313% +24.379% +24.444%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 5 (5.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking filter context mixed string view (kept 1/2) | |
| Benchmarking filter context mixed string view (kept 1/2): Warming up for 3.0000 s | |
| Benchmarking filter context mixed string view (kept 1/2): Collecting 100 samples in estimated 5.1302 s (71k iterations) | |
| Benchmarking filter context mixed string view (kept 1/2): Analyzing | |
| filter context mixed string view (kept 1/2) | |
| time: [72.553 µs 72.575 µs 72.598 µs] | |
| change: [+51.565% +51.722% +51.831%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) high mild | |
| Benchmarking filter context mixed string view high selectivity (kept 1023/1024) | |
| Benchmarking filter context mixed string view high selectivity (kept 1023/1024): Warming up for 3.0000 s | |
| Benchmarking filter context mixed string view high selectivity (kept 1023/1024): Collecting 100 samples in estimated 5.0092 s (177k iterations) | |
| Benchmarking filter context mixed string view high selectivity (kept 1023/1024): Analyzing | |
| filter context mixed string view high selectivity (kept 1023/1024) | |
| time: [28.653 µs 28.762 µs 28.895 µs] | |
| change: [-7.0374% -6.7647% -6.4873%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high mild | |
| Benchmarking filter context mixed string view low selectivity (kept 1/1024) | |
| Benchmarking filter context mixed string view low selectivity (kept 1/1024): Warming up for 3.0000 s | |
| Benchmarking filter context mixed string view low selectivity (kept 1/1024): Collecting 100 samples in estimated 5.0003 s (9.5M iterations) | |
| Benchmarking filter context mixed string view low selectivity (kept 1/1024): Analyzing | |
| filter context mixed string view low selectivity (kept 1/1024) | |
| time: [530.63 ns 530.73 ns 530.84 ns] | |
| change: [+26.182% +26.224% +26.266%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 2 (2.00%) low mild | |
| 5 (5.00%) high mild | |
| 2 (2.00%) high severe | |
| Running benches/interleave_kernels.rs (target/x86_64-unknown-linux-gnu/release/deps/interleave_kernels-0bea01dc1b304251) | |
| Benchmarking interleave i32(0.0) 100 [0..100, 100..230, 450..1000] | |
| Benchmarking interleave i32(0.0) 100 [0..100, 100..230, 450..1000]: Warming up for 3.0000 s | |
| Benchmarking interleave i32(0.0) 100 [0..100, 100..230, 450..1000]: Collecting 100 samples in estimated 5.0009 s (19M iterations) | |
| Benchmarking interleave i32(0.0) 100 [0..100, 100..230, 450..1000]: Analyzing | |
| interleave i32(0.0) 100 [0..100, 100..230, 450..1000] | |
| time: [264.43 ns 264.71 ns 265.03 ns] | |
| change: [-9.1491% -9.0608% -8.9744%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 3 (3.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking interleave i32(0.0) 400 [0..100, 100..230, 450..1000] | |
| Benchmarking interleave i32(0.0) 400 [0..100, 100..230, 450..1000]: Warming up for 3.0000 s | |
| Benchmarking interleave i32(0.0) 400 [0..100, 100..230, 450..1000]: Collecting 100 samples in estimated 5.0027 s (6.8M iterations) | |
| Benchmarking interleave i32(0.0) 400 [0..100, 100..230, 450..1000]: Analyzing | |
| interleave i32(0.0) 400 [0..100, 100..230, 450..1000] | |
| time: [747.21 ns 748.11 ns 749.10 ns] | |
| change: [-17.757% -17.605% -17.463%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) high mild | |
| Benchmarking interleave i32(0.0) 1024 [0..100, 100..230, 450..1000] | |
| Benchmarking interleave i32(0.0) 1024 [0..100, 100..230, 450..1000]: Warming up for 3.0000 s | |
| Benchmarking interleave i32(0.0) 1024 [0..100, 100..230, 450..1000]: Collecting 100 samples in estimated 5.0026 s (2.8M iterations) | |
| Benchmarking interleave i32(0.0) 1024 [0..100, 100..230, 450..1000]: Analyzing | |
| interleave i32(0.0) 1024 [0..100, 100..230, 450..1000] | |
| time: [1.7657 µs 1.7768 µs 1.7907 µs] | |
| change: [-15.368% -14.948% -14.409%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 1 (1.00%) high mild | |
| 8 (8.00%) high severe | |
| Benchmarking interleave i32(0.0) 1024 [0..100, 100..230, 450..1000, 0..1000] | |
| Benchmarking interleave i32(0.0) 1024 [0..100, 100..230, 450..1000, 0..1000]: Warming up for 3.0000 s | |
| Benchmarking interleave i32(0.0) 1024 [0..100, 100..230, 450..1000, 0..1000]: Collecting 100 samples in estimated 5.0081 s (2.8M iterations) | |
| Benchmarking interleave i32(0.0) 1024 [0..100, 100..230, 450..1000, 0..1000]: Analyzing | |
| interleave i32(0.0) 1024 [0..100, 100..230, 450..1000, 0..1000] | |
| time: [1.7724 µs 1.7740 µs 1.7757 µs] | |
| change: [-11.230% -10.937% -10.681%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 5 (5.00%) high mild | |
| Benchmarking interleave i32(0.5) 100 [0..100, 100..230, 450..1000] | |
| Benchmarking interleave i32(0.5) 100 [0..100, 100..230, 450..1000]: Warming up for 3.0000 s | |
| Benchmarking interleave i32(0.5) 100 [0..100, 100..230, 450..1000]: Collecting 100 samples in estimated 5.0034 s (6.9M iterations) | |
| Benchmarking interleave i32(0.5) 100 [0..100, 100..230, 450..1000]: Analyzing | |
| interleave i32(0.5) 100 [0..100, 100..230, 450..1000] | |
| time: [719.76 ns 719.97 ns 720.22 ns] | |
| change: [-9.3355% -9.3031% -9.2703%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 3 (3.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking interleave i32(0.5) 400 [0..100, 100..230, 450..1000] | |
| Benchmarking interleave i32(0.5) 400 [0..100, 100..230, 450..1000]: Warming up for 3.0000 s | |
| Benchmarking interleave i32(0.5) 400 [0..100, 100..230, 450..1000]: Collecting 100 samples in estimated 5.0061 s (2.3M iterations) | |
| Benchmarking interleave i32(0.5) 400 [0..100, 100..230, 450..1000]: Analyzing | |
| interleave i32(0.5) 400 [0..100, 100..230, 450..1000] | |
| time: [2.2242 µs 2.2252 µs 2.2263 µs] | |
| change: [-11.376% -11.329% -11.281%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking interleave i32(0.5) 1024 [0..100, 100..230, 450..1000] | |
| Benchmarking interleave i32(0.5) 1024 [0..100, 100..230, 450..1000]: Warming up for 3.0000 s | |
| Benchmarking interleave i32(0.5) 1024 [0..100, 100..230, 450..1000]: Collecting 100 samples in estimated 5.0138 s (954k iterations) | |
| Benchmarking interleave i32(0.5) 1024 [0..100, 100..230, 450..1000]: Analyzing | |
| interleave i32(0.5) 1024 [0..100, 100..230, 450..1000] | |
| time: [5.2585 µs 5.2662 µs 5.2781 µs] | |
| change: [-13.264% -13.172% -13.047%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 5 (5.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking interleave i32(0.5) 1024 [0..100, 100..230, 450..1000, 0..1000] | |
| Benchmarking interleave i32(0.5) 1024 [0..100, 100..230, 450..1000, 0..1000]: Warming up for 3.0000 s | |
| Benchmarking interleave i32(0.5) 1024 [0..100, 100..230, 450..1000, 0..1000]: Collecting 100 samples in estimated 5.0249 s (939k iterations) | |
| Benchmarking interleave i32(0.5) 1024 [0..100, 100..230, 450..1000, 0..1000]: Analyzing | |
| interleave i32(0.5) 1024 [0..100, 100..230, 450..1000, 0..1000] | |
| time: [5.3385 µs 5.3401 µs 5.3419 µs] | |
| change: [-12.469% -12.430% -12.396%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 6 (6.00%) high mild | |
| Benchmarking interleave str(20, 0.0) 100 [0..100, 100..230, 450..1000] | |
| Benchmarking interleave str(20, 0.0) 100 [0..100, 100..230, 450..1000]: Warming up for 3.0000 s | |
| Benchmarking interleave str(20, 0.0) 100 [0..100, 100..230, 450..1000]: Collecting 100 samples in estimated 5.0016 s (6.4M iterations) | |
| Benchmarking interleave str(20, 0.0) 100 [0..100, 100..230, 450..1000]: Analyzing | |
| interleave str(20, 0.0) 100 [0..100, 100..230, 450..1000] | |
| time: [789.16 ns 791.06 ns 794.87 ns] | |
| change: [+7.9922% +8.1370% +8.3476%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 1 (1.00%) low mild | |
| 3 (3.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking interleave str(20, 0.0) 400 [0..100, 100..230, 450..1000] | |
| Benchmarking interleave str(20, 0.0) 400 [0..100, 100..230, 450..1000]: Warming up for 3.0000 s | |
| Benchmarking interleave str(20, 0.0) 400 [0..100, 100..230, 450..1000]: Collecting 100 samples in estimated 5.0030 s (1.9M iterations) | |
| Benchmarking interleave str(20, 0.0) 400 [0..100, 100..230, 450..1000]: Analyzing | |
| interleave str(20, 0.0) 400 [0..100, 100..230, 450..1000] | |
| time: [2.6590 µs 2.6678 µs 2.6802 µs] | |
| change: [+8.0868% +8.5941% +9.2977%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 3 (3.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking interleave str(20, 0.0) 1024 [0..100, 100..230, 450..1000] | |
| Benchmarking interleave str(20, 0.0) 1024 [0..100, 100..230, 450..1000]: Warming up for 3.0000 s | |
| Benchmarking interleave str(20, 0.0) 1024 [0..100, 100..230, 450..1000]: Collecting 100 samples in estimated 5.0164 s (727k iterations) | |
| Benchmarking interleave str(20, 0.0) 1024 [0..100, 100..230, 450..1000]: Analyzing | |
| interleave str(20, 0.0) 1024 [0..100, 100..230, 450..1000] | |
| time: [6.8469 µs 6.8524 µs 6.8639 µs] | |
| change: [+16.612% +16.783% +17.169%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 2 (2.00%) low mild | |
| 3 (3.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking interleave str(20, 0.0) 1024 [0..100, 100..230, 450..1000, 0..1000] | |
| Benchmarking interleave str(20, 0.0) 1024 [0..100, 100..230, 450..1000, 0..1000]: Warming up for 3.0000 s | |
| Benchmarking interleave str(20, 0.0) 1024 [0..100, 100..230, 450..1000, 0..1000]: Collecting 100 samples in estimated 5.0297 s (737k iterations) | |
| Benchmarking interleave str(20, 0.0) 1024 [0..100, 100..230, 450..1000, 0..1000]: Analyzing | |
| interleave str(20, 0.0) 1024 [0..100, 100..230, 450..1000, 0..1000] | |
| time: [6.8552 µs 6.8562 µs 6.8572 µs] | |
| change: [+14.347% +14.415% +14.471%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 1 (1.00%) low mild | |
| 4 (4.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking interleave str(20, 0.5) 100 [0..100, 100..230, 450..1000] | |
| Benchmarking interleave str(20, 0.5) 100 [0..100, 100..230, 450..1000]: Warming up for 3.0000 s | |
| Benchmarking interleave str(20, 0.5) 100 [0..100, 100..230, 450..1000]: Collecting 100 samples in estimated 5.0054 s (4.3M iterations) | |
| Benchmarking interleave str(20, 0.5) 100 [0..100, 100..230, 450..1000]: Analyzing | |
| interleave str(20, 0.5) 100 [0..100, 100..230, 450..1000] | |
| time: [1.1393 µs 1.1396 µs 1.1400 µs] | |
| change: [-11.203% -10.661% -10.249%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 6 (6.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking interleave str(20, 0.5) 400 [0..100, 100..230, 450..1000] | |
| Benchmarking interleave str(20, 0.5) 400 [0..100, 100..230, 450..1000]: Warming up for 3.0000 s | |
| Benchmarking interleave str(20, 0.5) 400 [0..100, 100..230, 450..1000]: Collecting 100 samples in estimated 5.0020 s (1.3M iterations) | |
| Benchmarking interleave str(20, 0.5) 400 [0..100, 100..230, 450..1000]: Analyzing | |
| interleave str(20, 0.5) 400 [0..100, 100..230, 450..1000] | |
| time: [3.9583 µs 3.9590 µs 3.9598 µs] | |
| change: [-10.070% -9.3674% -8.8088%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 6 (6.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking interleave str(20, 0.5) 1024 [0..100, 100..230, 450..1000] | |
| Benchmarking interleave str(20, 0.5) 1024 [0..100, 100..230, 450..1000]: Warming up for 3.0000 s | |
| Benchmarking interleave str(20, 0.5) 1024 [0..100, 100..230, 450..1000]: Collecting 100 samples in estimated 5.0245 s (510k iterations) | |
| Benchmarking interleave str(20, 0.5) 1024 [0..100, 100..230, 450..1000]: Analyzing | |
| interleave str(20, 0.5) 1024 [0..100, 100..230, 450..1000] | |
| time: [9.8677 µs 9.8824 µs 9.8958 µs] | |
| change: [-15.072% -14.885% -14.711%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high mild | |
| Benchmarking interleave str(20, 0.5) 1024 [0..100, 100..230, 450..1000, 0..1000] | |
| Benchmarking interleave str(20, 0.5) 1024 [0..100, 100..230, 450..1000, 0..1000]: Warming up for 3.0000 s | |
| Benchmarking interleave str(20, 0.5) 1024 [0..100, 100..230, 450..1000, 0..1000]: Collecting 100 samples in estimated 5.0349 s (520k iterations) | |
| Benchmarking interleave str(20, 0.5) 1024 [0..100, 100..230, 450..1000, 0..1000]: Analyzing | |
| interleave str(20, 0.5) 1024 [0..100, 100..230, 450..1000, 0..1000] | |
| time: [9.7864 µs 9.7964 µs 9.8067 µs] | |
| change: [-9.6101% -9.0961% -8.7177%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 5 (5.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking interleave dict(20, 0.0) 100 [0..100, 100..230, 450..1000] | |
| Benchmarking interleave dict(20, 0.0) 100 [0..100, 100..230, 450..1000]: Warming up for 3.0000 s | |
| Benchmarking interleave dict(20, 0.0) 100 [0..100, 100..230, 450..1000]: Collecting 100 samples in estimated 5.0093 s (2.7M iterations) | |
| Benchmarking interleave dict(20, 0.0) 100 [0..100, 100..230, 450..1000]: Analyzing | |
| interleave dict(20, 0.0) 100 [0..100, 100..230, 450..1000] | |
| time: [1.9030 µs 1.9063 µs 1.9125 µs] | |
| change: [-19.114% -18.851% -18.647%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 1 (1.00%) low severe | |
| 6 (6.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking interleave dict(20, 0.0) 400 [0..100, 100..230, 450..1000] | |
| Benchmarking interleave dict(20, 0.0) 400 [0..100, 100..230, 450..1000]: Warming up for 3.0000 s | |
| Benchmarking interleave dict(20, 0.0) 400 [0..100, 100..230, 450..1000]: Collecting 100 samples in estimated 5.0046 s (1.4M iterations) | |
| Benchmarking interleave dict(20, 0.0) 400 [0..100, 100..230, 450..1000]: Analyzing | |
| interleave dict(20, 0.0) 400 [0..100, 100..230, 450..1000] | |
| time: [3.5418 µs 3.5425 µs 3.5433 µs] | |
| change: [-30.781% -30.605% -30.481%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 1 (1.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking interleave dict(20, 0.0) 1024 [0..100, 100..230, 450..1000] | |
| Benchmarking interleave dict(20, 0.0) 1024 [0..100, 100..230, 450..1000]: Warming up for 3.0000 s | |
| Benchmarking interleave dict(20, 0.0) 1024 [0..100, 100..230, 450..1000]: Collecting 100 samples in estimated 5.0289 s (752k iterations) | |
| Benchmarking interleave dict(20, 0.0) 1024 [0..100, 100..230, 450..1000]: Analyzing | |
| interleave dict(20, 0.0) 1024 [0..100, 100..230, 450..1000] | |
| time: [6.6821 µs 6.6831 µs 6.6843 µs] | |
| change: [-30.789% -30.506% -30.299%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 4 (4.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking interleave dict(20, 0.0) 1024 [0..100, 100..230, 450..1000, 0..1000] | |
| Benchmarking interleave dict(20, 0.0) 1024 [0..100, 100..230, 450..1000, 0..1000]: Warming up for 3.0000 s | |
| Benchmarking interleave dict(20, 0.0) 1024 [0..100, 100..230, 450..1000, 0..1000]: Collecting 100 samples in estimated 5.0278 s (722k iterations) | |
| Benchmarking interleave dict(20, 0.0) 1024 [0..100, 100..230, 450..1000, 0..1000]: Analyzing | |
| interleave dict(20, 0.0) 1024 [0..100, 100..230, 450..1000, 0..1000] | |
| time: [6.9698 µs 6.9708 µs 6.9720 µs] | |
| change: [-30.642% -30.614% -30.578%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 14 outliers among 100 measurements (14.00%) | |
| 11 (11.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking interleave dict_sparse(20, 0.0) 100 [0..100, 100..230, 450..1000] | |
| Benchmarking interleave dict_sparse(20, 0.0) 100 [0..100, 100..230, 450..1000]: Warming up for 3.0000 s | |
| Benchmarking interleave dict_sparse(20, 0.0) 100 [0..100, 100..230, 450..1000]: Collecting 100 samples in estimated 5.0047 s (2.7M iterations) | |
| Benchmarking interleave dict_sparse(20, 0.0) 100 [0..100, 100..230, 450..1000]: Analyzing | |
| interleave dict_sparse(20, 0.0) 100 [0..100, 100..230, 450..1000] | |
| time: [1.9063 µs 1.9066 µs 1.9069 µs] | |
| change: [-14.321% -14.298% -14.273%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 2 (2.00%) low mild | |
| 2 (2.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking interleave dict_sparse(20, 0.0) 400 [0..100, 100..230, 450..1000] | |
| Benchmarking interleave dict_sparse(20, 0.0) 400 [0..100, 100..230, 450..1000]: Warming up for 3.0000 s | |
| Benchmarking interleave dict_sparse(20, 0.0) 400 [0..100, 100..230, 450..1000]: Collecting 100 samples in estimated 5.0060 s (1.4M iterations) | |
| Benchmarking interleave dict_sparse(20, 0.0) 400 [0..100, 100..230, 450..1000]: Analyzing | |
| interleave dict_sparse(20, 0.0) 400 [0..100, 100..230, 450..1000] | |
| time: [3.5596 µs 3.5606 µs 3.5615 µs] | |
| change: [-23.781% -23.740% -23.708%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 7 (7.00%) low mild | |
| 3 (3.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking interleave dict_sparse(20, 0.0) 1024 [0..100, 100..230, 450..1000] | |
| Benchmarking interleave dict_sparse(20, 0.0) 1024 [0..100, 100..230, 450..1000]: Warming up for 3.0000 s | |
| Benchmarking interleave dict_sparse(20, 0.0) 1024 [0..100, 100..230, 450..1000]: Collecting 100 samples in estimated 5.0261 s (752k iterations) | |
| Benchmarking interleave dict_sparse(20, 0.0) 1024 [0..100, 100..230, 450..1000]: Analyzing | |
| interleave dict_sparse(20, 0.0) 1024 [0..100, 100..230, 450..1000] | |
| time: [6.6742 µs 6.6754 µs 6.6767 µs] | |
| change: [-29.214% -29.193% -29.174%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 3 (3.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking interleave dict_sparse(20, 0.0) 1024 [0..100, 100..230, 450..1000, 0..1000] | |
| Benchmarking interleave dict_sparse(20, 0.0) 1024 [0..100, 100..230, 450..1000, 0..1000]: Warming up for 3.0000 s | |
| Benchmarking interleave dict_sparse(20, 0.0) 1024 [0..100, 100..230, 450..1000, 0..1000]: Collecting 100 samples in estimated 5.0091 s (717k iterations) | |
| Benchmarking interleave dict_sparse(20, 0.0) 1024 [0..100, 100..230, 450..1000, 0..1000]: Analyzing | |
| interleave dict_sparse(20, 0.0) 1024 [0..100, 100..230, 450..1000, 0..1000] | |
| time: [6.9848 µs 6.9861 µs 6.9875 µs] | |
| change: [-51.021% -51.002% -50.985%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 3 (3.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking interleave dict_distinct 100 | |
| Benchmarking interleave dict_distinct 100: Warming up for 3.0000 s | |
| Benchmarking interleave dict_distinct 100: Collecting 100 samples in estimated 5.0020 s (2.3M iterations) | |
| Benchmarking interleave dict_distinct 100: Analyzing | |
| interleave dict_distinct 100 | |
| time: [2.1339 µs 2.1347 µs 2.1353 µs] | |
| change: [-6.2750% -6.2231% -6.1751%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high mild | |
| Benchmarking interleave dict_distinct 1024 | |
| Benchmarking interleave dict_distinct 1024: Warming up for 3.0000 s | |
| Benchmarking interleave dict_distinct 1024: Collecting 100 samples in estimated 5.0070 s (2.3M iterations) | |
| Benchmarking interleave dict_distinct 1024: Analyzing | |
| interleave dict_distinct 1024 | |
| time: [2.1309 µs 2.1316 µs 2.1325 µs] | |
| change: [-7.5675% -7.4561% -7.3686%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 1 (1.00%) low mild | |
| 6 (6.00%) high mild | |
| Benchmarking interleave dict_distinct 2048 | |
| Benchmarking interleave dict_distinct 2048: Warming up for 3.0000 s | |
| Benchmarking interleave dict_distinct 2048: Collecting 100 samples in estimated 5.0072 s (2.3M iterations) | |
| Benchmarking interleave dict_distinct 2048: Analyzing | |
| interleave dict_distinct 2048 | |
| time: [2.1469 µs 2.1585 µs 2.1732 µs] | |
| change: [-5.3005% -4.9208% -4.4976%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 2 (2.00%) low mild | |
| 1 (1.00%) high mild | |
| 5 (5.00%) high severe | |
| Running benches/json_reader.rs (target/x86_64-unknown-linux-gnu/release/deps/json_reader-4edcca4b1f146ce8) | |
| Benchmarking small_bench_primitive | |
| Benchmarking small_bench_primitive: Warming up for 3.0000 s | |
| Benchmarking small_bench_primitive: Collecting 100 samples in estimated 5.0090 s (1.2M iterations) | |
| Benchmarking small_bench_primitive: Analyzing | |
| small_bench_primitive time: [4.3483 µs 4.3725 µs 4.4032 µs] | |
| change: [-16.371% -16.054% -15.646%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 4 (4.00%) high severe | |
| Benchmarking large_bench_primitive | |
| Benchmarking large_bench_primitive: Warming up for 3.0000 s | |
| Benchmarking large_bench_primitive: Collecting 100 samples in estimated 5.1863 s (2300 iterations) | |
| Benchmarking large_bench_primitive: Analyzing | |
| large_bench_primitive time: [2.2636 ms 2.2662 ms 2.2704 ms] | |
| change: [-24.848% -24.638% -24.437%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 24 outliers among 100 measurements (24.00%) | |
| 4 (4.00%) low severe | |
| 12 (12.00%) low mild | |
| 2 (2.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking small_bench_list | |
| Benchmarking small_bench_list: Warming up for 3.0000 s | |
| Benchmarking small_bench_list: Collecting 100 samples in estimated 5.0374 s (566k iterations) | |
| Benchmarking small_bench_list: Analyzing | |
| small_bench_list time: [8.9926 µs 8.9958 µs 8.9990 µs] | |
| change: [-15.059% -14.489% -14.054%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 2 (2.00%) high mild | |
| 2 (2.00%) high severe | |
| Running benches/json_writer.rs (target/x86_64-unknown-linux-gnu/release/deps/json_writer-a6df09be24fe102a) | |
| Benchmarking bench_integer | |
| Benchmarking bench_integer: Warming up for 3.0000 s | |
| Benchmarking bench_integer: Collecting 100 samples in estimated 5.3324 s (1200 iterations) | |
| Benchmarking bench_integer: Analyzing | |
| bench_integer time: [4.4307 ms 4.4315 ms 4.4323 ms] | |
| change: [-13.878% -13.842% -13.809%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 8 (8.00%) high mild | |
| Benchmarking bench_float | |
| Benchmarking bench_float: Warming up for 3.0000 s | |
| Benchmarking bench_float: Collecting 100 samples in estimated 5.1140 s (1000 iterations) | |
| Benchmarking bench_float: Analyzing | |
| bench_float time: [5.1101 ms 5.1311 ms 5.1588 ms] | |
| change: [-9.5425% -9.1634% -8.5651%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 3 (3.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking bench_string | |
| Benchmarking bench_string: Warming up for 3.0000 s | |
| Benchmarking bench_string: Collecting 100 samples in estimated 6.1640 s (500 iterations) | |
| Benchmarking bench_string: Analyzing | |
| bench_string time: [12.194 ms 12.218 ms 12.247 ms] | |
| change: [+11.821% +12.832% +13.770%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 4 (4.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking bench_mixed | |
| Benchmarking bench_mixed: Warming up for 3.0000 s | |
| Benchmarking bench_mixed: Collecting 100 samples in estimated 5.1006 s (200 iterations) | |
| Benchmarking bench_mixed: Analyzing | |
| bench_mixed time: [25.475 ms 25.509 ms 25.547 ms] | |
| change: [+5.4512% +5.6985% +5.9481%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 2 (2.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking bench_dict_array | |
| Benchmarking bench_dict_array: Warming up for 3.0000 s | |
| Benchmarking bench_dict_array: Collecting 100 samples in estimated 5.1455 s (900 iterations) | |
| Benchmarking bench_dict_array: Analyzing | |
| bench_dict_array time: [5.7162 ms 5.7190 ms 5.7221 ms] | |
| change: [-3.2755% -3.1936% -3.1223%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 6 (6.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking bench_struct | |
| Benchmarking bench_struct: Warming up for 3.0000 s | |
| Benchmarking bench_struct: Collecting 100 samples in estimated 8.6461 s (200 iterations) | |
| Benchmarking bench_struct: Analyzing | |
| bench_struct time: [43.188 ms 43.321 ms 43.488 ms] | |
| change: [+9.0799% +9.5090% +10.019%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 15 outliers among 100 measurements (15.00%) | |
| 6 (6.00%) high mild | |
| 9 (9.00%) high severe | |
| Benchmarking bench_nullable_struct | |
| Benchmarking bench_nullable_struct: Warming up for 3.0000 s | |
| Benchmarking bench_nullable_struct: Collecting 100 samples in estimated 5.9328 s (400 iterations) | |
| Benchmarking bench_nullable_struct: Analyzing | |
| bench_nullable_struct time: [14.719 ms 14.802 ms 14.906 ms] | |
| change: [+12.926% +14.097% +15.296%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 17 outliers among 100 measurements (17.00%) | |
| 5 (5.00%) high mild | |
| 12 (12.00%) high severe | |
| Benchmarking bench_list | |
| Benchmarking bench_list: Warming up for 3.0000 s | |
| Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 6.8s, or reduce sample count to 70. | |
| Benchmarking bench_list: Collecting 100 samples in estimated 6.7801 s (100 iterations) | |
| Benchmarking bench_list: Analyzing | |
| bench_list time: [67.213 ms 67.303 ms 67.404 ms] | |
| change: [+13.747% +14.140% +14.482%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 3 (3.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking bench_nullable_list | |
| Benchmarking bench_nullable_list: Warming up for 3.0000 s | |
| Benchmarking bench_nullable_list: Collecting 100 samples in estimated 5.0856 s (600 iterations) | |
| Benchmarking bench_nullable_list: Analyzing | |
| bench_nullable_list time: [8.3667 ms 8.4054 ms 8.4557 ms] | |
| change: [+29.829% +30.913% +31.969%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 14 outliers among 100 measurements (14.00%) | |
| 1 (1.00%) high mild | |
| 13 (13.00%) high severe | |
| Benchmarking bench_struct_list | |
| Benchmarking bench_struct_list: Warming up for 3.0000 s | |
| Benchmarking bench_struct_list: Collecting 100 samples in estimated 5.1048 s (1200 iterations) | |
| Benchmarking bench_struct_list: Analyzing | |
| bench_struct_list time: [4.2493 ms 4.2626 ms 4.2827 ms] | |
| change: [-2.7376% -2.1116% -1.4640%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 13 outliers among 100 measurements (13.00%) | |
| 1 (1.00%) high mild | |
| 12 (12.00%) high severe | |
| Running benches/length_kernel.rs (target/x86_64-unknown-linux-gnu/release/deps/length_kernel-ef6777b5e2752729) | |
| Benchmarking length | |
| Benchmarking length: Warming up for 3.0000 s | |
| Benchmarking length: Collecting 100 samples in estimated 5.0019 s (11M iterations) | |
| Benchmarking length: Analyzing | |
| length time: [462.91 ns 463.39 ns 464.08 ns] | |
| change: [+1.3485% +1.8691% +3.2205%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 2 (2.00%) high mild | |
| 2 (2.00%) high severe | |
| Running benches/lexsort.rs (target/x86_64-unknown-linux-gnu/release/deps/lexsort-2bb116d997d64c79) | |
| Benchmarking lexsort_to_indices([i32, i32_opt]): 4096 | |
| Benchmarking lexsort_to_indices([i32, i32_opt]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([i32, i32_opt]): 4096: Collecting 100 samples in estimated 5.8657 s (30k iterations) | |
| Benchmarking lexsort_to_indices([i32, i32_opt]): 4096: Analyzing | |
| lexsort_to_indices([i32, i32_opt]): 4096 | |
| time: [197.13 µs 197.17 µs 197.22 µs] | |
| change: [+2.4054% +2.5848% +2.7540%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 1 (1.00%) low severe | |
| 4 (4.00%) low mild | |
| 4 (4.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking lexsort_rows([i32, i32_opt]): 4096 | |
| Benchmarking lexsort_rows([i32, i32_opt]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([i32, i32_opt]): 4096: Collecting 100 samples in estimated 5.6021 s (20k iterations) | |
| Benchmarking lexsort_rows([i32, i32_opt]): 4096: Analyzing | |
| lexsort_rows([i32, i32_opt]): 4096 | |
| time: [277.34 µs 278.44 µs 279.85 µs] | |
| change: [+5.3431% +5.6279% +5.9471%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 3 (3.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking lexsort_to_indices([i32, i32_opt]): 32768 | |
| Benchmarking lexsort_to_indices([i32, i32_opt]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([i32, i32_opt]): 32768: Collecting 100 samples in estimated 5.0898 s (2500 iterations) | |
| Benchmarking lexsort_to_indices([i32, i32_opt]): 32768: Analyzing | |
| lexsort_to_indices([i32, i32_opt]): 32768 | |
| time: [2.0498 ms 2.0565 ms 2.0651 ms] | |
| change: [+1.4146% +1.7507% +2.1884%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 3 (3.00%) high mild | |
| 7 (7.00%) high severe | |
| Benchmarking lexsort_rows([i32, i32_opt]): 32768 | |
| Benchmarking lexsort_rows([i32, i32_opt]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([i32, i32_opt]): 32768: Collecting 100 samples in estimated 5.1527 s (1800 iterations) | |
| Benchmarking lexsort_rows([i32, i32_opt]): 32768: Analyzing | |
| lexsort_rows([i32, i32_opt]): 32768 | |
| time: [2.8617 ms 2.8723 ms 2.8862 ms] | |
| change: [+3.7112% +4.1052% +4.5758%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 2 (2.00%) low severe | |
| 1 (1.00%) low mild | |
| 1 (1.00%) high mild | |
| 7 (7.00%) high severe | |
| Benchmarking lexsort_to_indices([i32, str_opt(16)]): 4096 | |
| Benchmarking lexsort_to_indices([i32, str_opt(16)]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([i32, str_opt(16)]): 4096: Collecting 100 samples in estimated 5.9103 s (30k iterations) | |
| Benchmarking lexsort_to_indices([i32, str_opt(16)]): 4096: Analyzing | |
| lexsort_to_indices([i32, str_opt(16)]): 4096 | |
| time: [199.72 µs 199.93 µs 200.09 µs] | |
| change: [+0.5793% +0.7821% +1.0107%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Benchmarking lexsort_rows([i32, str_opt(16)]): 4096 | |
| Benchmarking lexsort_rows([i32, str_opt(16)]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([i32, str_opt(16)]): 4096: Collecting 100 samples in estimated 6.5355 s (20k iterations) | |
| Benchmarking lexsort_rows([i32, str_opt(16)]): 4096: Analyzing | |
| lexsort_rows([i32, str_opt(16)]): 4096 | |
| time: [322.08 µs 323.10 µs 324.42 µs] | |
| change: [+0.7429% +1.0419% +1.3706%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 16 outliers among 100 measurements (16.00%) | |
| 11 (11.00%) low severe | |
| 1 (1.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking lexsort_to_indices([i32, str_opt(16)]): 32768 | |
| Benchmarking lexsort_to_indices([i32, str_opt(16)]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([i32, str_opt(16)]): 32768: Collecting 100 samples in estimated 5.0707 s (2500 iterations) | |
| Benchmarking lexsort_to_indices([i32, str_opt(16)]): 32768: Analyzing | |
| lexsort_to_indices([i32, str_opt(16)]): 32768 | |
| time: [2.0450 ms 2.0460 ms 2.0469 ms] | |
| change: [+0.6781% +0.7281% +0.7743%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 14 outliers among 100 measurements (14.00%) | |
| 8 (8.00%) low severe | |
| 5 (5.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking lexsort_rows([i32, str_opt(16)]): 32768 | |
| Benchmarking lexsort_rows([i32, str_opt(16)]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([i32, str_opt(16)]): 32768: Collecting 100 samples in estimated 5.2309 s (1600 iterations) | |
| Benchmarking lexsort_rows([i32, str_opt(16)]): 32768: Analyzing | |
| lexsort_rows([i32, str_opt(16)]): 32768 | |
| time: [3.2686 ms 3.2830 ms 3.3010 ms] | |
| change: [+4.3615% +4.8308% +5.2673%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 20 outliers among 100 measurements (20.00%) | |
| 10 (10.00%) low severe | |
| 4 (4.00%) low mild | |
| 2 (2.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking lexsort_to_indices([i32, str(16)]): 4096 | |
| Benchmarking lexsort_to_indices([i32, str(16)]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([i32, str(16)]): 4096: Collecting 100 samples in estimated 5.9132 s (30k iterations) | |
| Benchmarking lexsort_to_indices([i32, str(16)]): 4096: Analyzing | |
| lexsort_to_indices([i32, str(16)]): 4096 | |
| time: [198.34 µs 199.10 µs 199.86 µs] | |
| change: [+2.5343% +2.9646% +3.4207%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 3 (3.00%) high mild | |
| Benchmarking lexsort_rows([i32, str(16)]): 4096 | |
| Benchmarking lexsort_rows([i32, str(16)]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([i32, str(16)]): 4096: Collecting 100 samples in estimated 6.0328 s (20k iterations) | |
| Benchmarking lexsort_rows([i32, str(16)]): 4096: Analyzing | |
| lexsort_rows([i32, str(16)]): 4096 | |
| time: [298.12 µs 298.41 µs 298.83 µs] | |
| change: [+7.1166% +7.3372% +7.7312%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 1 (1.00%) low severe | |
| 1 (1.00%) low mild | |
| 1 (1.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking lexsort_to_indices([i32, str(16)]): 32768 | |
| Benchmarking lexsort_to_indices([i32, str(16)]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([i32, str(16)]): 32768: Collecting 100 samples in estimated 5.1194 s (2500 iterations) | |
| Benchmarking lexsort_to_indices([i32, str(16)]): 32768: Analyzing | |
| lexsort_to_indices([i32, str(16)]): 32768 | |
| time: [2.0437 ms 2.0440 ms 2.0443 ms] | |
| change: [+0.7487% +0.7667% +0.7858%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 1 (1.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking lexsort_rows([i32, str(16)]): 32768 | |
| Benchmarking lexsort_rows([i32, str(16)]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([i32, str(16)]): 32768: Collecting 100 samples in estimated 5.1566 s (1700 iterations) | |
| Benchmarking lexsort_rows([i32, str(16)]): 32768: Analyzing | |
| lexsort_rows([i32, str(16)]): 32768 | |
| time: [3.0269 ms 3.0350 ms 3.0460 ms] | |
| change: [+0.4568% +0.8661% +1.3609%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 3 (3.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking lexsort_to_indices([str_opt(16), str(16)]): 4096 | |
| Benchmarking lexsort_to_indices([str_opt(16), str(16)]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([str_opt(16), str(16)]): 4096: Collecting 100 samples in estimated 6.4052 s (15k iterations) | |
| Benchmarking lexsort_to_indices([str_opt(16), str(16)]): 4096: Analyzing | |
| lexsort_to_indices([str_opt(16), str(16)]): 4096 | |
| time: [429.51 µs 429.59 µs 429.68 µs] | |
| change: [-20.456% -20.428% -20.402%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking lexsort_rows([str_opt(16), str(16)]): 4096 | |
| Benchmarking lexsort_rows([str_opt(16), str(16)]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([str_opt(16), str(16)]): 4096: Collecting 100 samples in estimated 6.1084 s (20k iterations) | |
| Benchmarking lexsort_rows([str_opt(16), str(16)]): 4096: Analyzing | |
| lexsort_rows([str_opt(16), str(16)]): 4096 | |
| time: [301.82 µs 302.61 µs 303.59 µs] | |
| change: [-2.0577% -1.8615% -1.6225%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 2 (2.00%) low severe | |
| 3 (3.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking lexsort_to_indices([str_opt(16), str(16)]): 32768 | |
| Benchmarking lexsort_to_indices([str_opt(16), str(16)]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([str_opt(16), str(16)]): 32768: Collecting 100 samples in estimated 5.1013 s (1100 iterations) | |
| Benchmarking lexsort_to_indices([str_opt(16), str(16)]): 32768: Analyzing | |
| lexsort_to_indices([str_opt(16), str(16)]): 32768 | |
| time: [4.6675 ms 4.6842 ms 4.7057 ms] | |
| change: [-20.198% -19.877% -19.470%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 2 (2.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking lexsort_rows([str_opt(16), str(16)]): 32768 | |
| Benchmarking lexsort_rows([str_opt(16), str(16)]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([str_opt(16), str(16)]): 32768: Collecting 100 samples in estimated 5.2173 s (1700 iterations) | |
| Benchmarking lexsort_rows([str_opt(16), str(16)]): 32768: Analyzing | |
| lexsort_rows([str_opt(16), str(16)]): 32768 | |
| time: [3.0640 ms 3.0653 ms 3.0666 ms] | |
| change: [-3.2988% -3.2498% -3.2031%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking lexsort_to_indices([str_opt(16), str_opt(50), str(16)]): 4096 | |
| Benchmarking lexsort_to_indices([str_opt(16), str_opt(50), str(16)]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([str_opt(16), str_opt(50), str(16)]): 4096: Collecting 100 samples in estimated 6.3391 s (15k iterations) | |
| Benchmarking lexsort_to_indices([str_opt(16), str_opt(50), str(16)]): 4096: Analyzing | |
| lexsort_to_indices([str_opt(16), str_opt(50), str(16)]): 4096 | |
| time: [417.83 µs 417.89 µs 417.95 µs] | |
| change: [-21.475% -21.451% -21.427%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 3 (3.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking lexsort_rows([str_opt(16), str_opt(50), str(16)]): 4096 | |
| Benchmarking lexsort_rows([str_opt(16), str_opt(50), str(16)]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([str_opt(16), str_opt(50), str(16)]): 4096: Collecting 100 samples in estimated 5.0264 s (15k iterations) | |
| Benchmarking lexsort_rows([str_opt(16), str_opt(50), str(16)]): 4096: Analyzing | |
| lexsort_rows([str_opt(16), str_opt(50), str(16)]): 4096 | |
| time: [331.63 µs 331.69 µs 331.76 µs] | |
| change: [-6.9852% -6.9196% -6.8686%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 1 (1.00%) low mild | |
| 7 (7.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking lexsort_to_indices([str_opt(16), str_opt(50), str(16)]): 32768 | |
| Benchmarking lexsort_to_indices([str_opt(16), str_opt(50), str(16)]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([str_opt(16), str_opt(50), str(16)]): 32768: Collecting 100 samples in estimated 5.0320 s (1100 iterations) | |
| Benchmarking lexsort_to_indices([str_opt(16), str_opt(50), str(16)]): 32768: Analyzing | |
| lexsort_to_indices([str_opt(16), str_opt(50), str(16)]): 32768 | |
| time: [4.5715 ms 4.5770 ms 4.5871 ms] | |
| change: [-20.865% -20.766% -20.584%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 10 (10.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking lexsort_rows([str_opt(16), str_opt(50), str(16)]): 32768 | |
| Benchmarking lexsort_rows([str_opt(16), str_opt(50), str(16)]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([str_opt(16), str_opt(50), str(16)]): 32768: Collecting 100 samples in estimated 5.0892 s (1500 iterations) | |
| Benchmarking lexsort_rows([str_opt(16), str_opt(50), str(16)]): 32768: Analyzing | |
| lexsort_rows([str_opt(16), str_opt(50), str(16)]): 32768 | |
| time: [3.3942 ms 3.3948 ms 3.3954 ms] | |
| change: [-4.0151% -3.9913% -3.9675%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 6 (6.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking lexsort_to_indices([str_opt(16), str(16), str_opt(16), str_opt(16), str_opt(16)]): 4096 | |
| Benchmarking lexsort_to_indices([str_opt(16), str(16), str_opt(16), str_opt(16), str_opt(16)]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([str_opt(16), str(16), str_opt(16), str_opt(16), str_opt(16)]): 4096: Collecting 100 samples in estimated 6.4020 s (15k iterations) | |
| Benchmarking lexsort_to_indices([str_opt(16), str(16), str_opt(16), str_opt(16), str_opt(16)]): 4096: Analyzing | |
| lexsort_to_indices([str_opt(16), str(16), str_opt(16), str_opt(16), str_opt(16)]): 4096 | |
| time: [424.55 µs 424.88 µs 425.16 µs] | |
| change: [-22.387% -22.313% -22.242%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking lexsort_rows([str_opt(16), str(16), str_opt(16), str_opt(16), str_opt(16)]): 4096 | |
| Benchmarking lexsort_rows([str_opt(16), str(16), str_opt(16), str_opt(16), str_opt(16)]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([str_opt(16), str(16), str_opt(16), str_opt(16), str_opt(16)]): 4096: Collecting 100 samples in estimated 6.1942 s (15k iterations) | |
| Benchmarking lexsort_rows([str_opt(16), str(16), str_opt(16), str_opt(16), str_opt(16)]): 4096: Analyzing | |
| lexsort_rows([str_opt(16), str(16), str_opt(16), str_opt(16), str_opt(16)]): 4096 | |
| time: [407.29 µs 407.66 µs 408.01 µs] | |
| change: [-7.2936% -7.1341% -6.9686%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 21 outliers among 100 measurements (21.00%) | |
| 15 (15.00%) low severe | |
| 2 (2.00%) low mild | |
| 1 (1.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking lexsort_to_indices([str_opt(16), str(16), str_opt(16), str_opt(16), str_opt(16)]): 32768 | |
| Benchmarking lexsort_to_indices([str_opt(16), str(16), str_opt(16), str_opt(16), str_opt(16)]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([str_opt(16), str(16), str_opt(16), str_opt(16), str_opt(16)]): 32768: Collecting 100 samples in estimated 5.0744 s (1100 iterations) | |
| Benchmarking lexsort_to_indices([str_opt(16), str(16), str_opt(16), str_opt(16), str_opt(16)]): 32768: Analyzing | |
| lexsort_to_indices([str_opt(16), str(16), str_opt(16), str_opt(16), str_opt(16)]): 32768 | |
| time: [4.6096 ms 4.6257 ms 4.6464 ms] | |
| change: [-21.456% -21.183% -20.831%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 6 (6.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking lexsort_rows([str_opt(16), str(16), str_opt(16), str_opt(16), str_opt(16)]): 32768 | |
| Benchmarking lexsort_rows([str_opt(16), str(16), str_opt(16), str_opt(16), str_opt(16)]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([str_opt(16), str(16), str_opt(16), str_opt(16), str_opt(16)]): 32768: Collecting 100 samples in estimated 5.0040 s (1200 iterations) | |
| Benchmarking lexsort_rows([str_opt(16), str(16), str_opt(16), str_opt(16), str_opt(16)]): 32768: Analyzing | |
| lexsort_rows([str_opt(16), str(16), str_opt(16), str_opt(16), str_opt(16)]): 32768 | |
| time: [4.1535 ms 4.1601 ms 4.1723 ms] | |
| change: [-4.9394% -4.7830% -4.5079%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 15 outliers among 100 measurements (15.00%) | |
| 12 (12.00%) low mild | |
| 1 (1.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking lexsort_to_indices([i32_opt, dict(100,str_opt(50))]): 4096 | |
| Benchmarking lexsort_to_indices([i32_opt, dict(100,str_opt(50))]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([i32_opt, dict(100,str_opt(50))]): 4096: Collecting 100 samples in estimated 5.1147 s (20k iterations) | |
| Benchmarking lexsort_to_indices([i32_opt, dict(100,str_opt(50))]): 4096: Analyzing | |
| lexsort_to_indices([i32_opt, dict(100,str_opt(50))]): 4096 | |
| time: [253.05 µs 253.10 µs 253.15 µs] | |
| change: [-0.8450% -0.8100% -0.7746%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 3 (3.00%) high severe | |
| Benchmarking lexsort_rows([i32_opt, dict(100,str_opt(50))]): 4096 | |
| Benchmarking lexsort_rows([i32_opt, dict(100,str_opt(50))]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([i32_opt, dict(100,str_opt(50))]): 4096: Collecting 100 samples in estimated 5.4397 s (20k iterations) | |
| Benchmarking lexsort_rows([i32_opt, dict(100,str_opt(50))]): 4096: Analyzing | |
| lexsort_rows([i32_opt, dict(100,str_opt(50))]): 4096 | |
| time: [268.38 µs 268.46 µs 268.53 µs] | |
| change: [+4.5778% +4.7579% +4.8699%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Benchmarking lexsort_to_indices([i32_opt, dict(100,str_opt(50))]): 32768 | |
| Benchmarking lexsort_to_indices([i32_opt, dict(100,str_opt(50))]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([i32_opt, dict(100,str_opt(50))]): 32768: Collecting 100 samples in estimated 5.1863 s (1900 iterations) | |
| Benchmarking lexsort_to_indices([i32_opt, dict(100,str_opt(50))]): 32768: Analyzing | |
| lexsort_to_indices([i32_opt, dict(100,str_opt(50))]): 32768 | |
| time: [2.7308 ms 2.7341 ms 2.7400 ms] | |
| change: [-0.0164% +0.1084% +0.3332%] (p = 0.35 > 0.05) | |
| No change in performance detected. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high severe | |
| Benchmarking lexsort_rows([i32_opt, dict(100,str_opt(50))]): 32768 | |
| Benchmarking lexsort_rows([i32_opt, dict(100,str_opt(50))]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([i32_opt, dict(100,str_opt(50))]): 32768: Collecting 100 samples in estimated 5.1996 s (1900 iterations) | |
| Benchmarking lexsort_rows([i32_opt, dict(100,str_opt(50))]): 32768: Analyzing | |
| lexsort_rows([i32_opt, dict(100,str_opt(50))]): 32768 | |
| time: [2.7316 ms 2.7363 ms 2.7406 ms] | |
| change: [-0.5795% -0.3311% -0.1242%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 26 outliers among 100 measurements (26.00%) | |
| 20 (20.00%) low severe | |
| 1 (1.00%) low mild | |
| 5 (5.00%) high mild | |
| Benchmarking lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50))]): 4096 | |
| Benchmarking lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50))]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50))]): 4096: Collecting 100 samples in estimated 5.5519 s (50k iterations) | |
| Benchmarking lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50))]): 4096: Analyzing | |
| lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50))]): 4096 | |
| time: [109.93 µs 109.94 µs 109.96 µs] | |
| change: [-4.4801% -4.3576% -4.2544%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 3 (3.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50))]): 4096 | |
| Benchmarking lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50))]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50))]): 4096: Collecting 100 samples in estimated 5.3336 s (45k iterations) | |
| Benchmarking lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50))]): 4096: Analyzing | |
| lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50))]): 4096 | |
| time: [114.63 µs 114.87 µs 115.13 µs] | |
| change: [-3.5846% -3.2870% -2.9842%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 13 outliers among 100 measurements (13.00%) | |
| 13 (13.00%) high mild | |
| Benchmarking lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50))]): 32768 | |
| Benchmarking lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50))]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50))]): 32768: Collecting 100 samples in estimated 9.8614 s (10k iterations) | |
| Benchmarking lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50))]): 32768: Analyzing | |
| lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50))]): 32768 | |
| time: [978.75 µs 979.01 µs 979.45 µs] | |
| change: [-3.0482% -2.8977% -2.6377%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 22 outliers among 100 measurements (22.00%) | |
| 9 (9.00%) low severe | |
| 9 (9.00%) low mild | |
| 2 (2.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50))]): 32768 | |
| Benchmarking lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50))]): 32768: Warming up for 3.0000 s | |
| Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 5.1s, enable flat sampling, or reduce sample count to 70. | |
| Benchmarking lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50))]): 32768: Collecting 100 samples in estimated 5.0547 s (5050 iterations) | |
| Benchmarking lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50))]): 32768: Analyzing | |
| lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50))]): 32768 | |
| time: [999.96 µs 1.0001 ms 1.0003 ms] | |
| change: [-5.6564% -5.5196% -5.3768%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 1 (1.00%) low mild | |
| 5 (5.00%) high mild | |
| Benchmarking lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str(16)]): ... | |
| Benchmarking lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str(16)]): ...: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str(16)]): ...: Collecting 100 samples in estimated 8.3842 s (10k iterations) | |
| Benchmarking lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str(16)]): ...: Analyzing | |
| lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str(16)]): ... | |
| time: [822.85 µs 823.60 µs 824.42 µs] | |
| change: [-13.445% -13.326% -13.204%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 4 (4.00%) high mild | |
| Benchmarking lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str(16)]): 4096 | |
| Benchmarking lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str(16)]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str(16)]): 4096: Collecting 100 samples in estimated 6.1322 s (15k iterations) | |
| Benchmarking lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str(16)]): 4096: Analyzing | |
| lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str(16)]): 4096 | |
| time: [403.69 µs 403.89 µs 404.12 µs] | |
| change: [+2.9886% +3.0720% +3.1559%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Benchmarking lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str(16)]): ... #2 | |
| Benchmarking lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str(16)]): ... #2: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str(16)]): ... #2: Collecting 100 samples in estimated 5.4377 s (600 iterations) | |
| Benchmarking lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str(16)]): ... #2: Analyzing | |
| lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str(16)]): ... #2 | |
| time: [8.9691 ms 8.9759 ms 8.9832 ms] | |
| change: [-10.220% -10.072% -9.9162%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 5 (5.00%) high mild | |
| Benchmarking lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str(16)]): 32768 | |
| Benchmarking lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str(16)]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str(16)]): 32768: Collecting 100 samples in estimated 5.1062 s (1300 iterations) | |
| Benchmarking lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str(16)]): 32768: Analyzing | |
| lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str(16)]): 32768 | |
| time: [3.9262 ms 3.9285 ms 3.9305 ms] | |
| change: [+0.4805% +0.5472% +0.6043%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 4 (4.00%) low severe | |
| 1 (1.00%) low mild | |
| 4 (4.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)... | |
| Benchmarking lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)...: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)...: Collecting 100 samples in estimated 7.8151 s (10k iterations) | |
| Benchmarking lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)...: Analyzing | |
| lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)... | |
| time: [768.12 µs 770.05 µs 772.22 µs] | |
| change: [-11.956% -11.679% -11.387%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high severe | |
| Benchmarking lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)]): 40... | |
| Benchmarking lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)]): 40...: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)]): 40...: Collecting 100 samples in estimated 5.5892 s (15k iterations) | |
| Benchmarking lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)]): 40...: Analyzing | |
| lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)]): 40... | |
| time: [370.66 µs 370.95 µs 371.25 µs] | |
| change: [-0.6462% -0.5361% -0.4378%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) low mild | |
| Benchmarking lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)... #2 | |
| Benchmarking lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)... #2: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)... #2: Collecting 100 samples in estimated 5.4621 s (600 iterations) | |
| Benchmarking lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)... #2: Analyzing | |
| lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)... #2 | |
| time: [8.9814 ms 9.0052 ms 9.0298 ms] | |
| change: [-7.0362% -6.7729% -6.4609%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)]): 32... | |
| Benchmarking lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)]): 32...: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)]): 32...: Collecting 100 samples in estimated 5.2942 s (1400 iterations) | |
| Benchmarking lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)]): 32...: Analyzing | |
| lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)]): 32... | |
| time: [3.7681 ms 3.7691 ms 3.7703 ms] | |
| change: [-1.1178% -1.0719% -1.0241%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 14 outliers among 100 measurements (14.00%) | |
| 10 (10.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)... #3 | |
| Benchmarking lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)... #3: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)... #3: Collecting 100 samples in estimated 7.6937 s (10k iterations) | |
| Benchmarking lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)... #3: Analyzing | |
| lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)... #3 | |
| time: [754.36 µs 755.28 µs 756.34 µs] | |
| change: [-14.062% -13.862% -13.668%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 14 outliers among 100 measurements (14.00%) | |
| 13 (13.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)]): 40... #2 | |
| Benchmarking lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)]): 40... #2: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)]): 40... #2: Collecting 100 samples in estimated 5.6237 s (15k iterations) | |
| Benchmarking lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)]): 40... #2: Analyzing | |
| lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)]): 40... #2 | |
| time: [372.16 µs 372.72 µs 373.81 µs] | |
| change: [-0.4642% -0.3205% -0.1621%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high severe | |
| Benchmarking lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)... #4 | |
| Benchmarking lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)... #4: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)... #4: Collecting 100 samples in estimated 5.4143 s (600 iterations) | |
| Benchmarking lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)... #4: Analyzing | |
| lexsort_to_indices([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)... #4 | |
| time: [8.9503 ms 8.9621 ms 8.9738 ms] | |
| change: [-7.3778% -7.1880% -7.0044%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)]): 32... #2 | |
| Benchmarking lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)]): 32... #2: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)]): 32... #2: Collecting 100 samples in estimated 5.2714 s (1400 iterations) | |
| Benchmarking lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)]): 32... #2: Analyzing | |
| lexsort_rows([dict(100,str_opt(50)), dict(100,str_opt(50)), dict(100,str_opt(50)), str_opt(50)]): 32... #2 | |
| time: [3.7523 ms 3.7548 ms 3.7573 ms] | |
| change: [-1.3047% -1.2381% -1.1708%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking lexsort_to_indices([i32_opt, i32_list]): 4096 | |
| Benchmarking lexsort_to_indices([i32_opt, i32_list]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([i32_opt, i32_list]): 4096: Collecting 100 samples in estimated 6.3059 s (20k iterations) | |
| Benchmarking lexsort_to_indices([i32_opt, i32_list]): 4096: Analyzing | |
| lexsort_to_indices([i32_opt, i32_list]): 4096 | |
| time: [312.28 µs 313.75 µs 315.46 µs] | |
| change: [-1.2546% -1.0192% -0.6751%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 2 (2.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking lexsort_rows([i32_opt, i32_list]): 4096 | |
| Benchmarking lexsort_rows([i32_opt, i32_list]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([i32_opt, i32_list]): 4096: Collecting 100 samples in estimated 6.9169 s (15k iterations) | |
| Benchmarking lexsort_rows([i32_opt, i32_list]): 4096: Analyzing | |
| lexsort_rows([i32_opt, i32_list]): 4096 | |
| time: [457.73 µs 458.42 µs 459.38 µs] | |
| change: [+0.9454% +1.1723% +1.4206%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high severe | |
| Benchmarking lexsort_to_indices([i32_opt, i32_list]): 32768 | |
| Benchmarking lexsort_to_indices([i32_opt, i32_list]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([i32_opt, i32_list]): 32768: Collecting 100 samples in estimated 5.2854 s (1600 iterations) | |
| Benchmarking lexsort_to_indices([i32_opt, i32_list]): 32768: Analyzing | |
| lexsort_to_indices([i32_opt, i32_list]): 32768 | |
| time: [3.3061 ms 3.3067 ms 3.3075 ms] | |
| change: [-0.6713% -0.6390% -0.6050%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 1 (1.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking lexsort_rows([i32_opt, i32_list]): 32768 | |
| Benchmarking lexsort_rows([i32_opt, i32_list]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([i32_opt, i32_list]): 32768: Collecting 100 samples in estimated 5.3826 s (1200 iterations) | |
| Benchmarking lexsort_rows([i32_opt, i32_list]): 32768: Analyzing | |
| lexsort_rows([i32_opt, i32_list]): 32768 | |
| time: [4.4936 ms 4.4969 ms 4.5000 ms] | |
| change: [+0.1887% +0.2776% +0.3504%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 13 outliers among 100 measurements (13.00%) | |
| 10 (10.00%) low severe | |
| 1 (1.00%) low mild | |
| 2 (2.00%) high mild | |
| Benchmarking lexsort_to_indices([i32_opt, i32_list_opt]): 4096 | |
| Benchmarking lexsort_to_indices([i32_opt, i32_list_opt]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([i32_opt, i32_list_opt]): 4096: Collecting 100 samples in estimated 5.0049 s (15k iterations) | |
| Benchmarking lexsort_to_indices([i32_opt, i32_list_opt]): 4096: Analyzing | |
| lexsort_to_indices([i32_opt, i32_list_opt]): 4096 | |
| time: [329.92 µs 330.14 µs 330.35 µs] | |
| change: [-0.4844% -0.3713% -0.2554%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Benchmarking lexsort_rows([i32_opt, i32_list_opt]): 4096 | |
| Benchmarking lexsort_rows([i32_opt, i32_list_opt]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([i32_opt, i32_list_opt]): 4096: Collecting 100 samples in estimated 6.5911 s (15k iterations) | |
| Benchmarking lexsort_rows([i32_opt, i32_list_opt]): 4096: Analyzing | |
| lexsort_rows([i32_opt, i32_list_opt]): 4096 | |
| time: [438.90 µs 439.84 µs 440.77 µs] | |
| change: [-1.9198% -1.5237% -1.0792%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high severe | |
| Benchmarking lexsort_to_indices([i32_opt, i32_list_opt]): 32768 | |
| Benchmarking lexsort_to_indices([i32_opt, i32_list_opt]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([i32_opt, i32_list_opt]): 32768: Collecting 100 samples in estimated 5.1337 s (1500 iterations) | |
| Benchmarking lexsort_to_indices([i32_opt, i32_list_opt]): 32768: Analyzing | |
| lexsort_to_indices([i32_opt, i32_list_opt]): 32768 | |
| time: [3.4264 ms 3.4272 ms 3.4281 ms] | |
| change: [-1.4995% -1.4617% -1.4262%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 4 (4.00%) high mild | |
| Benchmarking lexsort_rows([i32_opt, i32_list_opt]): 32768 | |
| Benchmarking lexsort_rows([i32_opt, i32_list_opt]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([i32_opt, i32_list_opt]): 32768: Collecting 100 samples in estimated 5.1532 s (1200 iterations) | |
| Benchmarking lexsort_rows([i32_opt, i32_list_opt]): 32768: Analyzing | |
| lexsort_rows([i32_opt, i32_list_opt]): 32768 | |
| time: [4.3131 ms 4.3174 ms 4.3216 ms] | |
| change: [+0.0922% +0.2043% +0.3074%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Benchmarking lexsort_to_indices([i32_list_opt, i32_opt]): 4096 | |
| Benchmarking lexsort_to_indices([i32_list_opt, i32_opt]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([i32_list_opt, i32_opt]): 4096: Collecting 100 samples in estimated 5.4545 s (10k iterations) | |
| Benchmarking lexsort_to_indices([i32_list_opt, i32_opt]): 4096: Analyzing | |
| lexsort_to_indices([i32_list_opt, i32_opt]): 4096 | |
| time: [550.71 µs 550.82 µs 550.93 µs] | |
| change: [-1.1649% -1.0420% -0.9142%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 7 (7.00%) low mild | |
| 1 (1.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking lexsort_rows([i32_list_opt, i32_opt]): 4096 | |
| Benchmarking lexsort_rows([i32_list_opt, i32_opt]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([i32_list_opt, i32_opt]): 4096: Collecting 100 samples in estimated 6.6540 s (15k iterations) | |
| Benchmarking lexsort_rows([i32_list_opt, i32_opt]): 4096: Analyzing | |
| lexsort_rows([i32_list_opt, i32_opt]): 4096 | |
| time: [437.75 µs 438.51 µs 439.13 µs] | |
| change: [+0.5784% +0.7331% +0.8756%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 21 outliers among 100 measurements (21.00%) | |
| 15 (15.00%) low severe | |
| 2 (2.00%) low mild | |
| 2 (2.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking lexsort_to_indices([i32_list_opt, i32_opt]): 32768 | |
| Benchmarking lexsort_to_indices([i32_list_opt, i32_opt]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([i32_list_opt, i32_opt]): 32768: Collecting 100 samples in estimated 5.1014 s (900 iterations) | |
| Benchmarking lexsort_to_indices([i32_list_opt, i32_opt]): 32768: Analyzing | |
| lexsort_to_indices([i32_list_opt, i32_opt]): 32768 | |
| time: [5.6665 ms 5.6678 ms 5.6692 ms] | |
| change: [-2.0676% -2.0246% -1.9822%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 4 (4.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking lexsort_rows([i32_list_opt, i32_opt]): 32768 | |
| Benchmarking lexsort_rows([i32_list_opt, i32_opt]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([i32_list_opt, i32_opt]): 32768: Collecting 100 samples in estimated 5.1294 s (1200 iterations) | |
| Benchmarking lexsort_rows([i32_list_opt, i32_opt]): 32768: Analyzing | |
| lexsort_rows([i32_list_opt, i32_opt]): 32768 | |
| time: [4.2860 ms 4.2905 ms 4.2950 ms] | |
| change: [-1.1149% -0.8469% -0.6446%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Benchmarking lexsort_to_indices([i32, str_list(4)]): 4096 | |
| Benchmarking lexsort_to_indices([i32, str_list(4)]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([i32, str_list(4)]): 4096: Collecting 100 samples in estimated 5.7700 s (30k iterations) | |
| Benchmarking lexsort_to_indices([i32, str_list(4)]): 4096: Analyzing | |
| lexsort_to_indices([i32, str_list(4)]): 4096 | |
| time: [190.49 µs 190.78 µs 191.16 µs] | |
| change: [-2.8667% -2.4861% -2.1481%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 1 (1.00%) low severe | |
| 5 (5.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking lexsort_rows([i32, str_list(4)]): 4096 | |
| Benchmarking lexsort_rows([i32, str_list(4)]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([i32, str_list(4)]): 4096: Collecting 100 samples in estimated 8.4116 s (10k iterations) | |
| Benchmarking lexsort_rows([i32, str_list(4)]): 4096: Analyzing | |
| lexsort_rows([i32, str_list(4)]): 4096 | |
| time: [830.05 µs 830.46 µs 830.85 µs] | |
| change: [-2.5669% -2.3694% -2.1989%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 2 (2.00%) low mild | |
| 1 (1.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking lexsort_to_indices([i32, str_list(4)]): 32768 | |
| Benchmarking lexsort_to_indices([i32, str_list(4)]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([i32, str_list(4)]): 32768: Collecting 100 samples in estimated 5.0983 s (2500 iterations) | |
| Benchmarking lexsort_to_indices([i32, str_list(4)]): 32768: Analyzing | |
| lexsort_to_indices([i32, str_list(4)]): 32768 | |
| time: [2.0458 ms 2.0462 ms 2.0465 ms] | |
| change: [-0.0992% -0.0743% -0.0518%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 1 (1.00%) low mild | |
| 5 (5.00%) high severe | |
| Benchmarking lexsort_rows([i32, str_list(4)]): 32768 | |
| Benchmarking lexsort_rows([i32, str_list(4)]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([i32, str_list(4)]): 32768: Collecting 100 samples in estimated 5.1855 s (600 iterations) | |
| Benchmarking lexsort_rows([i32, str_list(4)]): 32768: Analyzing | |
| lexsort_rows([i32, str_list(4)]): 32768 | |
| time: [8.4694 ms 8.5344 ms 8.6144 ms] | |
| change: [-4.7984% -3.7893% -2.7279%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 14 outliers among 100 measurements (14.00%) | |
| 3 (3.00%) high mild | |
| 11 (11.00%) high severe | |
| Benchmarking lexsort_to_indices([str_list(4), i32]): 4096 | |
| Benchmarking lexsort_to_indices([str_list(4), i32]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([str_list(4), i32]): 4096: Collecting 100 samples in estimated 6.2874 s (10k iterations) | |
| Benchmarking lexsort_to_indices([str_list(4), i32]): 4096: Analyzing | |
| lexsort_to_indices([str_list(4), i32]): 4096 | |
| time: [623.00 µs 623.16 µs 623.32 µs] | |
| change: [-1.9962% -1.9518% -1.9123%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 5 (5.00%) high mild | |
| Benchmarking lexsort_rows([str_list(4), i32]): 4096 | |
| Benchmarking lexsort_rows([str_list(4), i32]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([str_list(4), i32]): 4096: Collecting 100 samples in estimated 7.8438 s (10k iterations) | |
| Benchmarking lexsort_rows([str_list(4), i32]): 4096: Analyzing | |
| lexsort_rows([str_list(4), i32]): 4096 | |
| time: [778.12 µs 778.27 µs 778.41 µs] | |
| change: [-3.0695% -3.0086% -2.9548%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 7 (7.00%) low mild | |
| 2 (2.00%) high mild | |
| Benchmarking lexsort_to_indices([str_list(4), i32]): 32768 | |
| Benchmarking lexsort_to_indices([str_list(4), i32]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([str_list(4), i32]): 32768: Collecting 100 samples in estimated 5.4057 s (700 iterations) | |
| Benchmarking lexsort_to_indices([str_list(4), i32]): 32768: Analyzing | |
| lexsort_to_indices([str_list(4), i32]): 32768 | |
| time: [7.7305 ms 7.7350 ms 7.7405 ms] | |
| change: [-2.5542% -2.1934% -1.9315%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 2 (2.00%) high mild | |
| 10 (10.00%) high severe | |
| Benchmarking lexsort_rows([str_list(4), i32]): 32768 | |
| Benchmarking lexsort_rows([str_list(4), i32]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([str_list(4), i32]): 32768: Collecting 100 samples in estimated 5.6676 s (700 iterations) | |
| Benchmarking lexsort_rows([str_list(4), i32]): 32768: Analyzing | |
| lexsort_rows([str_list(4), i32]): 32768 | |
| time: [7.9905 ms 8.0548 ms 8.1316 ms] | |
| change: [-6.0811% -4.9350% -3.7595%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 1 (1.00%) high mild | |
| 11 (11.00%) high severe | |
| Benchmarking lexsort_to_indices([i32, str_list_opt(4)]): 4096 | |
| Benchmarking lexsort_to_indices([i32, str_list_opt(4)]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([i32, str_list_opt(4)]): 4096: Collecting 100 samples in estimated 5.8011 s (30k iterations) | |
| Benchmarking lexsort_to_indices([i32, str_list_opt(4)]): 4096: Analyzing | |
| lexsort_to_indices([i32, str_list_opt(4)]): 4096 | |
| time: [191.93 µs 192.20 µs 192.78 µs] | |
| change: [-1.8626% -1.3538% -0.9669%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 4 (4.00%) high mild | |
| 8 (8.00%) high severe | |
| Benchmarking lexsort_rows([i32, str_list_opt(4)]): 4096 | |
| Benchmarking lexsort_rows([i32, str_list_opt(4)]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([i32, str_list_opt(4)]): 4096: Collecting 100 samples in estimated 8.1435 s (10k iterations) | |
| Benchmarking lexsort_rows([i32, str_list_opt(4)]): 4096: Analyzing | |
| lexsort_rows([i32, str_list_opt(4)]): 4096 | |
| time: [805.86 µs 806.31 µs 806.74 µs] | |
| change: [-1.4153% -1.3158% -1.2365%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high mild | |
| Benchmarking lexsort_to_indices([i32, str_list_opt(4)]): 32768 | |
| Benchmarking lexsort_to_indices([i32, str_list_opt(4)]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([i32, str_list_opt(4)]): 32768: Collecting 100 samples in estimated 5.1028 s (2500 iterations) | |
| Benchmarking lexsort_to_indices([i32, str_list_opt(4)]): 32768: Analyzing | |
| lexsort_to_indices([i32, str_list_opt(4)]): 32768 | |
| time: [2.0422 ms 2.0426 ms 2.0431 ms] | |
| change: [-0.0534% +0.3974% +0.7052%] (p = 0.03 < 0.05) | |
| Change within noise threshold. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 3 (3.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking lexsort_rows([i32, str_list_opt(4)]): 32768 | |
| Benchmarking lexsort_rows([i32, str_list_opt(4)]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([i32, str_list_opt(4)]): 32768: Collecting 100 samples in estimated 5.3820 s (700 iterations) | |
| Benchmarking lexsort_rows([i32, str_list_opt(4)]): 32768: Analyzing | |
| lexsort_rows([i32, str_list_opt(4)]): 32768 | |
| time: [7.6375 ms 7.6618 ms 7.6922 ms] | |
| change: [-3.0167% -2.5420% -2.0373%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 1 (1.00%) high mild | |
| 9 (9.00%) high severe | |
| Benchmarking lexsort_to_indices([str_list_opt(4), i32]): 4096 | |
| Benchmarking lexsort_to_indices([str_list_opt(4), i32]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([str_list_opt(4), i32]): 4096: Collecting 100 samples in estimated 7.1270 s (10k iterations) | |
| Benchmarking lexsort_to_indices([str_list_opt(4), i32]): 4096: Analyzing | |
| lexsort_to_indices([str_list_opt(4), i32]): 4096 | |
| time: [705.52 µs 705.96 µs 706.77 µs] | |
| change: [-3.2089% -3.0565% -2.7588%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 1 (1.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking lexsort_rows([str_list_opt(4), i32]): 4096 | |
| Benchmarking lexsort_rows([str_list_opt(4), i32]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([str_list_opt(4), i32]): 4096: Collecting 100 samples in estimated 7.4235 s (10k iterations) | |
| Benchmarking lexsort_rows([str_list_opt(4), i32]): 4096: Analyzing | |
| lexsort_rows([str_list_opt(4), i32]): 4096 | |
| time: [734.73 µs 735.13 µs 735.56 µs] | |
| change: [-2.2613% -2.2102% -2.1632%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking lexsort_to_indices([str_list_opt(4), i32]): 32768 | |
| Benchmarking lexsort_to_indices([str_list_opt(4), i32]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([str_list_opt(4), i32]): 32768: Collecting 100 samples in estimated 5.6880 s (700 iterations) | |
| Benchmarking lexsort_to_indices([str_list_opt(4), i32]): 32768: Analyzing | |
| lexsort_to_indices([str_list_opt(4), i32]): 32768 | |
| time: [8.1258 ms 8.1278 ms 8.1298 ms] | |
| change: [-2.3411% -2.3078% -2.2741%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking lexsort_rows([str_list_opt(4), i32]): 32768 | |
| Benchmarking lexsort_rows([str_list_opt(4), i32]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([str_list_opt(4), i32]): 32768: Collecting 100 samples in estimated 5.5774 s (800 iterations) | |
| Benchmarking lexsort_rows([str_list_opt(4), i32]): 32768: Analyzing | |
| lexsort_rows([str_list_opt(4), i32]): 32768 | |
| time: [6.9293 ms 6.9525 ms 6.9814 ms] | |
| change: [-3.5284% -2.8886% -2.2507%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 16 outliers among 100 measurements (16.00%) | |
| 7 (7.00%) high mild | |
| 9 (9.00%) high severe | |
| Benchmarking lexsort_to_indices([i32, i32_list, str(16)]): 4096 | |
| Benchmarking lexsort_to_indices([i32, i32_list, str(16)]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([i32, i32_list, str(16)]): 4096: Collecting 100 samples in estimated 5.8530 s (30k iterations) | |
| Benchmarking lexsort_to_indices([i32, i32_list, str(16)]): 4096: Analyzing | |
| lexsort_to_indices([i32, i32_list, str(16)]): 4096 | |
| time: [192.58 µs 192.77 µs 193.07 µs] | |
| change: [-0.5870% -0.1481% +0.2382%] (p = 0.51 > 0.05) | |
| No change in performance detected. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 2 (2.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking lexsort_rows([i32, i32_list, str(16)]): 4096 | |
| Benchmarking lexsort_rows([i32, i32_list, str(16)]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([i32, i32_list, str(16)]): 4096: Collecting 100 samples in estimated 6.8873 s (15k iterations) | |
| Benchmarking lexsort_rows([i32, i32_list, str(16)]): 4096: Analyzing | |
| lexsort_rows([i32, i32_list, str(16)]): 4096 | |
| time: [455.09 µs 456.83 µs 458.97 µs] | |
| change: [+2.2151% +2.4529% +2.7146%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 3 (3.00%) high severe | |
| Benchmarking lexsort_to_indices([i32, i32_list, str(16)]): 32768 | |
| Benchmarking lexsort_to_indices([i32, i32_list, str(16)]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([i32, i32_list, str(16)]): 32768: Collecting 100 samples in estimated 5.0668 s (2500 iterations) | |
| Benchmarking lexsort_to_indices([i32, i32_list, str(16)]): 32768: Analyzing | |
| lexsort_to_indices([i32, i32_list, str(16)]): 32768 | |
| time: [2.0294 ms 2.0297 ms 2.0301 ms] | |
| change: [+0.1690% +0.1910% +0.2133%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 16 outliers among 100 measurements (16.00%) | |
| 7 (7.00%) low mild | |
| 4 (4.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking lexsort_rows([i32, i32_list, str(16)]): 32768 | |
| Benchmarking lexsort_rows([i32, i32_list, str(16)]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([i32, i32_list, str(16)]): 32768: Collecting 100 samples in estimated 5.3145 s (1200 iterations) | |
| Benchmarking lexsort_rows([i32, i32_list, str(16)]): 32768: Analyzing | |
| lexsort_rows([i32, i32_list, str(16)]): 32768 | |
| time: [4.4283 ms 4.4343 ms 4.4453 ms] | |
| change: [-0.8980% -0.5961% -0.2726%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 13 outliers among 100 measurements (13.00%) | |
| 3 (3.00%) low mild | |
| 3 (3.00%) high mild | |
| 7 (7.00%) high severe | |
| Benchmarking lexsort_to_indices([i32_opt, i32_list_opt, str_opt(50)]): 4096 | |
| Benchmarking lexsort_to_indices([i32_opt, i32_list_opt, str_opt(50)]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([i32_opt, i32_list_opt, str_opt(50)]): 4096: Collecting 100 samples in estimated 5.3861 s (15k iterations) | |
| Benchmarking lexsort_to_indices([i32_opt, i32_list_opt, str_opt(50)]): 4096: Analyzing | |
| lexsort_to_indices([i32_opt, i32_list_opt, str_opt(50)]): 4096 | |
| time: [355.92 µs 356.90 µs 357.81 µs] | |
| change: [-5.5134% -5.3224% -5.1368%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 19 outliers among 100 measurements (19.00%) | |
| 16 (16.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking lexsort_rows([i32_opt, i32_list_opt, str_opt(50)]): 4096 | |
| Benchmarking lexsort_rows([i32_opt, i32_list_opt, str_opt(50)]): 4096: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([i32_opt, i32_list_opt, str_opt(50)]): 4096: Collecting 100 samples in estimated 6.0438 s (15k iterations) | |
| Benchmarking lexsort_rows([i32_opt, i32_list_opt, str_opt(50)]): 4096: Analyzing | |
| lexsort_rows([i32_opt, i32_list_opt, str_opt(50)]): 4096 | |
| time: [399.36 µs 399.69 µs 400.02 µs] | |
| change: [-5.5841% -5.5040% -5.4268%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 4 (4.00%) low mild | |
| Benchmarking lexsort_to_indices([i32_opt, i32_list_opt, str_opt(50)]): 32768 | |
| Benchmarking lexsort_to_indices([i32_opt, i32_list_opt, str_opt(50)]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_to_indices([i32_opt, i32_list_opt, str_opt(50)]): 32768: Collecting 100 samples in estimated 5.0298 s (1300 iterations) | |
| Benchmarking lexsort_to_indices([i32_opt, i32_list_opt, str_opt(50)]): 32768: Analyzing | |
| lexsort_to_indices([i32_opt, i32_list_opt, str_opt(50)]): 32768 | |
| time: [3.8610 ms 3.8626 ms 3.8641 ms] | |
| change: [-2.6625% -2.5960% -2.5291%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking lexsort_rows([i32_opt, i32_list_opt, str_opt(50)]): 32768 | |
| Benchmarking lexsort_rows([i32_opt, i32_list_opt, str_opt(50)]): 32768: Warming up for 3.0000 s | |
| Benchmarking lexsort_rows([i32_opt, i32_list_opt, str_opt(50)]): 32768: Collecting 100 samples in estimated 5.2593 s (1300 iterations) | |
| Benchmarking lexsort_rows([i32_opt, i32_list_opt, str_opt(50)]): 32768: Analyzing | |
| lexsort_rows([i32_opt, i32_list_opt, str_opt(50)]): 32768 | |
| time: [4.0486 ms 4.0493 ms 4.0500 ms] | |
| change: [-3.6985% -3.6617% -3.6278%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 7 (7.00%) high mild | |
| 2 (2.00%) high severe | |
| Running benches/mutable_array.rs (target/x86_64-unknown-linux-gnu/release/deps/mutable_array-0f11ef9dbed14587) | |
| Benchmarking mutable str 1024 | |
| Benchmarking mutable str 1024: Warming up for 3.0000 s | |
| Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 8.5s, or reduce sample count to 50. | |
| Benchmarking mutable str 1024: Collecting 100 samples in estimated 8.5152 s (100 iterations) | |
| Benchmarking mutable str 1024: Analyzing | |
| mutable str 1024 time: [84.472 ms 84.691 ms 84.947 ms] | |
| change: [-5.2945% -4.8707% -4.4597%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 11 (11.00%) high severe | |
| Benchmarking mutable str nulls 1024 | |
| Benchmarking mutable str nulls 1024: Warming up for 3.0000 s | |
| Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 5.5s, or reduce sample count to 90. | |
| Benchmarking mutable str nulls 1024: Collecting 100 samples in estimated 5.4912 s (100 iterations) | |
| Benchmarking mutable str nulls 1024: Analyzing | |
| mutable str nulls 1024 time: [55.224 ms 55.366 ms 55.545 ms] | |
| change: [+50.013% +51.269% +52.328%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 1 (1.00%) high mild | |
| 6 (6.00%) high severe | |
| Running benches/partition_kernels.rs (target/x86_64-unknown-linux-gnu/release/deps/partition_kernels-30e46b6e24481489) | |
| Benchmarking partition(u8) 2^10 | |
| Benchmarking partition(u8) 2^10: Warming up for 3.0000 s | |
| Benchmarking partition(u8) 2^10: Collecting 100 samples in estimated 5.0088 s (1.8M iterations) | |
| Benchmarking partition(u8) 2^10: Analyzing | |
| partition(u8) 2^10 time: [2.8316 µs 2.8329 µs 2.8343 µs] | |
| change: [-5.8751% -5.8210% -5.7706%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 2 (2.00%) low mild | |
| 1 (1.00%) high mild | |
| Benchmarking partition(u8) 2^12 | |
| Benchmarking partition(u8) 2^12: Warming up for 3.0000 s | |
| Benchmarking partition(u8) 2^12: Collecting 100 samples in estimated 5.0002 s (712k iterations) | |
| Benchmarking partition(u8) 2^12: Analyzing | |
| partition(u8) 2^12 time: [7.0087 µs 7.0201 µs 7.0432 µs] | |
| change: [-2.5335% -2.4585% -2.2971%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 1 (1.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking partition(u8) 2^10 with nulls | |
| Benchmarking partition(u8) 2^10 with nulls: Warming up for 3.0000 s | |
| Benchmarking partition(u8) 2^10 with nulls: Collecting 100 samples in estimated 5.0018 s (1.7M iterations) | |
| Benchmarking partition(u8) 2^10 with nulls: Analyzing | |
| partition(u8) 2^10 with nulls | |
| time: [2.9880 µs 2.9887 µs 2.9896 µs] | |
| change: [-6.9502% -6.7401% -6.5991%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 3 (3.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking partition(u8) 2^12 with nulls | |
| Benchmarking partition(u8) 2^12 with nulls: Warming up for 3.0000 s | |
| Benchmarking partition(u8) 2^12 with nulls: Collecting 100 samples in estimated 5.0031 s (662k iterations) | |
| Benchmarking partition(u8) 2^12 with nulls: Analyzing | |
| partition(u8) 2^12 with nulls | |
| time: [7.7635 µs 7.7703 µs 7.7765 µs] | |
| change: [-1.7629% -1.5428% -1.3422%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking partition(f64) 2^10 | |
| Benchmarking partition(f64) 2^10: Warming up for 3.0000 s | |
| Benchmarking partition(f64) 2^10: Collecting 100 samples in estimated 5.0057 s (1.0M iterations) | |
| Benchmarking partition(f64) 2^10: Analyzing | |
| partition(f64) 2^10 time: [4.8615 µs 4.8626 µs 4.8638 µs] | |
| change: [-2.9955% -2.9673% -2.9384%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 6 (6.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking partition(low cardinality) 1024 | |
| Benchmarking partition(low cardinality) 1024: Warming up for 3.0000 s | |
| Benchmarking partition(low cardinality) 1024: Collecting 100 samples in estimated 5.0034 s (7.4M iterations) | |
| Benchmarking partition(low cardinality) 1024: Analyzing | |
| partition(low cardinality) 1024 | |
| time: [680.21 ns 681.30 ns 683.31 ns] | |
| change: [-10.030% -9.9320% -9.7785%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high severe | |
| Running benches/primitive_run_accessor.rs (target/x86_64-unknown-linux-gnu/release/deps/primitive_run_accessor-9da330c1445282c0) | |
| Benchmarking primitive_run_accessor/(run_array_len:512, physical_array_len:128) | |
| Benchmarking primitive_run_accessor/(run_array_len:512, physical_array_len:128): Warming up for 3.0000 s | |
| Benchmarking primitive_run_accessor/(run_array_len:512, physical_array_len:128): Collecting 100 samples in estimated 5.0151 s (1.3M iterations) | |
| Benchmarking primitive_run_accessor/(run_array_len:512, physical_array_len:128): Analyzing | |
| primitive_run_accessor/(run_array_len:512, physical_array_len:128) | |
| time: [3.7766 µs 3.7778 µs 3.7791 µs] | |
| change: [+2.2570% +2.6889% +3.0862%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Benchmarking primitive_run_accessor/(run_array_len:1024, physical_array_len:256) | |
| Benchmarking primitive_run_accessor/(run_array_len:1024, physical_array_len:256): Warming up for 3.0000 s | |
| Benchmarking primitive_run_accessor/(run_array_len:1024, physical_array_len:256): Collecting 100 samples in estimated 5.0033 s (550k iterations) | |
| Benchmarking primitive_run_accessor/(run_array_len:1024, physical_array_len:256): Analyzing | |
| primitive_run_accessor/(run_array_len:1024, physical_array_len:256) | |
| time: [9.0884 µs 9.0906 µs 9.0932 µs] | |
| change: [-2.3086% -2.2743% -2.2420%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 5 (5.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking primitive_run_accessor/(run_array_len:2048, physical_array_len:512) | |
| Benchmarking primitive_run_accessor/(run_array_len:2048, physical_array_len:512): Warming up for 3.0000 s | |
| Benchmarking primitive_run_accessor/(run_array_len:2048, physical_array_len:512): Collecting 100 samples in estimated 5.0009 s (232k iterations) | |
| Benchmarking primitive_run_accessor/(run_array_len:2048, physical_array_len:512): Analyzing | |
| primitive_run_accessor/(run_array_len:2048, physical_array_len:512) | |
| time: [21.523 µs 21.526 µs 21.530 µs] | |
| change: [-1.2249% -1.2057% -1.1858%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 5 (5.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking primitive_run_accessor/(run_array_len:4096, physical_array_len:1024) | |
| Benchmarking primitive_run_accessor/(run_array_len:4096, physical_array_len:1024): Warming up for 3.0000 s | |
| Benchmarking primitive_run_accessor/(run_array_len:4096, physical_array_len:1024): Collecting 100 samples in estimated 5.1299 s (101k iterations) | |
| Benchmarking primitive_run_accessor/(run_array_len:4096, physical_array_len:1024): Analyzing | |
| primitive_run_accessor/(run_array_len:4096, physical_array_len:1024) | |
| time: [50.777 µs 50.824 µs 50.921 µs] | |
| change: [-0.3809% -0.2651% +0.0099%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 14 outliers among 100 measurements (14.00%) | |
| 8 (8.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking primitive_run_accessor/(run_array_len:8192, physical_array_len:2048) | |
| Benchmarking primitive_run_accessor/(run_array_len:8192, physical_array_len:2048): Warming up for 3.0000 s | |
| Benchmarking primitive_run_accessor/(run_array_len:8192, physical_array_len:2048): Collecting 100 samples in estimated 5.2782 s (45k iterations) | |
| Benchmarking primitive_run_accessor/(run_array_len:8192, physical_array_len:2048): Analyzing | |
| primitive_run_accessor/(run_array_len:8192, physical_array_len:2048) | |
| time: [116.07 µs 116.09 µs 116.12 µs] | |
| change: [-1.1024% -1.0776% -1.0488%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 13 outliers among 100 measurements (13.00%) | |
| 1 (1.00%) low severe | |
| 4 (4.00%) high mild | |
| 8 (8.00%) high severe | |
| Running benches/primitive_run_take.rs (target/x86_64-unknown-linux-gnu/release/deps/primitive_run_take-79a336be390b4b8b) | |
| Benchmarking primitive_run_take/(run_array_len:512, physical_array_len:64, take_len:512) | |
| Benchmarking primitive_run_take/(run_array_len:512, physical_array_len:64, take_len:512): Warming up for 3.0000 s | |
| Benchmarking primitive_run_take/(run_array_len:512, physical_array_len:64, take_len:512): Collecting 100 samples in estimated 5.0053 s (449k iterations) | |
| Benchmarking primitive_run_take/(run_array_len:512, physical_array_len:64, take_len:512): Analyzing | |
| primitive_run_take/(run_array_len:512, physical_array_len:64, take_len:512) | |
| time: [13.028 µs 13.052 µs 13.075 µs] | |
| change: [+51.869% +52.754% +53.360%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 1 (1.00%) low severe | |
| 1 (1.00%) low mild | |
| Benchmarking primitive_run_take/(run_array_len:512, physical_array_len:128, take_len:512) | |
| Benchmarking primitive_run_take/(run_array_len:512, physical_array_len:128, take_len:512): Warming up for 3.0000 s | |
| Benchmarking primitive_run_take/(run_array_len:512, physical_array_len:128, take_len:512): Collecting 100 samples in estimated 5.0080 s (384k iterations) | |
| Benchmarking primitive_run_take/(run_array_len:512, physical_array_len:128, take_len:512): Analyzing | |
| primitive_run_take/(run_array_len:512, physical_array_len:128, take_len:512) | |
| time: [13.219 µs 13.239 µs 13.258 µs] | |
| change: [+46.938% +47.210% +47.471%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) low mild | |
| Benchmarking primitive_run_take/(run_array_len:1024, physical_array_len:256, take_len:512) | |
| Benchmarking primitive_run_take/(run_array_len:1024, physical_array_len:256, take_len:512): Warming up for 3.0000 s | |
| Benchmarking primitive_run_take/(run_array_len:1024, physical_array_len:256, take_len:512): Collecting 100 samples in estimated 5.0434 s (490k iterations) | |
| Benchmarking primitive_run_take/(run_array_len:1024, physical_array_len:256, take_len:512): Analyzing | |
| primitive_run_take/(run_array_len:1024, physical_array_len:256, take_len:512) | |
| time: [10.617 µs 10.770 µs 10.943 µs] | |
| change: [+13.750% +14.337% +15.038%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 19 outliers among 100 measurements (19.00%) | |
| 7 (7.00%) low severe | |
| 5 (5.00%) low mild | |
| 2 (2.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking primitive_run_take/(run_array_len:1024, physical_array_len:256, take_len:1024) | |
| Benchmarking primitive_run_take/(run_array_len:1024, physical_array_len:256, take_len:1024): Warming up for 3.0000 s | |
| Benchmarking primitive_run_take/(run_array_len:1024, physical_array_len:256, take_len:1024): Collecting 100 samples in estimated 5.0669 s (197k iterations) | |
| Benchmarking primitive_run_take/(run_array_len:1024, physical_array_len:256, take_len:1024): Analyzing | |
| primitive_run_take/(run_array_len:1024, physical_array_len:256, take_len:1024) | |
| time: [26.224 µs 26.384 µs 26.540 µs] | |
| change: [+46.047% +47.039% +47.905%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) low mild | |
| Benchmarking primitive_run_take/(run_array_len:2048, physical_array_len:512, take_len:512) | |
| Benchmarking primitive_run_take/(run_array_len:2048, physical_array_len:512, take_len:512): Warming up for 3.0000 s | |
| Benchmarking primitive_run_take/(run_array_len:2048, physical_array_len:512, take_len:512): Collecting 100 samples in estimated 5.0071 s (369k iterations) | |
| Benchmarking primitive_run_take/(run_array_len:2048, physical_array_len:512, take_len:512): Analyzing | |
| primitive_run_take/(run_array_len:2048, physical_array_len:512, take_len:512) | |
| time: [13.775 µs 13.779 µs 13.783 µs] | |
| change: [+47.391% +47.634% +47.848%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 2 (2.00%) low severe | |
| 1 (1.00%) low mild | |
| 3 (3.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking primitive_run_take/(run_array_len:2048, physical_array_len:512, take_len:1024) | |
| Benchmarking primitive_run_take/(run_array_len:2048, physical_array_len:512, take_len:1024): Warming up for 3.0000 s | |
| Benchmarking primitive_run_take/(run_array_len:2048, physical_array_len:512, take_len:1024): Collecting 100 samples in estimated 5.0337 s (182k iterations) | |
| Benchmarking primitive_run_take/(run_array_len:2048, physical_array_len:512, take_len:1024): Analyzing | |
| primitive_run_take/(run_array_len:2048, physical_array_len:512, take_len:1024) | |
| time: [27.923 µs 28.004 µs 28.073 µs] | |
| change: [+51.478% +51.849% +52.254%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 15 outliers among 100 measurements (15.00%) | |
| 8 (8.00%) low severe | |
| 7 (7.00%) low mild | |
| Benchmarking primitive_run_take/(run_array_len:4096, physical_array_len:1024, take_len:512) | |
| Benchmarking primitive_run_take/(run_array_len:4096, physical_array_len:1024, take_len:512): Warming up for 3.0000 s | |
| Benchmarking primitive_run_take/(run_array_len:4096, physical_array_len:1024, take_len:512): Collecting 100 samples in estimated 5.0007 s (359k iterations) | |
| Benchmarking primitive_run_take/(run_array_len:4096, physical_array_len:1024, take_len:512): Analyzing | |
| primitive_run_take/(run_array_len:4096, physical_array_len:1024, take_len:512) | |
| time: [14.181 µs 14.258 µs 14.331 µs] | |
| change: [+40.941% +41.755% +42.461%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 2 (2.00%) low severe | |
| 6 (6.00%) low mild | |
| Benchmarking primitive_run_take/(run_array_len:4096, physical_array_len:1024, take_len:1024) | |
| Benchmarking primitive_run_take/(run_array_len:4096, physical_array_len:1024, take_len:1024): Warming up for 3.0000 s | |
| Benchmarking primitive_run_take/(run_array_len:4096, physical_array_len:1024, take_len:1024): Collecting 100 samples in estimated 5.0232 s (227k iterations) | |
| Benchmarking primitive_run_take/(run_array_len:4096, physical_array_len:1024, take_len:1024): Analyzing | |
| primitive_run_take/(run_array_len:4096, physical_array_len:1024, take_len:1024) | |
| time: [22.420 µs 22.445 µs 22.471 µs] | |
| change: [+18.449% +18.564% +18.685%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Running benches/regexp_kernels.rs (target/x86_64-unknown-linux-gnu/release/deps/regexp_kernels-d1ef7fce765c71fb) | |
| Benchmarking regexp | |
| Benchmarking regexp: Warming up for 3.0000 s | |
| Benchmarking regexp: Collecting 100 samples in estimated 5.4640 s (800 iterations) | |
| Benchmarking regexp: Analyzing | |
| regexp time: [6.7789 ms 6.8008 ms 6.8281 ms] | |
| change: [-6.6770% -6.1447% -5.6448%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 1 (1.00%) high mild | |
| 7 (7.00%) high severe | |
| Benchmarking regexp scalar | |
| Benchmarking regexp scalar: Warming up for 3.0000 s | |
| Benchmarking regexp scalar: Collecting 100 samples in estimated 5.1095 s (1100 iterations) | |
| Benchmarking regexp scalar: Analyzing | |
| regexp scalar time: [4.6169 ms 4.6391 ms 4.6661 ms] | |
| change: [+0.4608% +1.3214% +2.2001%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 13 outliers among 100 measurements (13.00%) | |
| 5 (5.00%) high mild | |
| 8 (8.00%) high severe | |
| Running benches/row_format.rs (target/x86_64-unknown-linux-gnu/release/deps/row_format-3780c95d49e330bd) | |
| Benchmarking convert_columns 4096 u64(0) | |
| Benchmarking convert_columns 4096 u64(0): Warming up for 3.0000 s | |
| Benchmarking convert_columns 4096 u64(0): Collecting 100 samples in estimated 5.0303 s (298k iterations) | |
| Benchmarking convert_columns 4096 u64(0): Analyzing | |
| convert_columns 4096 u64(0) | |
| time: [16.877 µs 16.880 µs 16.884 µs] | |
| change: [+36.309% +36.342% +36.374%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 9 (9.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking convert_columns_prepared 4096 u64(0) | |
| Benchmarking convert_columns_prepared 4096 u64(0): Warming up for 3.0000 s | |
| Benchmarking convert_columns_prepared 4096 u64(0): Collecting 100 samples in estimated 5.0425 s (354k iterations) | |
| Benchmarking convert_columns_prepared 4096 u64(0): Analyzing | |
| convert_columns_prepared 4096 u64(0) | |
| time: [14.248 µs 14.250 µs 14.253 µs] | |
| change: [+15.570% +15.642% +15.697%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 5 (5.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking convert_rows 4096 u64(0) | |
| Benchmarking convert_rows 4096 u64(0): Warming up for 3.0000 s | |
| Benchmarking convert_rows 4096 u64(0): Collecting 100 samples in estimated 5.0743 s (182k iterations) | |
| Benchmarking convert_rows 4096 u64(0): Analyzing | |
| convert_rows 4096 u64(0) | |
| time: [27.308 µs 27.346 µs 27.378 µs] | |
| change: [-7.7447% -7.5793% -7.4317%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking convert_columns 4096 u64(0.3) | |
| Benchmarking convert_columns 4096 u64(0.3): Warming up for 3.0000 s | |
| Benchmarking convert_columns 4096 u64(0.3): Collecting 100 samples in estimated 5.0132 s (404k iterations) | |
| Benchmarking convert_columns 4096 u64(0.3): Analyzing | |
| convert_columns 4096 u64(0.3) | |
| time: [12.425 µs 12.427 µs 12.430 µs] | |
| change: [+0.4383% +0.7956% +1.0017%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 1 (1.00%) low severe | |
| 1 (1.00%) low mild | |
| 5 (5.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking convert_columns_prepared 4096 u64(0.3) | |
| Benchmarking convert_columns_prepared 4096 u64(0.3): Warming up for 3.0000 s | |
| Benchmarking convert_columns_prepared 4096 u64(0.3): Collecting 100 samples in estimated 5.0019 s (409k iterations) | |
| Benchmarking convert_columns_prepared 4096 u64(0.3): Analyzing | |
| convert_columns_prepared 4096 u64(0.3) | |
| time: [12.182 µs 12.184 µs 12.187 µs] | |
| change: [-1.4026% -1.2489% -1.1595%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 5 (5.00%) low mild | |
| 6 (6.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking convert_rows 4096 u64(0.3) | |
| Benchmarking convert_rows 4096 u64(0.3): Warming up for 3.0000 s | |
| Benchmarking convert_rows 4096 u64(0.3): Collecting 100 samples in estimated 5.1207 s (182k iterations) | |
| Benchmarking convert_rows 4096 u64(0.3): Analyzing | |
| convert_rows 4096 u64(0.3) | |
| time: [29.325 µs 29.355 µs 29.384 µs] | |
| change: [-0.0494% +0.1190% +0.2709%] (p = 0.13 > 0.05) | |
| No change in performance detected. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 1 (1.00%) low severe | |
| 1 (1.00%) low mild | |
| 2 (2.00%) high mild | |
| Benchmarking convert_columns 4096 i64(0) | |
| Benchmarking convert_columns 4096 i64(0): Warming up for 3.0000 s | |
| Benchmarking convert_columns 4096 i64(0): Collecting 100 samples in estimated 5.0902 s (268k iterations) | |
| Benchmarking convert_columns 4096 i64(0): Analyzing | |
| convert_columns 4096 i64(0) | |
| time: [19.044 µs 19.074 µs 19.136 µs] | |
| change: [+54.167% +54.635% +54.937%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 14 outliers among 100 measurements (14.00%) | |
| 1 (1.00%) low severe | |
| 4 (4.00%) low mild | |
| 6 (6.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking convert_columns_prepared 4096 i64(0) | |
| Benchmarking convert_columns_prepared 4096 i64(0): Warming up for 3.0000 s | |
| Benchmarking convert_columns_prepared 4096 i64(0): Collecting 100 samples in estimated 5.0603 s (268k iterations) | |
| Benchmarking convert_columns_prepared 4096 i64(0): Analyzing | |
| convert_columns_prepared 4096 i64(0) | |
| time: [18.930 µs 18.937 µs 18.942 µs] | |
| change: [+53.434% +53.485% +53.530%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 3 (3.00%) low severe | |
| 3 (3.00%) low mild | |
| 4 (4.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking convert_rows 4096 i64(0) | |
| Benchmarking convert_rows 4096 i64(0): Warming up for 3.0000 s | |
| Benchmarking convert_rows 4096 i64(0): Collecting 100 samples in estimated 5.0063 s (162k iterations) | |
| Benchmarking convert_rows 4096 i64(0): Analyzing | |
| convert_rows 4096 i64(0) | |
| time: [30.081 µs 30.107 µs 30.130 µs] | |
| change: [+0.9355% +1.3044% +1.5709%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 2 (2.00%) low mild | |
| 3 (3.00%) high mild | |
| Benchmarking convert_columns 4096 i64(0.3) | |
| Benchmarking convert_columns 4096 i64(0.3): Warming up for 3.0000 s | |
| Benchmarking convert_columns 4096 i64(0.3): Collecting 100 samples in estimated 5.0037 s (404k iterations) | |
| Benchmarking convert_columns 4096 i64(0.3): Analyzing | |
| convert_columns 4096 i64(0.3) | |
| time: [12.396 µs 12.398 µs 12.400 µs] | |
| change: [-0.7134% -0.6873% -0.6607%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 1 (1.00%) low mild | |
| 4 (4.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking convert_columns_prepared 4096 i64(0.3) | |
| Benchmarking convert_columns_prepared 4096 i64(0.3): Warming up for 3.0000 s | |
| Benchmarking convert_columns_prepared 4096 i64(0.3): Collecting 100 samples in estimated 5.0159 s (399k iterations) | |
| Benchmarking convert_columns_prepared 4096 i64(0.3): Analyzing | |
| convert_columns_prepared 4096 i64(0.3) | |
| time: [12.548 µs 12.569 µs 12.611 µs] | |
| change: [+1.7697% +1.8730% +2.0089%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 1 (1.00%) low severe | |
| 1 (1.00%) low mild | |
| 3 (3.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking convert_rows 4096 i64(0.3) | |
| Benchmarking convert_rows 4096 i64(0.3): Warming up for 3.0000 s | |
| Benchmarking convert_rows 4096 i64(0.3): Collecting 100 samples in estimated 5.1272 s (167k iterations) | |
| Benchmarking convert_rows 4096 i64(0.3): Analyzing | |
| convert_rows 4096 i64(0.3) | |
| time: [29.997 µs 30.004 µs 30.011 µs] | |
| change: [+1.1609% +1.1961% +1.2308%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 3 (3.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking convert_columns 4096 bool(0, 0.5) | |
| Benchmarking convert_columns 4096 bool(0, 0.5): Warming up for 3.0000 s | |
| Benchmarking convert_columns 4096 bool(0, 0.5): Collecting 100 samples in estimated 5.0255 s (419k iterations) | |
| Benchmarking convert_columns 4096 bool(0, 0.5): Analyzing | |
| convert_columns 4096 bool(0, 0.5) | |
| time: [11.995 µs 11.997 µs 11.999 µs] | |
| change: [-3.1646% -3.1435% -3.1198%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 2 (2.00%) low mild | |
| 2 (2.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking convert_columns_prepared 4096 bool(0, 0.5) | |
| Benchmarking convert_columns_prepared 4096 bool(0, 0.5): Warming up for 3.0000 s | |
| Benchmarking convert_columns_prepared 4096 bool(0, 0.5): Collecting 100 samples in estimated 5.0269 s (419k iterations) | |
| Benchmarking convert_columns_prepared 4096 bool(0, 0.5): Analyzing | |
| convert_columns_prepared 4096 bool(0, 0.5) | |
| time: [11.988 µs 11.989 µs 11.992 µs] | |
| change: [+0.4291% +0.5641% +0.6490%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 1 (1.00%) low mild | |
| 4 (4.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking convert_rows 4096 bool(0, 0.5) | |
| Benchmarking convert_rows 4096 bool(0, 0.5): Warming up for 3.0000 s | |
| Benchmarking convert_rows 4096 bool(0, 0.5): Collecting 100 samples in estimated 5.0391 s (374k iterations) | |
| Benchmarking convert_rows 4096 bool(0, 0.5): Analyzing | |
| convert_rows 4096 bool(0, 0.5) | |
| time: [13.736 µs 13.740 µs 13.743 µs] | |
| change: [+1.2985% +1.4891% +1.6826%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 25 outliers among 100 measurements (25.00%) | |
| 24 (24.00%) low severe | |
| 1 (1.00%) low mild | |
| Benchmarking convert_columns 4096 bool(0.3, 0.5) | |
| Benchmarking convert_columns 4096 bool(0.3, 0.5): Warming up for 3.0000 s | |
| Benchmarking convert_columns 4096 bool(0.3, 0.5): Collecting 100 samples in estimated 5.0139 s (409k iterations) | |
| Benchmarking convert_columns 4096 bool(0.3, 0.5): Analyzing | |
| convert_columns 4096 bool(0.3, 0.5) | |
| time: [12.259 µs 12.261 µs 12.263 µs] | |
| change: [-2.3380% -2.3083% -2.2811%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 2 (2.00%) low mild | |
| 4 (4.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking convert_columns_prepared 4096 bool(0.3, 0.5) | |
| Benchmarking convert_columns_prepared 4096 bool(0.3, 0.5): Warming up for 3.0000 s | |
| Benchmarking convert_columns_prepared 4096 bool(0.3, 0.5): Collecting 100 samples in estimated 5.0163 s (404k iterations) | |
| Benchmarking convert_columns_prepared 4096 bool(0.3, 0.5): Analyzing | |
| convert_columns_prepared 4096 bool(0.3, 0.5) | |
| time: [12.388 µs 12.390 µs 12.393 µs] | |
| change: [-1.7574% -1.7061% -1.6326%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 1 (1.00%) low severe | |
| 1 (1.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking convert_rows 4096 bool(0.3, 0.5) | |
| Benchmarking convert_rows 4096 bool(0.3, 0.5): Warming up for 3.0000 s | |
| Benchmarking convert_rows 4096 bool(0.3, 0.5): Collecting 100 samples in estimated 5.0433 s (374k iterations) | |
| Benchmarking convert_rows 4096 bool(0.3, 0.5): Analyzing | |
| convert_rows 4096 bool(0.3, 0.5) | |
| time: [13.407 µs 13.408 µs 13.410 µs] | |
| change: [-0.4051% -0.3846% -0.3634%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 3 (3.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking convert_columns 4096 string(10, 0) | |
| Benchmarking convert_columns 4096 string(10, 0): Warming up for 3.0000 s | |
| Benchmarking convert_columns 4096 string(10, 0): Collecting 100 samples in estimated 5.1915 s (131k iterations) | |
| Benchmarking convert_columns 4096 string(10, 0): Analyzing | |
| convert_columns 4096 string(10, 0) | |
| time: [39.441 µs 39.446 µs 39.452 µs] | |
| change: [-11.808% -11.776% -11.740%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 1 (1.00%) low mild | |
| 4 (4.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking convert_columns_prepared 4096 string(10, 0) | |
| Benchmarking convert_columns_prepared 4096 string(10, 0): Warming up for 3.0000 s | |
| Benchmarking convert_columns_prepared 4096 string(10, 0): Collecting 100 samples in estimated 5.1645 s (131k iterations) | |
| Benchmarking convert_columns_prepared 4096 string(10, 0): Analyzing | |
| convert_columns_prepared 4096 string(10, 0) | |
| time: [39.294 µs 39.303 µs 39.314 µs] | |
| change: [-12.233% -12.193% -12.156%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 4 (4.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking convert_rows 4096 string(10, 0) | |
| Benchmarking convert_rows 4096 string(10, 0): Warming up for 3.0000 s | |
| Benchmarking convert_rows 4096 string(10, 0): Collecting 100 samples in estimated 5.0584 s (141k iterations) | |
| Benchmarking convert_rows 4096 string(10, 0): Analyzing | |
| convert_rows 4096 string(10, 0) | |
| time: [35.999 µs 36.004 µs 36.009 µs] | |
| change: [-19.447% -19.390% -19.340%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 4 (4.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking convert_columns 4096 string(30, 0) | |
| Benchmarking convert_columns 4096 string(30, 0): Warming up for 3.0000 s | |
| Benchmarking convert_columns 4096 string(30, 0): Collecting 100 samples in estimated 5.2054 s (116k iterations) | |
| Benchmarking convert_columns 4096 string(30, 0): Analyzing | |
| convert_columns 4096 string(30, 0) | |
| time: [43.757 µs 43.783 µs 43.811 µs] | |
| change: [-8.4782% -8.0323% -7.7012%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 1 (1.00%) low mild | |
| 5 (5.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking convert_columns_prepared 4096 string(30, 0) | |
| Benchmarking convert_columns_prepared 4096 string(30, 0): Warming up for 3.0000 s | |
| Benchmarking convert_columns_prepared 4096 string(30, 0): Collecting 100 samples in estimated 5.0982 s (116k iterations) | |
| Benchmarking convert_columns_prepared 4096 string(30, 0): Analyzing | |
| convert_columns_prepared 4096 string(30, 0) | |
| time: [44.678 µs 44.735 µs 44.839 µs] | |
| change: [-6.3748% -5.9696% -5.6675%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 1 (1.00%) low severe | |
| 4 (4.00%) low mild | |
| 3 (3.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking convert_rows 4096 string(30, 0) | |
| Benchmarking convert_rows 4096 string(30, 0): Warming up for 3.0000 s | |
| Benchmarking convert_rows 4096 string(30, 0): Collecting 100 samples in estimated 5.1948 s (111k iterations) | |
| Benchmarking convert_rows 4096 string(30, 0): Analyzing | |
| convert_rows 4096 string(30, 0) | |
| time: [46.868 µs 47.023 µs 47.260 µs] | |
| change: [-12.875% -12.459% -12.037%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) high severe | |
| Benchmarking convert_columns 4096 string(100, 0) | |
| Benchmarking convert_columns 4096 string(100, 0): Warming up for 3.0000 s | |
| Benchmarking convert_columns 4096 string(100, 0): Collecting 100 samples in estimated 5.1762 s (76k iterations) | |
| Benchmarking convert_columns 4096 string(100, 0): Analyzing | |
| convert_columns 4096 string(100, 0) | |
| time: [68.820 µs 68.843 µs 68.867 µs] | |
| change: [+0.5667% +1.4420% +2.1257%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Benchmarking convert_columns_prepared 4096 string(100, 0) | |
| Benchmarking convert_columns_prepared 4096 string(100, 0): Warming up for 3.0000 s | |
| Benchmarking convert_columns_prepared 4096 string(100, 0): Collecting 100 samples in estimated 5.1168 s (76k iterations) | |
| Benchmarking convert_columns_prepared 4096 string(100, 0): Analyzing | |
| convert_columns_prepared 4096 string(100, 0) | |
| time: [67.192 µs 67.326 µs 67.539 µs] | |
| change: [-1.2877% -1.0420% -0.6598%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 14 outliers among 100 measurements (14.00%) | |
| 2 (2.00%) low mild | |
| 9 (9.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking convert_rows 4096 string(100, 0) | |
| Benchmarking convert_rows 4096 string(100, 0): Warming up for 3.0000 s | |
| Benchmarking convert_rows 4096 string(100, 0): Collecting 100 samples in estimated 5.1457 s (76k iterations) | |
| Benchmarking convert_rows 4096 string(100, 0): Analyzing | |
| convert_rows 4096 string(100, 0) | |
| time: [67.631 µs 67.683 µs 67.788 µs] | |
| change: [-9.0657% -8.9097% -8.6324%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 3 (3.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking convert_columns 4096 string(100, 0.5) | |
| Benchmarking convert_columns 4096 string(100, 0.5): Warming up for 3.0000 s | |
| Benchmarking convert_columns 4096 string(100, 0.5): Collecting 100 samples in estimated 5.1296 s (91k iterations) | |
| Benchmarking convert_columns 4096 string(100, 0.5): Analyzing | |
| convert_columns 4096 string(100, 0.5) | |
| time: [55.457 µs 55.649 µs 55.877 µs] | |
| change: [-9.7647% -9.4623% -9.1479%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking convert_columns_prepared 4096 string(100, 0.5) | |
| Benchmarking convert_columns_prepared 4096 string(100, 0.5): Warming up for 3.0000 s | |
| Benchmarking convert_columns_prepared 4096 string(100, 0.5): Collecting 100 samples in estimated 5.0818 s (91k iterations) | |
| Benchmarking convert_columns_prepared 4096 string(100, 0.5): Analyzing | |
| convert_columns_prepared 4096 string(100, 0.5) | |
| time: [55.244 µs 55.335 µs 55.413 µs] | |
| change: [-9.6495% -9.4251% -9.2138%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking convert_rows 4096 string(100, 0.5) | |
| Benchmarking convert_rows 4096 string(100, 0.5): Warming up for 3.0000 s | |
| Benchmarking convert_rows 4096 string(100, 0.5): Collecting 100 samples in estimated 5.0211 s (91k iterations) | |
| Benchmarking convert_rows 4096 string(100, 0.5): Analyzing | |
| convert_rows 4096 string(100, 0.5) | |
| time: [56.288 µs 56.527 µs 56.712 µs] | |
| change: [-23.084% -22.542% -22.000%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking convert_columns 4096 string view(10, 0) | |
| Benchmarking convert_columns 4096 string view(10, 0): Warming up for 3.0000 s | |
| Benchmarking convert_columns 4096 string view(10, 0): Collecting 100 samples in estimated 5.1109 s (136k iterations) | |
| Benchmarking convert_columns 4096 string view(10, 0): Analyzing | |
| convert_columns 4096 string view(10, 0) | |
| time: [37.034 µs 37.069 µs 37.144 µs] | |
| change: [-20.650% -20.552% -20.349%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 1 (1.00%) low mild | |
| 4 (4.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking convert_columns_prepared 4096 string view(10, 0) | |
| Benchmarking convert_columns_prepared 4096 string view(10, 0): Warming up for 3.0000 s | |
| Benchmarking convert_columns_prepared 4096 string view(10, 0): Collecting 100 samples in estimated 5.0389 s (136k iterations) | |
| Benchmarking convert_columns_prepared 4096 string view(10, 0): Analyzing | |
| convert_columns_prepared 4096 string view(10, 0) | |
| time: [37.056 µs 37.061 µs 37.067 µs] | |
| change: [-21.713% -21.687% -21.649%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 2 (2.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking convert_rows 4096 string view(10, 0) | |
| Benchmarking convert_rows 4096 string view(10, 0): Warming up for 3.0000 s | |
| Benchmarking convert_rows 4096 string view(10, 0): Collecting 100 samples in estimated 5.0560 s (111k iterations) | |
| Benchmarking convert_rows 4096 string view(10, 0): Analyzing | |
| convert_rows 4096 string view(10, 0) | |
| time: [45.058 µs 45.066 µs 45.074 µs] | |
| change: [-22.678% -22.638% -22.597%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 4 (4.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking convert_columns 4096 string view(30, 0) | |
| Benchmarking convert_columns 4096 string view(30, 0): Warming up for 3.0000 s | |
| Benchmarking convert_columns 4096 string view(30, 0): Collecting 100 samples in estimated 5.1022 s (121k iterations) | |
| Benchmarking convert_columns 4096 string view(30, 0): Analyzing | |
| convert_columns 4096 string view(30, 0) | |
| time: [41.416 µs 41.450 µs 41.488 µs] | |
| change: [-14.880% -14.814% -14.737%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 18 outliers among 100 measurements (18.00%) | |
| 2 (2.00%) high mild | |
| 16 (16.00%) high severe | |
| Benchmarking convert_columns_prepared 4096 string view(30, 0) | |
| Benchmarking convert_columns_prepared 4096 string view(30, 0): Warming up for 3.0000 s | |
| Benchmarking convert_columns_prepared 4096 string view(30, 0): Collecting 100 samples in estimated 5.0446 s (121k iterations) | |
| Benchmarking convert_columns_prepared 4096 string view(30, 0): Analyzing | |
| convert_columns_prepared 4096 string view(30, 0) | |
| time: [41.773 µs 41.822 µs 41.869 µs] | |
| change: [-13.624% -13.456% -13.335%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking convert_rows 4096 string view(30, 0) | |
| Benchmarking convert_rows 4096 string view(30, 0): Warming up for 3.0000 s | |
| Benchmarking convert_rows 4096 string view(30, 0): Collecting 100 samples in estimated 5.2366 s (96k iterations) | |
| Benchmarking convert_rows 4096 string view(30, 0): Analyzing | |
| convert_rows 4096 string view(30, 0) | |
| time: [53.879 µs 53.930 µs 53.978 µs] | |
| change: [-12.263% -12.177% -12.096%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 16 outliers among 100 measurements (16.00%) | |
| 3 (3.00%) high mild | |
| 13 (13.00%) high severe | |
| Benchmarking convert_columns 40960 string view(100, 0) | |
| Benchmarking convert_columns 40960 string view(100, 0): Warming up for 3.0000 s | |
| Benchmarking convert_columns 40960 string view(100, 0): Collecting 100 samples in estimated 6.5956 s (10k iterations) | |
| Benchmarking convert_columns 40960 string view(100, 0): Analyzing | |
| convert_columns 40960 string view(100, 0) | |
| time: [656.99 µs 657.11 µs 657.25 µs] | |
| change: [-5.9266% -5.7862% -5.6975%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 1 (1.00%) low severe | |
| 3 (3.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking convert_columns_prepared 40960 string view(100, 0) | |
| Benchmarking convert_columns_prepared 40960 string view(100, 0): Warming up for 3.0000 s | |
| Benchmarking convert_columns_prepared 40960 string view(100, 0): Collecting 100 samples in estimated 6.4852 s (10k iterations) | |
| Benchmarking convert_columns_prepared 40960 string view(100, 0): Analyzing | |
| convert_columns_prepared 40960 string view(100, 0) | |
| time: [646.52 µs 646.95 µs 647.73 µs] | |
| change: [-7.7054% -7.1161% -6.6761%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 5 (5.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking convert_rows 40960 string view(100, 0) | |
| Benchmarking convert_rows 40960 string view(100, 0): Warming up for 3.0000 s | |
| Benchmarking convert_rows 40960 string view(100, 0): Collecting 100 samples in estimated 7.7603 s (10k iterations) | |
| Benchmarking convert_rows 40960 string view(100, 0): Analyzing | |
| convert_rows 40960 string view(100, 0) | |
| time: [762.31 µs 762.44 µs 762.58 µs] | |
| change: [-9.5014% -9.0164% -8.7179%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 18 outliers among 100 measurements (18.00%) | |
| 9 (9.00%) low severe | |
| 1 (1.00%) low mild | |
| 6 (6.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking convert_columns 4096 string view(100, 0.5) | |
| Benchmarking convert_columns 4096 string view(100, 0.5): Warming up for 3.0000 s | |
| Benchmarking convert_columns 4096 string view(100, 0.5): Collecting 100 samples in estimated 5.0563 s (91k iterations) | |
| Benchmarking convert_columns 4096 string view(100, 0.5): Analyzing | |
| convert_columns 4096 string view(100, 0.5) | |
| time: [55.457 µs 55.484 µs 55.512 µs] | |
| change: [-12.994% -12.796% -12.585%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high severe | |
| Benchmarking convert_columns_prepared 4096 string view(100, 0.5) | |
| Benchmarking convert_columns_prepared 4096 string view(100, 0.5): Warming up for 3.0000 s | |
| Benchmarking convert_columns_prepared 4096 string view(100, 0.5): Collecting 100 samples in estimated 5.0394 s (91k iterations) | |
| Benchmarking convert_columns_prepared 4096 string view(100, 0.5): Analyzing | |
| convert_columns_prepared 4096 string view(100, 0.5) | |
| time: [55.038 µs 55.267 µs 55.504 µs] | |
| change: [-11.572% -11.338% -11.116%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 17 outliers among 100 measurements (17.00%) | |
| 15 (15.00%) low severe | |
| 1 (1.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking convert_rows 4096 string view(100, 0.5) | |
| Benchmarking convert_rows 4096 string view(100, 0.5): Warming up for 3.0000 s | |
| Benchmarking convert_rows 4096 string view(100, 0.5): Collecting 100 samples in estimated 5.0042 s (81k iterations) | |
| Benchmarking convert_rows 4096 string view(100, 0.5): Analyzing | |
| convert_rows 4096 string view(100, 0.5) | |
| time: [61.207 µs 61.423 µs 61.691 µs] | |
| change: [-19.681% -19.530% -19.350%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 3 (3.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking convert_columns 4096 string_dictionary(10, 0) | |
| Benchmarking convert_columns 4096 string_dictionary(10, 0): Warming up for 3.0000 s | |
| Benchmarking convert_columns 4096 string_dictionary(10, 0): Collecting 100 samples in estimated 5.3590 s (66k iterations) | |
| Benchmarking convert_columns 4096 string_dictionary(10, 0): Analyzing | |
| convert_columns 4096 string_dictionary(10, 0) | |
| time: [81.590 µs 81.602 µs 81.615 µs] | |
| change: [-4.4206% -4.3819% -4.3397%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 2 (2.00%) low mild | |
| 5 (5.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking convert_columns_prepared 4096 string_dictionary(10, 0) | |
| Benchmarking convert_columns_prepared 4096 string_dictionary(10, 0): Warming up for 3.0000 s | |
| Benchmarking convert_columns_prepared 4096 string_dictionary(10, 0): Collecting 100 samples in estimated 5.4136 s (66k iterations) | |
| Benchmarking convert_columns_prepared 4096 string_dictionary(10, 0): Analyzing | |
| convert_columns_prepared 4096 string_dictionary(10, 0) | |
| time: [82.237 µs 82.248 µs 82.259 µs] | |
| change: [-2.2672% -2.1817% -2.0003%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 2 (2.00%) low mild | |
| 3 (3.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking convert_rows 4096 string_dictionary(10, 0) | |
| Benchmarking convert_rows 4096 string_dictionary(10, 0): Warming up for 3.0000 s | |
| Benchmarking convert_rows 4096 string_dictionary(10, 0): Collecting 100 samples in estimated 5.1644 s (141k iterations) | |
| Benchmarking convert_rows 4096 string_dictionary(10, 0): Analyzing | |
| convert_rows 4096 string_dictionary(10, 0) | |
| time: [36.715 µs 36.736 µs 36.753 µs] | |
| change: [-16.341% -16.151% -16.000%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking convert_columns 4096 string_dictionary(30, 0) | |
| Benchmarking convert_columns 4096 string_dictionary(30, 0): Warming up for 3.0000 s | |
| Benchmarking convert_columns 4096 string_dictionary(30, 0): Collecting 100 samples in estimated 5.3409 s (61k iterations) | |
| Benchmarking convert_columns 4096 string_dictionary(30, 0): Analyzing | |
| convert_columns 4096 string_dictionary(30, 0) | |
| time: [88.642 µs 88.656 µs 88.671 µs] | |
| change: [-2.0393% -1.9880% -1.9495%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 3 (3.00%) low mild | |
| 5 (5.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking convert_columns_prepared 4096 string_dictionary(30, 0) | |
| Benchmarking convert_columns_prepared 4096 string_dictionary(30, 0): Warming up for 3.0000 s | |
| Benchmarking convert_columns_prepared 4096 string_dictionary(30, 0): Collecting 100 samples in estimated 5.3014 s (61k iterations) | |
| Benchmarking convert_columns_prepared 4096 string_dictionary(30, 0): Analyzing | |
| convert_columns_prepared 4096 string_dictionary(30, 0) | |
| time: [87.133 µs 87.143 µs 87.154 µs] | |
| change: [-2.5947% -2.5428% -2.4993%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 1 (1.00%) low mild | |
| 6 (6.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking convert_rows 4096 string_dictionary(30, 0) | |
| Benchmarking convert_rows 4096 string_dictionary(30, 0): Warming up for 3.0000 s | |
| Benchmarking convert_rows 4096 string_dictionary(30, 0): Collecting 100 samples in estimated 5.2241 s (111k iterations) | |
| Benchmarking convert_rows 4096 string_dictionary(30, 0): Analyzing | |
| convert_rows 4096 string_dictionary(30, 0) | |
| time: [46.902 µs 46.910 µs 46.918 µs] | |
| change: [-13.034% -13.007% -12.984%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 15 outliers among 100 measurements (15.00%) | |
| 1 (1.00%) low mild | |
| 12 (12.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking convert_columns 4096 string_dictionary(100, 0) | |
| Benchmarking convert_columns 4096 string_dictionary(100, 0): Warming up for 3.0000 s | |
| Benchmarking convert_columns 4096 string_dictionary(100, 0): Collecting 100 samples in estimated 5.3352 s (45k iterations) | |
| Benchmarking convert_columns 4096 string_dictionary(100, 0): Analyzing | |
| convert_columns 4096 string_dictionary(100, 0) | |
| time: [119.64 µs 119.67 µs 119.69 µs] | |
| change: [+1.0722% +1.1264% +1.1699%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 3 (3.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking convert_columns_prepared 4096 string_dictionary(100, 0) | |
| Benchmarking convert_columns_prepared 4096 string_dictionary(100, 0): Warming up for 3.0000 s | |
| Benchmarking convert_columns_prepared 4096 string_dictionary(100, 0): Collecting 100 samples in estimated 5.2799 s (45k iterations) | |
| Benchmarking convert_columns_prepared 4096 string_dictionary(100, 0): Analyzing | |
| convert_columns_prepared 4096 string_dictionary(100, 0) | |
| time: [115.60 µs 115.62 µs 115.64 µs] | |
| change: [-4.1813% -4.1461% -4.1106%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 5 (5.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking convert_rows 4096 string_dictionary(100, 0) | |
| Benchmarking convert_rows 4096 string_dictionary(100, 0): Warming up for 3.0000 s | |
| Benchmarking convert_rows 4096 string_dictionary(100, 0): Collecting 100 samples in estimated 5.1358 s (76k iterations) | |
| Benchmarking convert_rows 4096 string_dictionary(100, 0): Analyzing | |
| convert_rows 4096 string_dictionary(100, 0) | |
| time: [67.926 µs 67.937 µs 67.948 µs] | |
| change: [-8.6741% -8.6168% -8.5763%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 16 outliers among 100 measurements (16.00%) | |
| 2 (2.00%) low mild | |
| 10 (10.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking convert_columns 4096 string_dictionary(100, 0.5) | |
| Benchmarking convert_columns 4096 string_dictionary(100, 0.5): Warming up for 3.0000 s | |
| Benchmarking convert_columns 4096 string_dictionary(100, 0.5): Collecting 100 samples in estimated 5.1238 s (56k iterations) | |
| Benchmarking convert_columns 4096 string_dictionary(100, 0.5): Analyzing | |
| convert_columns 4096 string_dictionary(100, 0.5) | |
| time: [93.140 µs 93.188 µs 93.246 µs] | |
| change: [+1.9444% +2.0430% +2.1368%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high mild | |
| Benchmarking convert_columns_prepared 4096 string_dictionary(100, 0.5) | |
| Benchmarking convert_columns_prepared 4096 string_dictionary(100, 0.5): Warming up for 3.0000 s | |
| Benchmarking convert_columns_prepared 4096 string_dictionary(100, 0.5): Collecting 100 samples in estimated 5.0298 s (56k iterations) | |
| Benchmarking convert_columns_prepared 4096 string_dictionary(100, 0.5): Analyzing | |
| convert_columns_prepared 4096 string_dictionary(100, 0.5) | |
| time: [91.359 µs 91.409 µs 91.465 µs] | |
| change: [+1.2169% +1.3321% +1.4720%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 1 (1.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking convert_rows 4096 string_dictionary(100, 0.5) | |
| Benchmarking convert_rows 4096 string_dictionary(100, 0.5): Warming up for 3.0000 s | |
| Benchmarking convert_rows 4096 string_dictionary(100, 0.5): Collecting 100 samples in estimated 5.2050 s (96k iterations) | |
| Benchmarking convert_rows 4096 string_dictionary(100, 0.5): Analyzing | |
| convert_rows 4096 string_dictionary(100, 0.5) | |
| time: [55.846 µs 56.587 µs 57.235 µs] | |
| change: [-21.942% -21.318% -20.775%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 23 outliers among 100 measurements (23.00%) | |
| 8 (8.00%) high mild | |
| 15 (15.00%) high severe | |
| Benchmarking convert_columns 4096 string_dictionary_low_cardinality(10, 0) | |
| Benchmarking convert_columns 4096 string_dictionary_low_cardinality(10, 0): Warming up for 3.0000 s | |
| Benchmarking convert_columns 4096 string_dictionary_low_cardinality(10, 0): Collecting 100 samples in estimated 5.1013 s (121k iterations) | |
| Benchmarking convert_columns 4096 string_dictionary_low_cardinality(10, 0): Analyzing | |
| convert_columns 4096 string_dictionary_low_cardinality(10, 0) | |
| time: [41.984 µs 41.989 µs 41.995 µs] | |
| change: [+2.3656% +2.3906% +2.4176%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 1 (1.00%) low mild | |
| 2 (2.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking convert_columns_prepared 4096 string_dictionary_low_cardinality(10, 0) | |
| Benchmarking convert_columns_prepared 4096 string_dictionary_low_cardinality(10, 0): Warming up for 3.0000 s | |
| Benchmarking convert_columns_prepared 4096 string_dictionary_low_cardinality(10, 0): Collecting 100 samples in estimated 5.0600 s (121k iterations) | |
| Benchmarking convert_columns_prepared 4096 string_dictionary_low_cardinality(10, 0): Analyzing | |
| convert_columns_prepared 4096 string_dictionary_low_cardinality(10, 0) | |
| time: [41.762 µs 41.768 µs 41.775 µs] | |
| change: [+4.8212% +4.9157% +5.0150%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 1 (1.00%) low severe | |
| 1 (1.00%) low mild | |
| 5 (5.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking convert_rows 4096 string_dictionary_low_cardinality(10, 0) | |
| Benchmarking convert_rows 4096 string_dictionary_low_cardinality(10, 0): Warming up for 3.0000 s | |
| Benchmarking convert_rows 4096 string_dictionary_low_cardinality(10, 0): Collecting 100 samples in estimated 5.1047 s (141k iterations) | |
| Benchmarking convert_rows 4096 string_dictionary_low_cardinality(10, 0): Analyzing | |
| convert_rows 4096 string_dictionary_low_cardinality(10, 0) | |
| time: [36.292 µs 36.376 µs 36.557 µs] | |
| change: [-18.427% -18.325% -18.082%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 1 (1.00%) low mild | |
| 4 (4.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking convert_columns 4096 string_dictionary_low_cardinality(30, 0) | |
| Benchmarking convert_columns 4096 string_dictionary_low_cardinality(30, 0): Warming up for 3.0000 s | |
| Benchmarking convert_columns 4096 string_dictionary_low_cardinality(30, 0): Collecting 100 samples in estimated 5.0854 s (116k iterations) | |
| Benchmarking convert_columns 4096 string_dictionary_low_cardinality(30, 0): Analyzing | |
| convert_columns 4096 string_dictionary_low_cardinality(30, 0) | |
| time: [43.782 µs 43.828 µs 43.886 µs] | |
| change: [+1.7838% +2.4872% +2.8816%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 1 (1.00%) low mild | |
| 3 (3.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking convert_columns_prepared 4096 string_dictionary_low_cardinality(30, 0) | |
| Benchmarking convert_columns_prepared 4096 string_dictionary_low_cardinality(30, 0): Warming up for 3.0000 s | |
| Benchmarking convert_columns_prepared 4096 string_dictionary_low_cardinality(30, 0): Collecting 100 samples in estimated 5.0143 s (116k iterations) | |
| Benchmarking convert_columns_prepared 4096 string_dictionary_low_cardinality(30, 0): Analyzing | |
| convert_columns_prepared 4096 string_dictionary_low_cardinality(30, 0) | |
| time: [43.202 µs 43.217 µs 43.244 µs] | |
| change: [+4.4480% +4.4873% +4.5303%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 5 (5.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking convert_rows 4096 string_dictionary_low_cardinality(30, 0) | |
| Benchmarking convert_rows 4096 string_dictionary_low_cardinality(30, 0): Warming up for 3.0000 s | |
| Benchmarking convert_rows 4096 string_dictionary_low_cardinality(30, 0): Collecting 100 samples in estimated 5.0502 s (106k iterations) | |
| Benchmarking convert_rows 4096 string_dictionary_low_cardinality(30, 0): Analyzing | |
| convert_rows 4096 string_dictionary_low_cardinality(30, 0) | |
| time: [47.843 µs 47.856 µs 47.867 µs] | |
| change: [-11.209% -11.085% -10.992%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking convert_columns 4096 string_dictionary_low_cardinality(100, 0) | |
| Benchmarking convert_columns 4096 string_dictionary_low_cardinality(100, 0): Warming up for 3.0000 s | |
| Benchmarking convert_columns 4096 string_dictionary_low_cardinality(100, 0): Collecting 100 samples in estimated 5.0115 s (101k iterations) | |
| Benchmarking convert_columns 4096 string_dictionary_low_cardinality(100, 0): Analyzing | |
| convert_columns 4096 string_dictionary_low_cardinality(100, 0) | |
| time: [51.750 µs 51.830 µs 51.993 µs] | |
| change: [-1.4119% -1.3008% -1.1673%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 5 (5.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking convert_columns_prepared 4096 string_dictionary_low_cardinality(100, 0) | |
| Benchmarking convert_columns_prepared 4096 string_dictionary_low_cardinality(100, 0): Warming up for 3.0000 s | |
| Benchmarking convert_columns_prepared 4096 string_dictionary_low_cardinality(100, 0): Collecting 100 samples in estimated 5.1636 s (106k iterations) | |
| Benchmarking convert_columns_prepared 4096 string_dictionary_low_cardinality(100, 0): Analyzing | |
| convert_columns_prepared 4096 string_dictionary_low_cardinality(100, 0) | |
| time: [48.320 µs 48.329 µs 48.339 µs] | |
| change: [-6.0452% -5.9693% -5.9131%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 1 (1.00%) low mild | |
| 7 (7.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking convert_rows 4096 string_dictionary_low_cardinality(100, 0) | |
| Benchmarking convert_rows 4096 string_dictionary_low_cardinality(100, 0): Warming up for 3.0000 s | |
| Benchmarking convert_rows 4096 string_dictionary_low_cardinality(100, 0): Collecting 100 samples in estimated 5.1916 s (76k iterations) | |
| Benchmarking convert_rows 4096 string_dictionary_low_cardinality(100, 0): Analyzing | |
| convert_rows 4096 string_dictionary_low_cardinality(100, 0) | |
| time: [69.510 µs 69.710 µs 70.015 µs] | |
| change: [-6.4257% -5.9612% -5.2140%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 4 (4.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking convert_columns 4096 string(20, 0.5), string(30, 0), string(100, 0), i64(0) | |
| Benchmarking convert_columns 4096 string(20, 0.5), string(30, 0), string(100, 0), i64(0): Warming up for 3.0000 s | |
| Benchmarking convert_columns 4096 string(20, 0.5), string(30, 0), string(100, 0), i64(0): Collecting 100 samples in estimated 5.4325 s (35k iterations) | |
| Benchmarking convert_columns 4096 string(20, 0.5), string(30, 0), string(100, 0), i64(0): Analyzing | |
| convert_columns 4096 string(20, 0.5), string(30, 0), string(100, 0), i64(0) | |
| time: [157.28 µs 157.58 µs 158.03 µs] | |
| change: [-3.8915% -3.6802% -3.3577%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 13 outliers among 100 measurements (13.00%) | |
| 7 (7.00%) low mild | |
| 2 (2.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking convert_columns_prepared 4096 string(20, 0.5), string(30, 0), string(100, 0), i64(0) | |
| Benchmarking convert_columns_prepared 4096 string(20, 0.5), string(30, 0), string(100, 0), i64(0): Warming up for 3.0000 s | |
| Benchmarking convert_columns_prepared 4096 string(20, 0.5), string(30, 0), string(100, 0), i64(0): Collecting 100 samples in estimated 5.3751 s (35k iterations) | |
| Benchmarking convert_columns_prepared 4096 string(20, 0.5), string(30, 0), string(100, 0), i64(0): Analyzing | |
| convert_columns_prepared 4096 string(20, 0.5), string(30, 0), string(100, 0), i64(0) | |
| time: [152.96 µs 153.62 µs 154.45 µs] | |
| change: [-6.5672% -6.1994% -5.8117%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 7 (7.00%) high severe | |
| Benchmarking convert_rows 4096 string(20, 0.5), string(30, 0), string(100, 0), i64(0) | |
| Benchmarking convert_rows 4096 string(20, 0.5), string(30, 0), string(100, 0), i64(0): Warming up for 3.0000 s | |
| Benchmarking convert_rows 4096 string(20, 0.5), string(30, 0), string(100, 0), i64(0): Collecting 100 samples in estimated 5.7522 s (30k iterations) | |
| Benchmarking convert_rows 4096 string(20, 0.5), string(30, 0), string(100, 0), i64(0): Analyzing | |
| convert_rows 4096 string(20, 0.5), string(30, 0), string(100, 0), i64(0) | |
| time: [200.35 µs 200.90 µs 201.60 µs] | |
| change: [-5.8837% -5.6144% -5.2816%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 1 (1.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking convert_columns 4096 4096 string_dictionary(20, 0.5), string_dictionary(30, 0), string_dictionary(10... | |
| Benchmarking convert_columns 4096 4096 string_dictionary(20, 0.5), string_dictionary(30, 0), string_dictionary(10...: Warming up for 3.0000 s | |
| Benchmarking convert_columns 4096 4096 string_dictionary(20, 0.5), string_dictionary(30, 0), string_dictionary(10...: Collecting 100 samples in estimated 5.8025 s (20k iterations) | |
| Benchmarking convert_columns 4096 4096 string_dictionary(20, 0.5), string_dictionary(30, 0), string_dictionary(10...: Analyzing | |
| convert_columns 4096 4096 string_dictionary(20, 0.5), string_dictionary(30, 0), string_dictionary(10... | |
| time: [289.44 µs 289.52 µs 289.61 µs] | |
| change: [+0.7000% +0.7826% +0.8646%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 3 (3.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking convert_columns_prepared 4096 4096 string_dictionary(20, 0.5), string_dictionary(30, 0), string_dict... | |
| Benchmarking convert_columns_prepared 4096 4096 string_dictionary(20, 0.5), string_dictionary(30, 0), string_dict...: Warming up for 3.0000 s | |
| Benchmarking convert_columns_prepared 4096 4096 string_dictionary(20, 0.5), string_dictionary(30, 0), string_dict...: Collecting 100 samples in estimated 5.7634 s (20k iterations) | |
| Benchmarking convert_columns_prepared 4096 4096 string_dictionary(20, 0.5), string_dictionary(30, 0), string_dict...: Analyzing | |
| convert_columns_prepared 4096 4096 string_dictionary(20, 0.5), string_dictionary(30, 0), string_dict... | |
| time: [283.72 µs 284.53 µs 285.63 µs] | |
| change: [-1.3399% -1.1291% -0.8552%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 3 (3.00%) high mild | |
| 7 (7.00%) high severe | |
| Benchmarking convert_rows 4096 4096 string_dictionary(20, 0.5), string_dictionary(30, 0), string_dictionary(100, ... | |
| Benchmarking convert_rows 4096 4096 string_dictionary(20, 0.5), string_dictionary(30, 0), string_dictionary(100, ...: Warming up for 3.0000 s | |
| Benchmarking convert_rows 4096 4096 string_dictionary(20, 0.5), string_dictionary(30, 0), string_dictionary(100, ...: Collecting 100 samples in estimated 5.7220 s (30k iterations) | |
| Benchmarking convert_rows 4096 4096 string_dictionary(20, 0.5), string_dictionary(30, 0), string_dictionary(100, ...: Analyzing | |
| convert_rows 4096 4096 string_dictionary(20, 0.5), string_dictionary(30, 0), string_dictionary(100, ... | |
| time: [187.55 µs 187.68 µs 187.80 µs] | |
| change: [-15.654% -15.526% -15.401%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 23 outliers among 100 measurements (23.00%) | |
| 4 (4.00%) low severe | |
| 7 (7.00%) low mild | |
| 8 (8.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking iterate rows | |
| Benchmarking iterate rows: Warming up for 3.0000 s | |
| Benchmarking iterate rows: Collecting 100 samples in estimated 5.0869 s (278k iterations) | |
| Benchmarking iterate rows: Analyzing | |
| iterate rows time: [22.627 µs 22.631 µs 22.635 µs] | |
| change: [-2.8363% -2.5300% -2.3375%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) high mild | |
| Running benches/sort_kernel.rs (target/x86_64-unknown-linux-gnu/release/deps/sort_kernel-ff587e2fbc163f30) | |
| Benchmarking sort i32 2^10 | |
| Benchmarking sort i32 2^10: Warming up for 3.0000 s | |
| Benchmarking sort i32 2^10: Collecting 100 samples in estimated 5.0082 s (788k iterations) | |
| Benchmarking sort i32 2^10: Analyzing | |
| sort i32 2^10 time: [6.3680 µs 6.3694 µs 6.3709 µs] | |
| change: [+0.5629% +0.6001% +0.6378%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 3 (3.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking sort i32 to indices 2^10 | |
| Benchmarking sort i32 to indices 2^10: Warming up for 3.0000 s | |
| Benchmarking sort i32 to indices 2^10: Collecting 100 samples in estimated 5.0223 s (485k iterations) | |
| Benchmarking sort i32 to indices 2^10: Analyzing | |
| sort i32 to indices 2^10 | |
| time: [10.348 µs 10.349 µs 10.351 µs] | |
| change: [-2.2735% -2.2371% -2.1973%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 3 (3.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking sort i32 2^12 | |
| Benchmarking sort i32 2^12: Warming up for 3.0000 s | |
| Benchmarking sort i32 2^12: Collecting 100 samples in estimated 5.1120 s (167k iterations) | |
| Benchmarking sort i32 2^12: Analyzing | |
| sort i32 2^12 time: [30.644 µs 30.652 µs 30.662 µs] | |
| change: [-1.0511% -0.8661% -0.7521%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 2 (2.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking sort i32 to indices 2^12 | |
| Benchmarking sort i32 to indices 2^12: Warming up for 3.0000 s | |
| Benchmarking sort i32 to indices 2^12: Collecting 100 samples in estimated 5.0810 s (106k iterations) | |
| Benchmarking sort i32 to indices 2^12: Analyzing | |
| sort i32 to indices 2^12 | |
| time: [47.848 µs 47.854 µs 47.862 µs] | |
| change: [-1.2505% -1.1897% -1.1509%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 5 (5.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking sort i32 nulls 2^10 | |
| Benchmarking sort i32 nulls 2^10: Warming up for 3.0000 s | |
| Benchmarking sort i32 nulls 2^10: Collecting 100 samples in estimated 5.0208 s (1.1M iterations) | |
| Benchmarking sort i32 nulls 2^10: Analyzing | |
| sort i32 nulls 2^10 time: [4.4310 µs 4.4335 µs 4.4356 µs] | |
| change: [+1.1202% +1.2015% +1.2728%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Benchmarking sort i32 nulls to indices 2^10 | |
| Benchmarking sort i32 nulls to indices 2^10: Warming up for 3.0000 s | |
| Benchmarking sort i32 nulls to indices 2^10: Collecting 100 samples in estimated 5.0208 s (581k iterations) | |
| Benchmarking sort i32 nulls to indices 2^10: Analyzing | |
| sort i32 nulls to indices 2^10 | |
| time: [8.6002 µs 8.6229 µs 8.6612 µs] | |
| change: [-0.4276% +0.4073% +1.2359%] (p = 0.36 > 0.05) | |
| No change in performance detected. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 1 (1.00%) low mild | |
| 3 (3.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking sort i32 nulls 2^12 | |
| Benchmarking sort i32 nulls 2^12: Warming up for 3.0000 s | |
| Benchmarking sort i32 nulls 2^12: Collecting 100 samples in estimated 5.0159 s (273k iterations) | |
| Benchmarking sort i32 nulls 2^12: Analyzing | |
| sort i32 nulls 2^12 time: [18.367 µs 18.383 µs 18.416 µs] | |
| change: [+0.5970% +0.9048% +1.4386%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) high severe | |
| Benchmarking sort i32 nulls to indices 2^12 | |
| Benchmarking sort i32 nulls to indices 2^12: Warming up for 3.0000 s | |
| Benchmarking sort i32 nulls to indices 2^12: Collecting 100 samples in estimated 5.1497 s (152k iterations) | |
| Benchmarking sort i32 nulls to indices 2^12: Analyzing | |
| sort i32 nulls to indices 2^12 | |
| time: [33.951 µs 34.095 µs 34.277 µs] | |
| change: [+0.4432% +0.6077% +0.8334%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 1 (1.00%) low mild | |
| 3 (3.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking sort f32 2^12 | |
| Benchmarking sort f32 2^12: Warming up for 3.0000 s | |
| Benchmarking sort f32 2^12: Collecting 100 samples in estimated 5.1755 s (111k iterations) | |
| Benchmarking sort f32 2^12: Analyzing | |
| sort f32 2^12 time: [46.622 µs 46.628 µs 46.635 µs] | |
| change: [+1.6555% +2.6882% +3.2314%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 3 (3.00%) low mild | |
| 2 (2.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking sort f32 to indices 2^12 | |
| Benchmarking sort f32 to indices 2^12: Warming up for 3.0000 s | |
| Benchmarking sort f32 to indices 2^12: Collecting 100 samples in estimated 5.0022 s (86k iterations) | |
| Benchmarking sort f32 to indices 2^12: Analyzing | |
| sort f32 to indices 2^12 | |
| time: [58.257 µs 58.266 µs 58.275 µs] | |
| change: [-0.9520% -0.7762% -0.6536%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 3 (3.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking sort f32 nulls 2^12 | |
| Benchmarking sort f32 nulls 2^12: Warming up for 3.0000 s | |
| Benchmarking sort f32 nulls 2^12: Collecting 100 samples in estimated 5.0802 s (197k iterations) | |
| Benchmarking sort f32 nulls 2^12: Analyzing | |
| sort f32 nulls 2^12 time: [25.778 µs 25.785 µs 25.791 µs] | |
| change: [-0.6074% +0.0321% +0.5115%] (p = 0.92 > 0.05) | |
| No change in performance detected. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high severe | |
| Benchmarking sort f32 nulls to indices 2^12 | |
| Benchmarking sort f32 nulls to indices 2^12: Warming up for 3.0000 s | |
| Benchmarking sort f32 nulls to indices 2^12: Collecting 100 samples in estimated 5.1943 s (126k iterations) | |
| Benchmarking sort f32 nulls to indices 2^12: Analyzing | |
| sort f32 nulls to indices 2^12 | |
| time: [41.070 µs 41.077 µs 41.084 µs] | |
| change: [+0.3421% +0.3786% +0.4141%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 2 (2.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking sort string[10] to indices 2^12 | |
| Benchmarking sort string[10] to indices 2^12: Warming up for 3.0000 s | |
| Benchmarking sort string[10] to indices 2^12: Collecting 100 samples in estimated 5.0262 s (20k iterations) | |
| Benchmarking sort string[10] to indices 2^12: Analyzing | |
| sort string[10] to indices 2^12 | |
| time: [243.63 µs 243.77 µs 243.90 µs] | |
| change: [-0.1012% +0.1885% +0.4268%] (p = 0.18 > 0.05) | |
| No change in performance detected. | |
| Benchmarking sort string[10] nulls to indices 2^12 | |
| Benchmarking sort string[10] nulls to indices 2^12: Warming up for 3.0000 s | |
| Benchmarking sort string[10] nulls to indices 2^12: Collecting 100 samples in estimated 5.5820 s (45k iterations) | |
| Benchmarking sort string[10] nulls to indices 2^12: Analyzing | |
| sort string[10] nulls to indices 2^12 | |
| time: [127.27 µs 128.28 µs 129.19 µs] | |
| change: [+2.2814% +3.1019% +3.8531%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Benchmarking sort string[10] dict to indices 2^12 | |
| Benchmarking sort string[10] dict to indices 2^12: Warming up for 3.0000 s | |
| Benchmarking sort string[10] dict to indices 2^12: Collecting 100 samples in estimated 6.1918 s (20k iterations) | |
| Benchmarking sort string[10] dict to indices 2^12: Analyzing | |
| sort string[10] dict to indices 2^12 | |
| time: [306.72 µs 306.76 µs 306.80 µs] | |
| change: [-6.5962% -6.5200% -6.4471%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 1 (1.00%) low severe | |
| 1 (1.00%) low mild | |
| 3 (3.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking sort string[10] dict nulls to indices 2^12 | |
| Benchmarking sort string[10] dict nulls to indices 2^12: Warming up for 3.0000 s | |
| Benchmarking sort string[10] dict nulls to indices 2^12: Collecting 100 samples in estimated 5.5287 s (35k iterations) | |
| Benchmarking sort string[10] dict nulls to indices 2^12: Analyzing | |
| sort string[10] dict nulls to indices 2^12 | |
| time: [163.98 µs 164.69 µs 165.31 µs] | |
| change: [-1.5220% -0.6857% +0.1852%] (p = 0.12 > 0.05) | |
| No change in performance detected. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) high mild | |
| Benchmarking sort primitive run 2^12 | |
| Benchmarking sort primitive run 2^12: Warming up for 3.0000 s | |
| Benchmarking sort primitive run 2^12: Collecting 100 samples in estimated 5.0294 s (823k iterations) | |
| Benchmarking sort primitive run 2^12: Analyzing | |
| sort primitive run 2^12 time: [6.0862 µs 6.0874 µs 6.0888 µs] | |
| change: [-11.948% -11.915% -11.879%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 1 (1.00%) low mild | |
| 6 (6.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking sort primitive run to indices 2^12 | |
| Benchmarking sort primitive run to indices 2^12: Warming up for 3.0000 s | |
| Benchmarking sort primitive run to indices 2^12: Collecting 100 samples in estimated 5.0016 s (823k iterations) | |
| Benchmarking sort primitive run to indices 2^12: Analyzing | |
| sort primitive run to indices 2^12 | |
| time: [6.0752 µs 6.0760 µs 6.0769 µs] | |
| change: [-1.1957% -1.0784% -0.9403%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 13 outliers among 100 measurements (13.00%) | |
| 6 (6.00%) low mild | |
| 5 (5.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking lexsort (f32, f32) 2^10 | |
| Benchmarking lexsort (f32, f32) 2^10: Warming up for 3.0000 s | |
| Benchmarking lexsort (f32, f32) 2^10: Collecting 100 samples in estimated 5.0491 s (111k iterations) | |
| Benchmarking lexsort (f32, f32) 2^10: Analyzing | |
| lexsort (f32, f32) 2^10 time: [45.186 µs 45.374 µs 45.626 µs] | |
| change: [+3.1181% +3.5677% +4.2474%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 14 outliers among 100 measurements (14.00%) | |
| 1 (1.00%) low mild | |
| 5 (5.00%) high mild | |
| 8 (8.00%) high severe | |
| Benchmarking lexsort (f32, f32) 2^12 | |
| Benchmarking lexsort (f32, f32) 2^12: Warming up for 3.0000 s | |
| Benchmarking lexsort (f32, f32) 2^12: Collecting 100 samples in estimated 5.5164 s (25k iterations) | |
| Benchmarking lexsort (f32, f32) 2^12: Analyzing | |
| lexsort (f32, f32) 2^12 time: [218.65 µs 218.67 µs 218.70 µs] | |
| change: [-1.1960% -1.1034% -0.9955%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 3 (3.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking lexsort (f32, f32) nulls 2^10 | |
| Benchmarking lexsort (f32, f32) nulls 2^10: Warming up for 3.0000 s | |
| Benchmarking lexsort (f32, f32) nulls 2^10: Collecting 100 samples in estimated 5.1749 s (116k iterations) | |
| Benchmarking lexsort (f32, f32) nulls 2^10: Analyzing | |
| lexsort (f32, f32) nulls 2^10 | |
| time: [44.794 µs 44.861 µs 44.917 µs] | |
| change: [-2.2213% -2.0199% -1.8598%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 24 outliers among 100 measurements (24.00%) | |
| 24 (24.00%) high mild | |
| Benchmarking lexsort (f32, f32) nulls 2^12 | |
| Benchmarking lexsort (f32, f32) nulls 2^12: Warming up for 3.0000 s | |
| Benchmarking lexsort (f32, f32) nulls 2^12: Collecting 100 samples in estimated 5.9373 s (30k iterations) | |
| Benchmarking lexsort (f32, f32) nulls 2^12: Analyzing | |
| lexsort (f32, f32) nulls 2^12 | |
| time: [196.26 µs 196.78 µs 197.58 µs] | |
| change: [-1.4629% -1.0181% -0.6383%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 3 (3.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking lexsort (bool, bool) 2^12 | |
| Benchmarking lexsort (bool, bool) 2^12: Warming up for 3.0000 s | |
| Benchmarking lexsort (bool, bool) 2^12: Collecting 100 samples in estimated 5.0908 s (61k iterations) | |
| Benchmarking lexsort (bool, bool) 2^12: Analyzing | |
| lexsort (bool, bool) 2^12 | |
| time: [83.156 µs 83.930 µs 84.788 µs] | |
| change: [-5.3547% -4.9153% -4.4144%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 18 outliers among 100 measurements (18.00%) | |
| 4 (4.00%) high mild | |
| 14 (14.00%) high severe | |
| Benchmarking lexsort (bool, bool) nulls 2^12 | |
| Benchmarking lexsort (bool, bool) nulls 2^12: Warming up for 3.0000 s | |
| Benchmarking lexsort (bool, bool) nulls 2^12: Collecting 100 samples in estimated 5.3211 s (50k iterations) | |
| Benchmarking lexsort (bool, bool) nulls 2^12: Analyzing | |
| lexsort (bool, bool) nulls 2^12 | |
| time: [105.76 µs 105.91 µs 106.18 µs] | |
| change: [-0.8980% -0.2083% +0.7803%] (p = 0.68 > 0.05) | |
| No change in performance detected. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 2 (2.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking lexsort (f32, f32) 2^12 limit 10 | |
| Benchmarking lexsort (f32, f32) 2^12 limit 10: Warming up for 3.0000 s | |
| Benchmarking lexsort (f32, f32) 2^12 limit 10: Collecting 100 samples in estimated 5.1753 s (126k iterations) | |
| Benchmarking lexsort (f32, f32) 2^12 limit 10: Analyzing | |
| lexsort (f32, f32) 2^12 limit 10 | |
| time: [40.358 µs 40.369 µs 40.387 µs] | |
| change: [-4.1977% -3.5855% -2.7678%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 1 (1.00%) low mild | |
| 4 (4.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking lexsort (f32, f32) 2^12 limit 100 | |
| Benchmarking lexsort (f32, f32) 2^12 limit 100: Warming up for 3.0000 s | |
| Benchmarking lexsort (f32, f32) 2^12 limit 100: Collecting 100 samples in estimated 5.1353 s (121k iterations) | |
| Benchmarking lexsort (f32, f32) 2^12 limit 100: Analyzing | |
| lexsort (f32, f32) 2^12 limit 100 | |
| time: [42.324 µs 42.330 µs 42.336 µs] | |
| change: [-4.6927% -4.4898% -4.3667%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 4 (4.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking lexsort (f32, f32) 2^12 limit 1000 | |
| Benchmarking lexsort (f32, f32) 2^12 limit 1000: Warming up for 3.0000 s | |
| Benchmarking lexsort (f32, f32) 2^12 limit 1000: Collecting 100 samples in estimated 5.2888 s (66k iterations) | |
| Benchmarking lexsort (f32, f32) 2^12 limit 1000: Analyzing | |
| lexsort (f32, f32) 2^12 limit 1000 | |
| time: [79.783 µs 80.166 µs 80.639 µs] | |
| change: [-1.4137% -1.1143% -0.7620%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 22 outliers among 100 measurements (22.00%) | |
| 2 (2.00%) high mild | |
| 20 (20.00%) high severe | |
| Benchmarking lexsort (f32, f32) 2^12 limit 2^12 | |
| Benchmarking lexsort (f32, f32) 2^12 limit 2^12: Warming up for 3.0000 s | |
| Benchmarking lexsort (f32, f32) 2^12 limit 2^12: Collecting 100 samples in estimated 5.4985 s (25k iterations) | |
| Benchmarking lexsort (f32, f32) 2^12 limit 2^12: Analyzing | |
| lexsort (f32, f32) 2^12 limit 2^12 | |
| time: [218.82 µs 218.85 µs 218.88 µs] | |
| change: [+5.0938% +5.1328% +5.1658%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 1 (1.00%) low mild | |
| 4 (4.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking lexsort (f32, f32) nulls 2^12 limit 10 | |
| Benchmarking lexsort (f32, f32) nulls 2^12 limit 10: Warming up for 3.0000 s | |
| Benchmarking lexsort (f32, f32) nulls 2^12 limit 10: Collecting 100 samples in estimated 5.1887 s (81k iterations) | |
| Benchmarking lexsort (f32, f32) nulls 2^12 limit 10: Analyzing | |
| lexsort (f32, f32) nulls 2^12 limit 10 | |
| time: [64.139 µs 64.392 µs 64.762 µs] | |
| change: [-2.8296% -2.6769% -2.4110%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 3 (3.00%) low mild | |
| 2 (2.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking lexsort (f32, f32) nulls 2^12 limit 100 | |
| Benchmarking lexsort (f32, f32) nulls 2^12 limit 100: Warming up for 3.0000 s | |
| Benchmarking lexsort (f32, f32) nulls 2^12 limit 100: Collecting 100 samples in estimated 5.2991 s (81k iterations) | |
| Benchmarking lexsort (f32, f32) nulls 2^12 limit 100: Analyzing | |
| lexsort (f32, f32) nulls 2^12 limit 100 | |
| time: [65.338 µs 65.689 µs 66.110 µs] | |
| change: [-2.3328% -1.8503% -1.0632%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 1 (1.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking lexsort (f32, f32) nulls 2^12 limit 1000 | |
| Benchmarking lexsort (f32, f32) nulls 2^12 limit 1000: Warming up for 3.0000 s | |
| Benchmarking lexsort (f32, f32) nulls 2^12 limit 1000: Collecting 100 samples in estimated 5.0447 s (66k iterations) | |
| Benchmarking lexsort (f32, f32) nulls 2^12 limit 1000: Analyzing | |
| lexsort (f32, f32) nulls 2^12 limit 1000 | |
| time: [76.570 µs 76.597 µs 76.625 µs] | |
| change: [+3.9749% +4.0999% +4.2199%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 1 (1.00%) low severe | |
| 1 (1.00%) high severe | |
| Benchmarking lexsort (f32, f32) nulls 2^12 limit 2^12 | |
| Benchmarking lexsort (f32, f32) nulls 2^12 limit 2^12: Warming up for 3.0000 s | |
| Benchmarking lexsort (f32, f32) nulls 2^12 limit 2^12: Collecting 100 samples in estimated 5.9466 s (30k iterations) | |
| Benchmarking lexsort (f32, f32) nulls 2^12 limit 2^12: Analyzing | |
| lexsort (f32, f32) nulls 2^12 limit 2^12 | |
| time: [196.19 µs 196.23 µs 196.28 µs] | |
| change: [-1.2108% -0.9794% -0.8488%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) high mild | |
| Benchmarking rank f32 2^12 | |
| Benchmarking rank f32 2^12: Warming up for 3.0000 s | |
| Benchmarking rank f32 2^12: Collecting 100 samples in estimated 5.1409 s (91k iterations) | |
| Benchmarking rank f32 2^12: Analyzing | |
| rank f32 2^12 time: [56.573 µs 56.580 µs 56.588 µs] | |
| change: [-0.1623% -0.1264% -0.0901%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 2 (2.00%) low mild | |
| 2 (2.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking rank f32 nulls 2^12 | |
| Benchmarking rank f32 nulls 2^12: Warming up for 3.0000 s | |
| Benchmarking rank f32 nulls 2^12: Collecting 100 samples in estimated 5.1273 s (167k iterations) | |
| Benchmarking rank f32 nulls 2^12: Analyzing | |
| rank f32 nulls 2^12 time: [30.778 µs 30.784 µs 30.790 µs] | |
| change: [-0.9325% -0.8979% -0.8702%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Benchmarking rank string[10] 2^12 | |
| Benchmarking rank string[10] 2^12: Warming up for 3.0000 s | |
| Benchmarking rank string[10] 2^12: Collecting 100 samples in estimated 5.2243 s (20k iterations) | |
| Benchmarking rank string[10] 2^12: Analyzing | |
| rank string[10] 2^12 time: [277.27 µs 277.76 µs 278.29 µs] | |
| change: [+5.5955% +5.9620% +6.3031%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 15 outliers among 100 measurements (15.00%) | |
| 5 (5.00%) low severe | |
| 10 (10.00%) low mild | |
| Benchmarking rank string[10] nulls 2^12 | |
| Benchmarking rank string[10] nulls 2^12: Warming up for 3.0000 s | |
| Benchmarking rank string[10] nulls 2^12: Collecting 100 samples in estimated 5.3864 s (45k iterations) | |
| Benchmarking rank string[10] nulls 2^12: Analyzing | |
| rank string[10] nulls 2^12 | |
| time: [118.38 µs 118.43 µs 118.48 µs] | |
| change: [-4.6771% -4.5838% -4.5006%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 1 (1.00%) high mild | |
| 1 (1.00%) high severe | |
| Running benches/string_dictionary_builder.rs (target/x86_64-unknown-linux-gnu/release/deps/string_dictionary_builder-6a29fbece8827872) | |
| Benchmarking string_dictionary_builder/(dict_size:20, len:1000, key_len: 5) | |
| Benchmarking string_dictionary_builder/(dict_size:20, len:1000, key_len: 5): Warming up for 3.0000 s | |
| Benchmarking string_dictionary_builder/(dict_size:20, len:1000, key_len: 5): Collecting 100 samples in estimated 5.0181 s (429k iterations) | |
| Benchmarking string_dictionary_builder/(dict_size:20, len:1000, key_len: 5): Analyzing | |
| string_dictionary_builder/(dict_size:20, len:1000, key_len: 5) | |
| time: [11.692 µs 11.704 µs 11.716 µs] | |
| change: [-36.657% -36.343% -36.128%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 1 (1.00%) low mild | |
| 2 (2.00%) high mild | |
| Benchmarking string_dictionary_builder/(dict_size:100, len:1000, key_len: 5) | |
| Benchmarking string_dictionary_builder/(dict_size:100, len:1000, key_len: 5): Warming up for 3.0000 s | |
| Benchmarking string_dictionary_builder/(dict_size:100, len:1000, key_len: 5): Collecting 100 samples in estimated 5.0056 s (343k iterations) | |
| Benchmarking string_dictionary_builder/(dict_size:100, len:1000, key_len: 5): Analyzing | |
| string_dictionary_builder/(dict_size:100, len:1000, key_len: 5) | |
| time: [14.581 µs 14.607 µs 14.635 µs] | |
| change: [-30.138% -29.888% -29.581%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) high severe | |
| Benchmarking string_dictionary_builder/(dict_size:100, len:1000, key_len: 10) | |
| Benchmarking string_dictionary_builder/(dict_size:100, len:1000, key_len: 10): Warming up for 3.0000 s | |
| Benchmarking string_dictionary_builder/(dict_size:100, len:1000, key_len: 10): Collecting 100 samples in estimated 5.0052 s (343k iterations) | |
| Benchmarking string_dictionary_builder/(dict_size:100, len:1000, key_len: 10): Analyzing | |
| string_dictionary_builder/(dict_size:100, len:1000, key_len: 10) | |
| time: [14.577 µs 14.626 µs 14.694 µs] | |
| change: [-29.733% -29.459% -29.026%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 2 (2.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking string_dictionary_builder/(dict_size:100, len:10000, key_len: 10) | |
| Benchmarking string_dictionary_builder/(dict_size:100, len:10000, key_len: 10): Warming up for 3.0000 s | |
| Benchmarking string_dictionary_builder/(dict_size:100, len:10000, key_len: 10): Collecting 100 samples in estimated 5.2464 s (45k iterations) | |
| Benchmarking string_dictionary_builder/(dict_size:100, len:10000, key_len: 10): Analyzing | |
| string_dictionary_builder/(dict_size:100, len:10000, key_len: 10) | |
| time: [115.21 µs 115.44 µs 115.85 µs] | |
| change: [-35.186% -34.915% -34.509%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 1 (1.00%) low mild | |
| 3 (3.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking string_dictionary_builder/(dict_size:100, len:10000, key_len: 100) | |
| Benchmarking string_dictionary_builder/(dict_size:100, len:10000, key_len: 100): Warming up for 3.0000 s | |
| Benchmarking string_dictionary_builder/(dict_size:100, len:10000, key_len: 100): Collecting 100 samples in estimated 6.9310 s (15k iterations) | |
| Benchmarking string_dictionary_builder/(dict_size:100, len:10000, key_len: 100): Analyzing | |
| string_dictionary_builder/(dict_size:100, len:10000, key_len: 100) | |
| time: [457.01 µs 457.31 µs 457.77 µs] | |
| change: [-11.539% -11.422% -11.230%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high severe | |
| Running benches/string_run_builder.rs (target/x86_64-unknown-linux-gnu/release/deps/string_run_builder-3fced6c5560872d7) | |
| Benchmarking string_run_builder/(run_array_len:1000, physical_array_len:20, string_len: 5) | |
| Benchmarking string_run_builder/(run_array_len:1000, physical_array_len:20, string_len: 5): Warming up for 3.0000 s | |
| Benchmarking string_run_builder/(run_array_len:1000, physical_array_len:20, string_len: 5): Collecting 100 samples in estimated 5.0067 s (1.0M iterations) | |
| Benchmarking string_run_builder/(run_array_len:1000, physical_array_len:20, string_len: 5): Analyzing | |
| string_run_builder/(run_array_len:1000, physical_array_len:20, string_len: 5) | |
| time: [4.9208 µs 4.9319 µs 4.9429 µs] | |
| change: [-1.0706% -0.4296% +0.2400%] (p = 0.20 > 0.05) | |
| No change in performance detected. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) low mild | |
| Benchmarking string_run_builder/(run_array_len:1000, physical_array_len:100, string_len: 5) | |
| Benchmarking string_run_builder/(run_array_len:1000, physical_array_len:100, string_len: 5): Warming up for 3.0000 s | |
| Benchmarking string_run_builder/(run_array_len:1000, physical_array_len:100, string_len: 5): Collecting 100 samples in estimated 5.0209 s (828k iterations) | |
| Benchmarking string_run_builder/(run_array_len:1000, physical_array_len:100, string_len: 5): Analyzing | |
| string_run_builder/(run_array_len:1000, physical_array_len:100, string_len: 5) | |
| time: [6.0162 µs 6.0469 µs 6.0846 µs] | |
| change: [+1.6122% +2.2324% +2.8859%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 1 (1.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking string_run_builder/(run_array_len:1000, physical_array_len:100, string_len: 10) | |
| Benchmarking string_run_builder/(run_array_len:1000, physical_array_len:100, string_len: 10): Warming up for 3.0000 s | |
| Benchmarking string_run_builder/(run_array_len:1000, physical_array_len:100, string_len: 10): Collecting 100 samples in estimated 5.0047 s (869k iterations) | |
| Benchmarking string_run_builder/(run_array_len:1000, physical_array_len:100, string_len: 10): Analyzing | |
| string_run_builder/(run_array_len:1000, physical_array_len:100, string_len: 10) | |
| time: [5.7285 µs 5.7555 µs 5.7885 µs] | |
| change: [+2.9934% +3.7268% +4.7392%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 2 (2.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking string_run_builder/(run_array_len:10000, physical_array_len:100, string_len: 10) | |
| Benchmarking string_run_builder/(run_array_len:10000, physical_array_len:100, string_len: 10): Warming up for 3.0000 s | |
| Benchmarking string_run_builder/(run_array_len:10000, physical_array_len:100, string_len: 10): Collecting 100 samples in estimated 5.1422 s (131k iterations) | |
| Benchmarking string_run_builder/(run_array_len:10000, physical_array_len:100, string_len: 10): Analyzing | |
| string_run_builder/(run_array_len:10000, physical_array_len:100, string_len: 10) | |
| time: [39.170 µs 39.189 µs 39.209 µs] | |
| change: [+6.7299% +6.9657% +7.1146%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 1 (1.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking string_run_builder/(run_array_len:10000, physical_array_len:100, string_len: 100) | |
| Benchmarking string_run_builder/(run_array_len:10000, physical_array_len:100, string_len: 100): Warming up for 3.0000 s | |
| Benchmarking string_run_builder/(run_array_len:10000, physical_array_len:100, string_len: 100): Collecting 100 samples in estimated 5.0026 s (71k iterations) | |
| Benchmarking string_run_builder/(run_array_len:10000, physical_array_len:100, string_len: 100): Analyzing | |
| string_run_builder/(run_array_len:10000, physical_array_len:100, string_len: 100) | |
| time: [71.126 µs 71.239 µs 71.346 µs] | |
| change: [-5.5939% -5.3436% -5.1104%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 1 (1.00%) low severe | |
| 3 (3.00%) low mild | |
| Running benches/string_run_iterator.rs (target/x86_64-unknown-linux-gnu/release/deps/string_run_iterator-d05c0e6eb5e7756d) | |
| Benchmarking string_run_iterator/(run_array_len:1024, physical_array_len:256, string_len: 5) | |
| Benchmarking string_run_iterator/(run_array_len:1024, physical_array_len:256, string_len: 5): Warming up for 3.0000 s | |
| Benchmarking string_run_iterator/(run_array_len:1024, physical_array_len:256, string_len: 5): Collecting 100 samples in estimated 5.0021 s (2.0M iterations) | |
| Benchmarking string_run_iterator/(run_array_len:1024, physical_array_len:256, string_len: 5): Analyzing | |
| string_run_iterator/(run_array_len:1024, physical_array_len:256, string_len: 5) | |
| time: [2.4461 µs 2.4537 µs 2.4617 µs] | |
| change: [-1.6532% -1.3339% -1.0416%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high mild | |
| Benchmarking string_run_iterator/(run_array_len:1024, physical_array_len:256, string_len: 25) | |
| Benchmarking string_run_iterator/(run_array_len:1024, physical_array_len:256, string_len: 25): Warming up for 3.0000 s | |
| Benchmarking string_run_iterator/(run_array_len:1024, physical_array_len:256, string_len: 25): Collecting 100 samples in estimated 5.0028 s (2.0M iterations) | |
| Benchmarking string_run_iterator/(run_array_len:1024, physical_array_len:256, string_len: 25): Analyzing | |
| string_run_iterator/(run_array_len:1024, physical_array_len:256, string_len: 25) | |
| time: [2.4362 µs 2.4424 µs 2.4491 µs] | |
| change: [-2.1097% -1.8683% -1.6292%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking string_run_iterator/(run_array_len:1024, physical_array_len:256, string_len: 100) | |
| Benchmarking string_run_iterator/(run_array_len:1024, physical_array_len:256, string_len: 100): Warming up for 3.0000 s | |
| Benchmarking string_run_iterator/(run_array_len:1024, physical_array_len:256, string_len: 100): Collecting 100 samples in estimated 5.0097 s (2.1M iterations) | |
| Benchmarking string_run_iterator/(run_array_len:1024, physical_array_len:256, string_len: 100): Analyzing | |
| string_run_iterator/(run_array_len:1024, physical_array_len:256, string_len: 100) | |
| time: [2.4363 µs 2.4449 µs 2.4558 µs] | |
| change: [-1.7391% -1.3672% -0.9849%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 3 (3.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking string_run_iterator/(run_array_len:2048, physical_array_len:512, string_len: 5) | |
| Benchmarking string_run_iterator/(run_array_len:2048, physical_array_len:512, string_len: 5): Warming up for 3.0000 s | |
| Benchmarking string_run_iterator/(run_array_len:2048, physical_array_len:512, string_len: 5): Collecting 100 samples in estimated 5.0070 s (1.0M iterations) | |
| Benchmarking string_run_iterator/(run_array_len:2048, physical_array_len:512, string_len: 5): Analyzing | |
| string_run_iterator/(run_array_len:2048, physical_array_len:512, string_len: 5) | |
| time: [4.8996 µs 4.9124 µs 4.9262 µs] | |
| change: [-1.8393% -1.6036% -1.3999%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 3 (3.00%) high mild | |
| Benchmarking string_run_iterator/(run_array_len:2048, physical_array_len:512, string_len: 25) | |
| Benchmarking string_run_iterator/(run_array_len:2048, physical_array_len:512, string_len: 25): Warming up for 3.0000 s | |
| Benchmarking string_run_iterator/(run_array_len:2048, physical_array_len:512, string_len: 25): Collecting 100 samples in estimated 5.0028 s (1.0M iterations) | |
| Benchmarking string_run_iterator/(run_array_len:2048, physical_array_len:512, string_len: 25): Analyzing | |
| string_run_iterator/(run_array_len:2048, physical_array_len:512, string_len: 25) | |
| time: [4.9037 µs 4.9158 µs 4.9286 µs] | |
| change: [-1.6371% -1.4350% -1.2107%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking string_run_iterator/(run_array_len:2048, physical_array_len:512, string_len: 100) | |
| Benchmarking string_run_iterator/(run_array_len:2048, physical_array_len:512, string_len: 100): Warming up for 3.0000 s | |
| Benchmarking string_run_iterator/(run_array_len:2048, physical_array_len:512, string_len: 100): Collecting 100 samples in estimated 5.0174 s (1.0M iterations) | |
| Benchmarking string_run_iterator/(run_array_len:2048, physical_array_len:512, string_len: 100): Analyzing | |
| string_run_iterator/(run_array_len:2048, physical_array_len:512, string_len: 100) | |
| time: [4.9189 µs 4.9289 µs 4.9388 µs] | |
| change: [-1.5615% -1.3439% -1.1199%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 3 (3.00%) high mild | |
| Benchmarking string_run_iterator/(run_array_len:4096, physical_array_len:1024, string_len: 5) | |
| Benchmarking string_run_iterator/(run_array_len:4096, physical_array_len:1024, string_len: 5): Warming up for 3.0000 s | |
| Benchmarking string_run_iterator/(run_array_len:4096, physical_array_len:1024, string_len: 5): Collecting 100 samples in estimated 5.0339 s (510k iterations) | |
| Benchmarking string_run_iterator/(run_array_len:4096, physical_array_len:1024, string_len: 5): Analyzing | |
| string_run_iterator/(run_array_len:4096, physical_array_len:1024, string_len: 5) | |
| time: [9.8228 µs 9.8362 µs 9.8496 µs] | |
| change: [-1.7334% -1.6042% -1.4721%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking string_run_iterator/(run_array_len:4096, physical_array_len:1024, string_len: 25) | |
| Benchmarking string_run_iterator/(run_array_len:4096, physical_array_len:1024, string_len: 25): Warming up for 3.0000 s | |
| Benchmarking string_run_iterator/(run_array_len:4096, physical_array_len:1024, string_len: 25): Collecting 100 samples in estimated 5.0170 s (510k iterations) | |
| Benchmarking string_run_iterator/(run_array_len:4096, physical_array_len:1024, string_len: 25): Analyzing | |
| string_run_iterator/(run_array_len:4096, physical_array_len:1024, string_len: 25) | |
| time: [9.8107 µs 9.8275 µs 9.8440 µs] | |
| change: [-1.8287% -1.6614% -1.4974%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 2 (2.00%) low mild | |
| 4 (4.00%) high mild | |
| Benchmarking string_run_iterator/(run_array_len:4096, physical_array_len:1024, string_len: 100) | |
| Benchmarking string_run_iterator/(run_array_len:4096, physical_array_len:1024, string_len: 100): Warming up for 3.0000 s | |
| Benchmarking string_run_iterator/(run_array_len:4096, physical_array_len:1024, string_len: 100): Collecting 100 samples in estimated 5.0270 s (510k iterations) | |
| Benchmarking string_run_iterator/(run_array_len:4096, physical_array_len:1024, string_len: 100): Analyzing | |
| string_run_iterator/(run_array_len:4096, physical_array_len:1024, string_len: 100) | |
| time: [9.8384 µs 9.8514 µs 9.8646 µs] | |
| change: [-1.5275% -1.3988% -1.2600%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Running benches/substring_kernels.rs (target/x86_64-unknown-linux-gnu/release/deps/substring_kernels-ea30f6aaf2ef52d5) | |
| Benchmarking substring utf8 (start = 0, length = None) | |
| Benchmarking substring utf8 (start = 0, length = None): Warming up for 3.0000 s | |
| Benchmarking substring utf8 (start = 0, length = None): Collecting 100 samples in estimated 6.6257 s (200 iterations) | |
| Benchmarking substring utf8 (start = 0, length = None): Analyzing | |
| substring utf8 (start = 0, length = None) | |
| time: [32.983 ms 33.073 ms 33.181 ms] | |
| change: [-4.7433% -4.3137% -3.8786%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 3 (3.00%) high mild | |
| 8 (8.00%) high severe | |
| Benchmarking substring utf8 (start = 1, length = str_len - 1) | |
| Benchmarking substring utf8 (start = 1, length = str_len - 1): Warming up for 3.0000 s | |
| Benchmarking substring utf8 (start = 1, length = str_len - 1): Collecting 100 samples in estimated 6.6615 s (200 iterations) | |
| Benchmarking substring utf8 (start = 1, length = str_len - 1): Analyzing | |
| substring utf8 (start = 1, length = str_len - 1) | |
| time: [33.140 ms 33.239 ms 33.356 ms] | |
| change: [-5.5107% -4.8324% -4.2099%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 13 outliers among 100 measurements (13.00%) | |
| 4 (4.00%) high mild | |
| 9 (9.00%) high severe | |
| Benchmarking substring utf8 by char | |
| Benchmarking substring utf8 by char: Warming up for 3.0000 s | |
| Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 6.9s, or reduce sample count to 70. | |
| Benchmarking substring utf8 by char: Collecting 100 samples in estimated 6.8856 s (100 iterations) | |
| Benchmarking substring utf8 by char: Analyzing | |
| substring utf8 by char time: [68.633 ms 68.719 ms 68.817 ms] | |
| change: [-21.605% -21.446% -21.294%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 2 (2.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking substring fixed size binary array | |
| Benchmarking substring fixed size binary array: Warming up for 3.0000 s | |
| Benchmarking substring fixed size binary array: Collecting 100 samples in estimated 6.5295 s (200 iterations) | |
| Benchmarking substring fixed size binary array: Analyzing | |
| substring fixed size binary array | |
| time: [32.423 ms 32.502 ms 32.602 ms] | |
| change: [-5.0154% -4.5626% -4.1215%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 3 (3.00%) high mild | |
| 8 (8.00%) high severe | |
| Running benches/take_kernels.rs (target/x86_64-unknown-linux-gnu/release/deps/take_kernels-49cae257c3e4099c) | |
| Benchmarking take i32 512 | |
| Benchmarking take i32 512: Warming up for 3.0000 s | |
| Benchmarking take i32 512: Collecting 100 samples in estimated 5.0012 s (17M iterations) | |
| Benchmarking take i32 512: Analyzing | |
| take i32 512 time: [296.50 ns 296.65 ns 296.80 ns] | |
| change: [-0.0779% -0.0016% +0.0661%] (p = 0.97 > 0.05) | |
| No change in performance detected. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 2 (2.00%) low severe | |
| 2 (2.00%) high mild | |
| Benchmarking take i32 1024 | |
| Benchmarking take i32 1024: Warming up for 3.0000 s | |
| Benchmarking take i32 1024: Collecting 100 samples in estimated 5.0034 s (6.7M iterations) | |
| Benchmarking take i32 1024: Analyzing | |
| take i32 1024 time: [747.25 ns 747.53 ns 747.81 ns] | |
| change: [+61.672% +61.875% +62.014%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 3 (3.00%) low mild | |
| 2 (2.00%) high mild | |
| Benchmarking take i32 null indices 1024 | |
| Benchmarking take i32 null indices 1024: Warming up for 3.0000 s | |
| Benchmarking take i32 null indices 1024: Collecting 100 samples in estimated 5.0017 s (7.2M iterations) | |
| Benchmarking take i32 null indices 1024: Analyzing | |
| take i32 null indices 1024 | |
| time: [698.12 ns 699.27 ns 701.54 ns] | |
| change: [-0.7154% -0.1039% +0.3928%] (p = 0.73 > 0.05) | |
| No change in performance detected. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 3 (3.00%) low mild | |
| 4 (4.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking take i32 null values 1024 | |
| Benchmarking take i32 null values 1024: Warming up for 3.0000 s | |
| Benchmarking take i32 null values 1024: Collecting 100 samples in estimated 5.0022 s (3.3M iterations) | |
| Benchmarking take i32 null values 1024: Analyzing | |
| take i32 null values 1024 | |
| time: [1.5399 µs 1.5403 µs 1.5407 µs] | |
| change: [-4.7019% -4.6261% -4.5594%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 2 (2.00%) low mild | |
| 5 (5.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking take i32 null values null indices 1024 | |
| Benchmarking take i32 null values null indices 1024: Warming up for 3.0000 s | |
| Benchmarking take i32 null values null indices 1024: Collecting 100 samples in estimated 5.0025 s (2.5M iterations) | |
| Benchmarking take i32 null values null indices 1024: Analyzing | |
| take i32 null values null indices 1024 | |
| time: [1.9656 µs 1.9662 µs 1.9668 µs] | |
| change: [-1.9260% -1.8964% -1.8653%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 2 (2.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking take check bounds i32 512 | |
| Benchmarking take check bounds i32 512: Warming up for 3.0000 s | |
| Benchmarking take check bounds i32 512: Collecting 100 samples in estimated 5.0000 s (11M iterations) | |
| Benchmarking take check bounds i32 512: Analyzing | |
| take check bounds i32 512 | |
| time: [450.39 ns 450.57 ns 450.75 ns] | |
| change: [-1.6964% -1.6296% -1.5637%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 1 (1.00%) low severe | |
| 4 (4.00%) low mild | |
| 1 (1.00%) high mild | |
| Benchmarking take check bounds i32 1024 | |
| Benchmarking take check bounds i32 1024: Warming up for 3.0000 s | |
| Benchmarking take check bounds i32 1024: Collecting 100 samples in estimated 5.0008 s (6.5M iterations) | |
| Benchmarking take check bounds i32 1024: Analyzing | |
| take check bounds i32 1024 | |
| time: [772.94 ns 773.25 ns 773.56 ns] | |
| change: [+1.1249% +1.2108% +1.2777%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high mild | |
| Benchmarking take bool 512 | |
| Benchmarking take bool 512: Warming up for 3.0000 s | |
| Benchmarking take bool 512: Collecting 100 samples in estimated 5.0017 s (9.1M iterations) | |
| Benchmarking take bool 512: Analyzing | |
| take bool 512 time: [551.15 ns 551.64 ns 552.17 ns] | |
| change: [-5.9470% -5.7339% -5.4782%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 6 (6.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking take bool 1024 | |
| Benchmarking take bool 1024: Warming up for 3.0000 s | |
| Benchmarking take bool 1024: Collecting 100 samples in estimated 5.0039 s (4.7M iterations) | |
| Benchmarking take bool 1024: Analyzing | |
| take bool 1024 time: [1.0676 µs 1.0682 µs 1.0688 µs] | |
| change: [-3.6684% -3.5921% -3.5185%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 3 (3.00%) high mild | |
| Benchmarking take bool null indices 1024 | |
| Benchmarking take bool null indices 1024: Warming up for 3.0000 s | |
| Benchmarking take bool null indices 1024: Collecting 100 samples in estimated 5.0042 s (3.9M iterations) | |
| Benchmarking take bool null indices 1024: Analyzing | |
| take bool null indices 1024 | |
| time: [1.2910 µs 1.2914 µs 1.2919 µs] | |
| change: [+0.4644% +0.5039% +0.5529%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 1 (1.00%) low mild | |
| 3 (3.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking take bool null values 1024 | |
| Benchmarking take bool null values 1024: Warming up for 3.0000 s | |
| Benchmarking take bool null values 1024: Collecting 100 samples in estimated 5.0026 s (2.5M iterations) | |
| Benchmarking take bool null values 1024: Analyzing | |
| take bool null values 1024 | |
| time: [1.9860 µs 1.9890 µs 1.9948 µs] | |
| change: [-4.2216% -4.0692% -3.9080%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 2 (2.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking take bool null values null indices 1024 | |
| Benchmarking take bool null values null indices 1024: Warming up for 3.0000 s | |
| Benchmarking take bool null values null indices 1024: Collecting 100 samples in estimated 5.0014 s (2.0M iterations) | |
| Benchmarking take bool null values null indices 1024: Analyzing | |
| take bool null values null indices 1024 | |
| time: [2.5120 µs 2.5128 µs 2.5138 µs] | |
| change: [+1.0940% +1.1408% +1.1854%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 1 (1.00%) low mild | |
| 3 (3.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking take str 512 | |
| Benchmarking take str 512: Warming up for 3.0000 s | |
| Benchmarking take str 512: Collecting 100 samples in estimated 5.0125 s (687k iterations) | |
| Benchmarking take str 512: Analyzing | |
| take str 512 time: [7.2945 µs 7.2985 µs 7.3022 µs] | |
| change: [+1.8481% +1.9354% +2.0333%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high severe | |
| Benchmarking take str 1024 | |
| Benchmarking take str 1024: Warming up for 3.0000 s | |
| Benchmarking take str 1024: Collecting 100 samples in estimated 5.0797 s (318k iterations) | |
| Benchmarking take str 1024: Analyzing | |
| take str 1024 time: [15.993 µs 15.997 µs 16.001 µs] | |
| change: [-1.7566% -1.6511% -1.5799%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 9 (9.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking take str null indices 512 | |
| Benchmarking take str null indices 512: Warming up for 3.0000 s | |
| Benchmarking take str null indices 512: Collecting 100 samples in estimated 5.0065 s (1.2M iterations) | |
| Benchmarking take str null indices 512: Analyzing | |
| take str null indices 512 | |
| time: [4.1816 µs 4.1864 µs 4.1960 µs] | |
| change: [-7.2324% -7.0934% -6.8786%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 3 (3.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking take str null indices 1024 | |
| Benchmarking take str null indices 1024: Warming up for 3.0000 s | |
| Benchmarking take str null indices 1024: Collecting 100 samples in estimated 5.0413 s (475k iterations) | |
| Benchmarking take str null indices 1024: Analyzing | |
| take str null indices 1024 | |
| time: [10.708 µs 10.716 µs 10.727 µs] | |
| change: [+0.0633% +0.3980% +0.8371%] (p = 0.03 < 0.05) | |
| Change within noise threshold. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high severe | |
| Benchmarking take str null values 1024 | |
| Benchmarking take str null values 1024: Warming up for 3.0000 s | |
| Benchmarking take str null values 1024: Collecting 100 samples in estimated 5.0017 s (525k iterations) | |
| Benchmarking take str null values 1024: Analyzing | |
| take str null values 1024 | |
| time: [9.5455 µs 9.5519 µs 9.5572 µs] | |
| change: [-6.4445% -6.3640% -6.2968%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 30 outliers among 100 measurements (30.00%) | |
| 7 (7.00%) low severe | |
| 14 (14.00%) low mild | |
| 6 (6.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking take str null values null indices 1024 | |
| Benchmarking take str null values null indices 1024: Warming up for 3.0000 s | |
| Benchmarking take str null values null indices 1024: Collecting 100 samples in estimated 5.0113 s (828k iterations) | |
| Benchmarking take str null values null indices 1024: Analyzing | |
| take str null values null indices 1024 | |
| time: [6.0202 µs 6.0325 µs 6.0538 µs] | |
| change: [+0.2970% +0.6957% +1.2458%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 3 (3.00%) high severe | |
| Benchmarking take stringview 512 | |
| Benchmarking take stringview 512: Warming up for 3.0000 s | |
| Benchmarking take stringview 512: Collecting 100 samples in estimated 5.0010 s (14M iterations) | |
| Benchmarking take stringview 512: Analyzing | |
| take stringview 512 time: [352.40 ns 352.49 ns 352.59 ns] | |
| change: [+2.6253% +2.8082% +2.9260%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 1 (1.00%) low severe | |
| 4 (4.00%) low mild | |
| 3 (3.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking take stringview 1024 | |
| Benchmarking take stringview 1024: Warming up for 3.0000 s | |
| Benchmarking take stringview 1024: Collecting 100 samples in estimated 5.0008 s (8.3M iterations) | |
| Benchmarking take stringview 1024: Analyzing | |
| take stringview 1024 time: [604.76 ns 604.93 ns 605.11 ns] | |
| change: [-8.7566% -8.1145% -7.6412%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 3 (3.00%) high mild | |
| Benchmarking take stringview null indices 512 | |
| Benchmarking take stringview null indices 512: Warming up for 3.0000 s | |
| Benchmarking take stringview null indices 512: Collecting 100 samples in estimated 5.0001 s (8.6M iterations) | |
| Benchmarking take stringview null indices 512: Analyzing | |
| take stringview null indices 512 | |
| time: [577.82 ns 577.97 ns 578.15 ns] | |
| change: [+9.7841% +9.8640% +9.9345%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 6 (6.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking take stringview null indices 1024 | |
| Benchmarking take stringview null indices 1024: Warming up for 3.0000 s | |
| Benchmarking take stringview null indices 1024: Collecting 100 samples in estimated 5.0030 s (4.6M iterations) | |
| Benchmarking take stringview null indices 1024: Analyzing | |
| take stringview null indices 1024 | |
| time: [1.0826 µs 1.0829 µs 1.0832 µs] | |
| change: [+23.141% +23.285% +23.403%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 1 (1.00%) low severe | |
| 4 (4.00%) high mild | |
| 7 (7.00%) high severe | |
| Benchmarking take stringview null values 1024 | |
| Benchmarking take stringview null values 1024: Warming up for 3.0000 s | |
| Benchmarking take stringview null values 1024: Collecting 100 samples in estimated 5.0012 s (3.1M iterations) | |
| Benchmarking take stringview null values 1024: Analyzing | |
| take stringview null values 1024 | |
| time: [1.5922 µs 1.5926 µs 1.5931 µs] | |
| change: [-3.8975% -3.8582% -3.8210%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 1 (1.00%) low mild | |
| 1 (1.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking take stringview null values null indices 1024 | |
| Benchmarking take stringview null values null indices 1024: Warming up for 3.0000 s | |
| Benchmarking take stringview null values null indices 1024: Collecting 100 samples in estimated 5.0103 s (2.3M iterations) | |
| Benchmarking take stringview null values null indices 1024: Analyzing | |
| take stringview null values null indices 1024 | |
| time: [2.2264 µs 2.2284 µs 2.2304 µs] | |
| change: [+5.1643% +5.2359% +5.3185%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Benchmarking take primitive run logical len: 1024, physical len: 512, indices: 1024 | |
| Benchmarking take primitive run logical len: 1024, physical len: 512, indices: 1024: Warming up for 3.0000 s | |
| Benchmarking take primitive run logical len: 1024, physical len: 512, indices: 1024: Collecting 100 samples in estimated 5.1023 s (227k iterations) | |
| Benchmarking take primitive run logical len: 1024, physical len: 512, indices: 1024: Analyzing | |
| take primitive run logical len: 1024, physical len: 512, indices: 1024 | |
| time: [22.482 µs 22.489 µs 22.496 µs] | |
| change: [+19.481% +19.771% +19.938%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 7 (7.00%) low mild | |
| 4 (4.00%) high mild | |
| 1 (1.00%) high severe | |
| Running benches/decimal_overflow.rs (target/x86_64-unknown-linux-gnu/release/deps/decimal_overflow-09f78cc00d18406c) | |
| Benchmarking validate_decimal_precision_128 | |
| Benchmarking validate_decimal_precision_128: Warming up for 3.0000 s | |
| Benchmarking validate_decimal_precision_128: Collecting 100 samples in estimated 5.0003 s (43M iterations) | |
| Benchmarking validate_decimal_precision_128: Analyzing | |
| validate_decimal_precision_128 | |
| time: [113.00 ns 113.45 ns 113.90 ns] | |
| change: [+11.368% +11.987% +12.687%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 1 (1.00%) low mild | |
| 3 (3.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking null_if_overflow_precision_128 | |
| Benchmarking null_if_overflow_precision_128: Warming up for 3.0000 s | |
| Benchmarking null_if_overflow_precision_128: Collecting 100 samples in estimated 5.0615 s (258k iterations) | |
| Benchmarking null_if_overflow_precision_128: Analyzing | |
| null_if_overflow_precision_128 | |
| time: [19.647 µs 19.657 µs 19.669 µs] | |
| change: [-2.9657% -2.7542% -2.4329%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 1 (1.00%) low mild | |
| 3 (3.00%) high mild | |
| 7 (7.00%) high severe | |
| Benchmarking validate_decimal_precision_256 | |
| Benchmarking validate_decimal_precision_256: Warming up for 3.0000 s | |
| Benchmarking validate_decimal_precision_256: Collecting 100 samples in estimated 5.0004 s (16M iterations) | |
| Benchmarking validate_decimal_precision_256: Analyzing | |
| validate_decimal_precision_256 | |
| time: [311.47 ns 311.67 ns 311.88 ns] | |
| change: [+0.4724% +0.5928% +0.6881%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) high mild | |
| Benchmarking null_if_overflow_precision_256 | |
| Benchmarking null_if_overflow_precision_256: Warming up for 3.0000 s | |
| Benchmarking null_if_overflow_precision_256: Collecting 100 samples in estimated 5.0261 s (136k iterations) | |
| Benchmarking null_if_overflow_precision_256: Analyzing | |
| null_if_overflow_precision_256 | |
| time: [36.855 µs 36.864 µs 36.873 µs] | |
| change: [-7.8468% -7.7846% -7.7284%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 6 (6.00%) high mild | |
| 3 (3.00%) high severe | |
| Running benches/fixed_size_list_array.rs (target/x86_64-unknown-linux-gnu/release/deps/fixed_size_list_array-aaa1e33d3b9bf656) | |
| Benchmarking fixed_size_list_array(len: 4096, value_len: 1) | |
| Benchmarking fixed_size_list_array(len: 4096, value_len: 1): Warming up for 3.0000 s | |
| Benchmarking fixed_size_list_array(len: 4096, value_len: 1): Collecting 100 samples in estimated 5.4466 s (40k iterations) | |
| Benchmarking fixed_size_list_array(len: 4096, value_len: 1): Analyzing | |
| fixed_size_list_array(len: 4096, value_len: 1) | |
| time: [136.25 µs 136.45 µs 136.73 µs] | |
| change: [-10.998% -10.546% -10.192%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 2 (2.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking fixed_size_list_array(len: 4096, value_len: 32) | |
| Benchmarking fixed_size_list_array(len: 4096, value_len: 32): Warming up for 3.0000 s | |
| Benchmarking fixed_size_list_array(len: 4096, value_len: 32): Collecting 100 samples in estimated 5.0077 s (1.2M iterations) | |
| Benchmarking fixed_size_list_array(len: 4096, value_len: 32): Analyzing | |
| fixed_size_list_array(len: 4096, value_len: 32) | |
| time: [4.2561 µs 4.2584 µs 4.2607 µs] | |
| change: [-6.8995% -6.5706% -6.3727%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 3 (3.00%) high mild | |
| Benchmarking fixed_size_list_array(len: 4096, value_len: 1024) | |
| Benchmarking fixed_size_list_array(len: 4096, value_len: 1024): Warming up for 3.0000 s | |
| Benchmarking fixed_size_list_array(len: 4096, value_len: 1024): Collecting 100 samples in estimated 5.0001 s (38M iterations) | |
| Benchmarking fixed_size_list_array(len: 4096, value_len: 1024): Analyzing | |
| fixed_size_list_array(len: 4096, value_len: 1024) | |
| time: [133.73 ns 134.37 ns 135.22 ns] | |
| change: [-11.156% -10.955% -10.734%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 3 (3.00%) high severe | |
| Running benches/gc_view_types.rs (target/x86_64-unknown-linux-gnu/release/deps/gc_view_types-8d316e598c2bc95c) | |
| Benchmarking gc view types all | |
| Benchmarking gc view types all: Warming up for 3.0000 s | |
| Benchmarking gc view types all: Collecting 100 samples in estimated 5.6836 s (10k iterations) | |
| Benchmarking gc view types all: Analyzing | |
| gc view types all time: [559.40 µs 562.60 µs 566.61 µs] | |
| change: [-20.373% -19.996% -19.468%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 13 outliers among 100 measurements (13.00%) | |
| 7 (7.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking gc view types slice half | |
| Benchmarking gc view types slice half: Warming up for 3.0000 s | |
| Benchmarking gc view types slice half: Collecting 100 samples in estimated 5.6998 s (20k iterations) | |
| Benchmarking gc view types slice half: Analyzing | |
| gc view types slice half | |
| time: [281.31 µs 281.36 µs 281.42 µs] | |
| change: [-20.289% -20.258% -20.230%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 1 (1.00%) low severe | |
| 2 (2.00%) low mild | |
| 2 (2.00%) high mild | |
| 2 (2.00%) high severe | |
| Running benches/occupancy.rs (target/x86_64-unknown-linux-gnu/release/deps/occupancy-54b2d7b1cf7ac0e1) | |
| Benchmarking occupancy(values: 10, occupancy: 1, null_percent: 0) | |
| Benchmarking occupancy(values: 10, occupancy: 1, null_percent: 0): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 10, occupancy: 1, null_percent: 0): Collecting 100 samples in estimated 5.0003 s (3.1M iterations) | |
| Benchmarking occupancy(values: 10, occupancy: 1, null_percent: 0): Analyzing | |
| occupancy(values: 10, occupancy: 1, null_percent: 0) | |
| time: [1.6068 µs 1.6073 µs 1.6078 µs] | |
| change: [-2.7827% -2.7470% -2.7146%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 3 (3.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking occupancy(values: 10, occupancy: 1, null_percent: 0.1) | |
| Benchmarking occupancy(values: 10, occupancy: 1, null_percent: 0.1): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 10, occupancy: 1, null_percent: 0.1): Collecting 100 samples in estimated 5.0054 s (2.9M iterations) | |
| Benchmarking occupancy(values: 10, occupancy: 1, null_percent: 0.1): Analyzing | |
| occupancy(values: 10, occupancy: 1, null_percent: 0.1) | |
| time: [1.8939 µs 1.8995 µs 1.9050 µs] | |
| change: [-1.4506% -0.7838% -0.2555%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Benchmarking occupancy(values: 10, occupancy: 1, null_percent: 0.5) | |
| Benchmarking occupancy(values: 10, occupancy: 1, null_percent: 0.5): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 10, occupancy: 1, null_percent: 0.5): Collecting 100 samples in estimated 5.0027 s (4.6M iterations) | |
| Benchmarking occupancy(values: 10, occupancy: 1, null_percent: 0.5): Analyzing | |
| occupancy(values: 10, occupancy: 1, null_percent: 0.5) | |
| time: [1.0917 µs 1.0926 µs 1.0936 µs] | |
| change: [-3.4432% -3.3168% -3.1925%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 6 (6.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking occupancy(values: 10, occupancy: 1, null_percent: 0.9) | |
| Benchmarking occupancy(values: 10, occupancy: 1, null_percent: 0.9): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 10, occupancy: 1, null_percent: 0.9): Collecting 100 samples in estimated 5.0006 s (19M iterations) | |
| Benchmarking occupancy(values: 10, occupancy: 1, null_percent: 0.9): Analyzing | |
| occupancy(values: 10, occupancy: 1, null_percent: 0.9) | |
| time: [272.11 ns 272.54 ns 272.99 ns] | |
| change: [+12.124% +12.361% +12.583%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 6 (6.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking occupancy(values: 10, occupancy: 0.5, null_percent: 0) | |
| Benchmarking occupancy(values: 10, occupancy: 0.5, null_percent: 0): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 10, occupancy: 0.5, null_percent: 0): Collecting 100 samples in estimated 5.0074 s (2.4M iterations) | |
| Benchmarking occupancy(values: 10, occupancy: 0.5, null_percent: 0): Analyzing | |
| occupancy(values: 10, occupancy: 0.5, null_percent: 0) | |
| time: [2.0530 µs 2.0534 µs 2.0538 µs] | |
| change: [+0.1688% +0.1999% +0.2304%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 2 (2.00%) low mild | |
| 3 (3.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking occupancy(values: 10, occupancy: 0.5, null_percent: 0.1) | |
| Benchmarking occupancy(values: 10, occupancy: 0.5, null_percent: 0.1): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 10, occupancy: 0.5, null_percent: 0.1): Collecting 100 samples in estimated 5.0088 s (2.6M iterations) | |
| Benchmarking occupancy(values: 10, occupancy: 0.5, null_percent: 0.1): Analyzing | |
| occupancy(values: 10, occupancy: 0.5, null_percent: 0.1) | |
| time: [1.9259 µs 1.9266 µs 1.9275 µs] | |
| change: [+6.0826% +6.1879% +6.2866%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 1 (1.00%) low mild | |
| 3 (3.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking occupancy(values: 10, occupancy: 0.5, null_percent: 0.5) | |
| Benchmarking occupancy(values: 10, occupancy: 0.5, null_percent: 0.5): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 10, occupancy: 0.5, null_percent: 0.5): Collecting 100 samples in estimated 5.0018 s (4.4M iterations) | |
| Benchmarking occupancy(values: 10, occupancy: 0.5, null_percent: 0.5): Analyzing | |
| occupancy(values: 10, occupancy: 0.5, null_percent: 0.5) | |
| time: [1.1317 µs 1.1320 µs 1.1323 µs] | |
| change: [+2.5622% +3.3870% +3.8753%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 3 (3.00%) high mild | |
| Benchmarking occupancy(values: 10, occupancy: 0.5, null_percent: 0.9) | |
| Benchmarking occupancy(values: 10, occupancy: 0.5, null_percent: 0.9): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 10, occupancy: 0.5, null_percent: 0.9): Collecting 100 samples in estimated 5.0011 s (18M iterations) | |
| Benchmarking occupancy(values: 10, occupancy: 0.5, null_percent: 0.9): Analyzing | |
| occupancy(values: 10, occupancy: 0.5, null_percent: 0.9) | |
| time: [284.02 ns 284.16 ns 284.31 ns] | |
| change: [-2.1788% -1.9052% -1.6893%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) high mild | |
| Benchmarking occupancy(values: 10, occupancy: 0.1, null_percent: 0) | |
| Benchmarking occupancy(values: 10, occupancy: 0.1, null_percent: 0): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 10, occupancy: 0.1, null_percent: 0): Collecting 100 samples in estimated 5.0000 s (2.4M iterations) | |
| Benchmarking occupancy(values: 10, occupancy: 0.1, null_percent: 0): Analyzing | |
| occupancy(values: 10, occupancy: 0.1, null_percent: 0) | |
| time: [2.0539 µs 2.0543 µs 2.0547 µs] | |
| change: [+0.3780% +0.4095% +0.4395%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Benchmarking occupancy(values: 10, occupancy: 0.1, null_percent: 0.1) | |
| Benchmarking occupancy(values: 10, occupancy: 0.1, null_percent: 0.1): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 10, occupancy: 0.1, null_percent: 0.1): Collecting 100 samples in estimated 5.0076 s (2.6M iterations) | |
| Benchmarking occupancy(values: 10, occupancy: 0.1, null_percent: 0.1): Analyzing | |
| occupancy(values: 10, occupancy: 0.1, null_percent: 0.1) | |
| time: [1.8895 µs 1.8900 µs 1.8905 µs] | |
| change: [+3.6832% +3.7994% +3.9008%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 3 (3.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking occupancy(values: 10, occupancy: 0.1, null_percent: 0.5) | |
| Benchmarking occupancy(values: 10, occupancy: 0.1, null_percent: 0.5): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 10, occupancy: 0.1, null_percent: 0.5): Collecting 100 samples in estimated 5.0005 s (4.2M iterations) | |
| Benchmarking occupancy(values: 10, occupancy: 0.1, null_percent: 0.5): Analyzing | |
| occupancy(values: 10, occupancy: 0.1, null_percent: 0.5) | |
| time: [1.1795 µs 1.1858 µs 1.1927 µs] | |
| change: [+5.7102% +6.0408% +6.3492%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 19 outliers among 100 measurements (19.00%) | |
| 14 (14.00%) low severe | |
| 3 (3.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking occupancy(values: 10, occupancy: 0.1, null_percent: 0.9) | |
| Benchmarking occupancy(values: 10, occupancy: 0.1, null_percent: 0.9): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 10, occupancy: 0.1, null_percent: 0.9): Collecting 100 samples in estimated 5.0006 s (14M iterations) | |
| Benchmarking occupancy(values: 10, occupancy: 0.1, null_percent: 0.9): Analyzing | |
| occupancy(values: 10, occupancy: 0.1, null_percent: 0.9) | |
| time: [346.75 ns 346.87 ns 347.00 ns] | |
| change: [+19.567% +19.735% +19.880%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 1 (1.00%) low mild | |
| 2 (2.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking occupancy(values: 100, occupancy: 1, null_percent: 0) | |
| Benchmarking occupancy(values: 100, occupancy: 1, null_percent: 0): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 100, occupancy: 1, null_percent: 0): Collecting 100 samples in estimated 5.0036 s (5.7M iterations) | |
| Benchmarking occupancy(values: 100, occupancy: 1, null_percent: 0): Analyzing | |
| occupancy(values: 100, occupancy: 1, null_percent: 0) | |
| time: [883.73 ns 883.96 ns 884.22 ns] | |
| change: [+2.1787% +2.7274% +3.0259%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 5 (5.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking occupancy(values: 100, occupancy: 1, null_percent: 0.1) | |
| Benchmarking occupancy(values: 100, occupancy: 1, null_percent: 0.1): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 100, occupancy: 1, null_percent: 0.1): Collecting 100 samples in estimated 5.0043 s (2.8M iterations) | |
| Benchmarking occupancy(values: 100, occupancy: 1, null_percent: 0.1): Analyzing | |
| occupancy(values: 100, occupancy: 1, null_percent: 0.1) | |
| time: [1.7706 µs 1.7713 µs 1.7721 µs] | |
| change: [-0.5692% -0.3698% -0.1986%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 5 (5.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking occupancy(values: 100, occupancy: 1, null_percent: 0.5) | |
| Benchmarking occupancy(values: 100, occupancy: 1, null_percent: 0.5): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 100, occupancy: 1, null_percent: 0.5): Collecting 100 samples in estimated 5.0012 s (4.7M iterations) | |
| Benchmarking occupancy(values: 100, occupancy: 1, null_percent: 0.5): Analyzing | |
| occupancy(values: 100, occupancy: 1, null_percent: 0.5) | |
| time: [1.0613 µs 1.0618 µs 1.0622 µs] | |
| change: [+3.3437% +3.5337% +3.7846%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 2 (2.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking occupancy(values: 100, occupancy: 1, null_percent: 0.9) | |
| Benchmarking occupancy(values: 100, occupancy: 1, null_percent: 0.9): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 100, occupancy: 1, null_percent: 0.9): Collecting 100 samples in estimated 5.0009 s (18M iterations) | |
| Benchmarking occupancy(values: 100, occupancy: 1, null_percent: 0.9): Analyzing | |
| occupancy(values: 100, occupancy: 1, null_percent: 0.9) | |
| time: [275.91 ns 275.99 ns 276.07 ns] | |
| change: [+10.146% +10.212% +10.273%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 2 (2.00%) low mild | |
| 1 (1.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking occupancy(values: 100, occupancy: 0.5, null_percent: 0) | |
| Benchmarking occupancy(values: 100, occupancy: 0.5, null_percent: 0): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 100, occupancy: 0.5, null_percent: 0): Collecting 100 samples in estimated 5.0006 s (5.6M iterations) | |
| Benchmarking occupancy(values: 100, occupancy: 0.5, null_percent: 0): Analyzing | |
| occupancy(values: 100, occupancy: 0.5, null_percent: 0) | |
| time: [893.20 ns 895.86 ns 901.39 ns] | |
| change: [+6.6018% +6.7829% +7.0595%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 1 (1.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking occupancy(values: 100, occupancy: 0.5, null_percent: 0.1) | |
| Benchmarking occupancy(values: 100, occupancy: 0.5, null_percent: 0.1): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 100, occupancy: 0.5, null_percent: 0.1): Collecting 100 samples in estimated 5.0011 s (2.6M iterations) | |
| Benchmarking occupancy(values: 100, occupancy: 0.5, null_percent: 0.1): Analyzing | |
| occupancy(values: 100, occupancy: 0.5, null_percent: 0.1) | |
| time: [1.8809 µs 1.8875 µs 1.8970 µs] | |
| change: [+5.0719% +5.6095% +6.2424%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 2 (2.00%) low severe | |
| 2 (2.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking occupancy(values: 100, occupancy: 0.5, null_percent: 0.5) | |
| Benchmarking occupancy(values: 100, occupancy: 0.5, null_percent: 0.5): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 100, occupancy: 0.5, null_percent: 0.5): Collecting 100 samples in estimated 5.0033 s (4.4M iterations) | |
| Benchmarking occupancy(values: 100, occupancy: 0.5, null_percent: 0.5): Analyzing | |
| occupancy(values: 100, occupancy: 0.5, null_percent: 0.5) | |
| time: [1.1255 µs 1.1278 µs 1.1324 µs] | |
| change: [+6.5321% +6.7974% +7.2042%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 4 (4.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking occupancy(values: 100, occupancy: 0.5, null_percent: 0.9) | |
| Benchmarking occupancy(values: 100, occupancy: 0.5, null_percent: 0.9): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 100, occupancy: 0.5, null_percent: 0.9): Collecting 100 samples in estimated 5.0006 s (22M iterations) | |
| Benchmarking occupancy(values: 100, occupancy: 0.5, null_percent: 0.9): Analyzing | |
| occupancy(values: 100, occupancy: 0.5, null_percent: 0.9) | |
| time: [234.97 ns 235.92 ns 236.98 ns] | |
| change: [+1.5435% +1.9006% +2.2684%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Benchmarking occupancy(values: 100, occupancy: 0.1, null_percent: 0) | |
| Benchmarking occupancy(values: 100, occupancy: 0.1, null_percent: 0): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 100, occupancy: 0.1, null_percent: 0): Collecting 100 samples in estimated 5.0033 s (3.1M iterations) | |
| Benchmarking occupancy(values: 100, occupancy: 0.1, null_percent: 0): Analyzing | |
| occupancy(values: 100, occupancy: 0.1, null_percent: 0) | |
| time: [1.6033 µs 1.6036 µs 1.6040 µs] | |
| change: [+1.2823% +1.3183% +1.3524%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 1 (1.00%) low mild | |
| 4 (4.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking occupancy(values: 100, occupancy: 0.1, null_percent: 0.1) | |
| Benchmarking occupancy(values: 100, occupancy: 0.1, null_percent: 0.1): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 100, occupancy: 0.1, null_percent: 0.1): Collecting 100 samples in estimated 5.0051 s (2.6M iterations) | |
| Benchmarking occupancy(values: 100, occupancy: 0.1, null_percent: 0.1): Analyzing | |
| occupancy(values: 100, occupancy: 0.1, null_percent: 0.1) | |
| time: [1.9353 µs 1.9365 µs 1.9378 µs] | |
| change: [+2.3528% +2.5238% +2.6752%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high mild | |
| Benchmarking occupancy(values: 100, occupancy: 0.1, null_percent: 0.5) | |
| Benchmarking occupancy(values: 100, occupancy: 0.1, null_percent: 0.5): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 100, occupancy: 0.1, null_percent: 0.5): Collecting 100 samples in estimated 5.0034 s (4.5M iterations) | |
| Benchmarking occupancy(values: 100, occupancy: 0.1, null_percent: 0.5): Analyzing | |
| occupancy(values: 100, occupancy: 0.1, null_percent: 0.5) | |
| time: [1.1262 µs 1.1271 µs 1.1281 µs] | |
| change: [+3.8558% +3.9847% +4.1073%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 3 (3.00%) high mild | |
| Benchmarking occupancy(values: 100, occupancy: 0.1, null_percent: 0.9) | |
| Benchmarking occupancy(values: 100, occupancy: 0.1, null_percent: 0.9): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 100, occupancy: 0.1, null_percent: 0.9): Collecting 100 samples in estimated 5.0005 s (20M iterations) | |
| Benchmarking occupancy(values: 100, occupancy: 0.1, null_percent: 0.9): Analyzing | |
| occupancy(values: 100, occupancy: 0.1, null_percent: 0.9) | |
| time: [257.50 ns 258.02 ns 258.63 ns] | |
| change: [+11.608% +11.743% +11.897%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 2 (2.00%) low mild | |
| 6 (6.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking occupancy(values: 512, occupancy: 1, null_percent: 0) | |
| Benchmarking occupancy(values: 512, occupancy: 1, null_percent: 0): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 512, occupancy: 1, null_percent: 0): Collecting 100 samples in estimated 5.0021 s (6.0M iterations) | |
| Benchmarking occupancy(values: 512, occupancy: 1, null_percent: 0): Analyzing | |
| occupancy(values: 512, occupancy: 1, null_percent: 0) | |
| time: [839.69 ns 840.24 ns 840.77 ns] | |
| change: [+1.4171% +1.5150% +1.6090%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Benchmarking occupancy(values: 512, occupancy: 1, null_percent: 0.1) | |
| Benchmarking occupancy(values: 512, occupancy: 1, null_percent: 0.1): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 512, occupancy: 1, null_percent: 0.1): Collecting 100 samples in estimated 5.0013 s (2.7M iterations) | |
| Benchmarking occupancy(values: 512, occupancy: 1, null_percent: 0.1): Analyzing | |
| occupancy(values: 512, occupancy: 1, null_percent: 0.1) | |
| time: [1.8835 µs 1.8840 µs 1.8846 µs] | |
| change: [+10.626% +10.827% +10.979%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 2 (2.00%) low mild | |
| 4 (4.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking occupancy(values: 512, occupancy: 1, null_percent: 0.5) | |
| Benchmarking occupancy(values: 512, occupancy: 1, null_percent: 0.5): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 512, occupancy: 1, null_percent: 0.5): Collecting 100 samples in estimated 5.0010 s (4.5M iterations) | |
| Benchmarking occupancy(values: 512, occupancy: 1, null_percent: 0.5): Analyzing | |
| occupancy(values: 512, occupancy: 1, null_percent: 0.5) | |
| time: [1.1127 µs 1.1131 µs 1.1135 µs] | |
| change: [+10.376% +10.490% +10.594%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 1 (1.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking occupancy(values: 512, occupancy: 1, null_percent: 0.9) | |
| Benchmarking occupancy(values: 512, occupancy: 1, null_percent: 0.9): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 512, occupancy: 1, null_percent: 0.9): Collecting 100 samples in estimated 5.0009 s (18M iterations) | |
| Benchmarking occupancy(values: 512, occupancy: 1, null_percent: 0.9): Analyzing | |
| occupancy(values: 512, occupancy: 1, null_percent: 0.9) | |
| time: [265.45 ns 267.98 ns 271.02 ns] | |
| change: [+9.0227% +10.343% +12.556%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 23 outliers among 100 measurements (23.00%) | |
| 16 (16.00%) low severe | |
| 1 (1.00%) low mild | |
| 1 (1.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking occupancy(values: 512, occupancy: 0.5, null_percent: 0) | |
| Benchmarking occupancy(values: 512, occupancy: 0.5, null_percent: 0): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 512, occupancy: 0.5, null_percent: 0): Collecting 100 samples in estimated 5.0003 s (6.2M iterations) | |
| Benchmarking occupancy(values: 512, occupancy: 0.5, null_percent: 0): Analyzing | |
| occupancy(values: 512, occupancy: 0.5, null_percent: 0) | |
| time: [805.60 ns 807.19 ns 809.69 ns] | |
| change: [+1.4154% +1.6488% +1.9950%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 2 (2.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking occupancy(values: 512, occupancy: 0.5, null_percent: 0.1) | |
| Benchmarking occupancy(values: 512, occupancy: 0.5, null_percent: 0.1): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 512, occupancy: 0.5, null_percent: 0.1): Collecting 100 samples in estimated 5.0094 s (2.6M iterations) | |
| Benchmarking occupancy(values: 512, occupancy: 0.5, null_percent: 0.1): Analyzing | |
| occupancy(values: 512, occupancy: 0.5, null_percent: 0.1) | |
| time: [1.9115 µs 1.9212 µs 1.9357 µs] | |
| change: [+12.575% +12.907% +13.307%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 1 (1.00%) low mild | |
| 6 (6.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking occupancy(values: 512, occupancy: 0.5, null_percent: 0.5) | |
| Benchmarking occupancy(values: 512, occupancy: 0.5, null_percent: 0.5): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 512, occupancy: 0.5, null_percent: 0.5): Collecting 100 samples in estimated 5.0045 s (4.8M iterations) | |
| Benchmarking occupancy(values: 512, occupancy: 0.5, null_percent: 0.5): Analyzing | |
| occupancy(values: 512, occupancy: 0.5, null_percent: 0.5) | |
| time: [1.0279 µs 1.0290 µs 1.0299 µs] | |
| change: [+4.0384% +4.1731% +4.2919%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) low severe | |
| Benchmarking occupancy(values: 512, occupancy: 0.5, null_percent: 0.9) | |
| Benchmarking occupancy(values: 512, occupancy: 0.5, null_percent: 0.9): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 512, occupancy: 0.5, null_percent: 0.9): Collecting 100 samples in estimated 5.0002 s (20M iterations) | |
| Benchmarking occupancy(values: 512, occupancy: 0.5, null_percent: 0.9): Analyzing | |
| occupancy(values: 512, occupancy: 0.5, null_percent: 0.9) | |
| time: [255.22 ns 256.45 ns 258.31 ns] | |
| change: [+4.6914% +4.9556% +5.3015%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 1 (1.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking occupancy(values: 512, occupancy: 0.1, null_percent: 0) | |
| Benchmarking occupancy(values: 512, occupancy: 0.1, null_percent: 0): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 512, occupancy: 0.1, null_percent: 0): Collecting 100 samples in estimated 5.0001 s (5.6M iterations) | |
| Benchmarking occupancy(values: 512, occupancy: 0.1, null_percent: 0): Analyzing | |
| occupancy(values: 512, occupancy: 0.1, null_percent: 0) | |
| time: [892.18 ns 896.26 ns 902.48 ns] | |
| change: [+8.2490% +8.5800% +8.9880%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 1 (1.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking occupancy(values: 512, occupancy: 0.1, null_percent: 0.1) | |
| Benchmarking occupancy(values: 512, occupancy: 0.1, null_percent: 0.1): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 512, occupancy: 0.1, null_percent: 0.1): Collecting 100 samples in estimated 5.0064 s (2.6M iterations) | |
| Benchmarking occupancy(values: 512, occupancy: 0.1, null_percent: 0.1): Analyzing | |
| occupancy(values: 512, occupancy: 0.1, null_percent: 0.1) | |
| time: [1.9157 µs 1.9203 µs 1.9296 µs] | |
| change: [+7.4560% +7.7168% +8.0509%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 3 (3.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking occupancy(values: 512, occupancy: 0.1, null_percent: 0.5) | |
| Benchmarking occupancy(values: 512, occupancy: 0.1, null_percent: 0.5): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 512, occupancy: 0.1, null_percent: 0.5): Collecting 100 samples in estimated 5.0037 s (4.9M iterations) | |
| Benchmarking occupancy(values: 512, occupancy: 0.1, null_percent: 0.5): Analyzing | |
| occupancy(values: 512, occupancy: 0.1, null_percent: 0.5) | |
| time: [1.0492 µs 1.0499 µs 1.0507 µs] | |
| change: [-2.3358% -2.0147% -1.7335%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) high mild | |
| Benchmarking occupancy(values: 512, occupancy: 0.1, null_percent: 0.9) | |
| Benchmarking occupancy(values: 512, occupancy: 0.1, null_percent: 0.9): Warming up for 3.0000 s | |
| Benchmarking occupancy(values: 512, occupancy: 0.1, null_percent: 0.9): Collecting 100 samples in estimated 5.0002 s (18M iterations) | |
| Benchmarking occupancy(values: 512, occupancy: 0.1, null_percent: 0.9): Analyzing | |
| occupancy(values: 512, occupancy: 0.1, null_percent: 0.9) | |
| time: [292.15 ns 292.98 ns 293.62 ns] | |
| change: [+26.768% +27.282% +27.790%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Running benches/bit_mask.rs (target/x86_64-unknown-linux-gnu/release/deps/bit_mask-deb615f31eca6987) | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_0_len_1_datum_0 | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_0_len_1_datum_0: Warming up for 3.0000 s | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_0_len_1_datum_0: Collecting 100 samples in estimated 5.0000 s (921M iterations) | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_0_len_1_datum_0: Analyzing | |
| bit_mask/set_bits/offset_write_0_offset_read_0_len_1_datum_0 | |
| time: [5.4304 ns 5.4312 ns 5.4321 ns] | |
| change: [-9.1169% -9.1004% -9.0818%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 3 (3.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_0_len_1_datum_173 | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_0_len_1_datum_173: Warming up for 3.0000 s | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_0_len_1_datum_173: Collecting 100 samples in estimated 5.0000 s (920M iterations) | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_0_len_1_datum_173: Analyzing | |
| bit_mask/set_bits/offset_write_0_offset_read_0_len_1_datum_173 | |
| time: [5.4301 ns 5.4307 ns 5.4314 ns] | |
| change: [-9.1086% -9.0892% -9.0644%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 3 (3.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_0_len_17_datum_0 | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_0_len_17_datum_0: Warming up for 3.0000 s | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_0_len_17_datum_0: Collecting 100 samples in estimated 5.0000 s (319M iterations) | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_0_len_17_datum_0: Analyzing | |
| bit_mask/set_bits/offset_write_0_offset_read_0_len_17_datum_0 | |
| time: [14.567 ns 14.578 ns 14.591 ns] | |
| change: [-17.809% -17.617% -17.319%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 3 (3.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_0_len_17_datum_173 | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_0_len_17_datum_173: Warming up for 3.0000 s | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_0_len_17_datum_173: Collecting 100 samples in estimated 5.0001 s (320M iterations) | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_0_len_17_datum_173: Analyzing | |
| bit_mask/set_bits/offset_write_0_offset_read_0_len_17_datum_173 | |
| time: [14.569 ns 14.573 ns 14.577 ns] | |
| change: [-18.110% -17.923% -17.774%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 2 (2.00%) low mild | |
| 2 (2.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_0_len_65_datum_0 | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_0_len_65_datum_0: Warming up for 3.0000 s | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_0_len_65_datum_0: Collecting 100 samples in estimated 5.0000 s (658M iterations) | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_0_len_65_datum_0: Analyzing | |
| bit_mask/set_bits/offset_write_0_offset_read_0_len_65_datum_0 | |
| time: [7.6020 ns 7.6029 ns 7.6039 ns] | |
| change: [-7.4222% -7.2714% -7.1314%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 4 (4.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_0_len_65_datum_173 | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_0_len_65_datum_173: Warming up for 3.0000 s | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_0_len_65_datum_173: Collecting 100 samples in estimated 5.0000 s (658M iterations) | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_0_len_65_datum_173: Analyzing | |
| bit_mask/set_bits/offset_write_0_offset_read_0_len_65_datum_173 | |
| time: [7.6020 ns 7.6029 ns 7.6039 ns] | |
| change: [-8.7752% -7.9562% -7.3138%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 4 (4.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_5_len_1_datum_0 | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_5_len_1_datum_0: Warming up for 3.0000 s | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_5_len_1_datum_0: Collecting 100 samples in estimated 5.0000 s (921M iterations) | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_5_len_1_datum_0: Analyzing | |
| bit_mask/set_bits/offset_write_0_offset_read_5_len_1_datum_0 | |
| time: [5.4305 ns 5.4312 ns 5.4320 ns] | |
| change: [-9.8910% -9.3955% -9.0904%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 5 (5.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_5_len_1_datum_173 | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_5_len_1_datum_173: Warming up for 3.0000 s | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_5_len_1_datum_173: Collecting 100 samples in estimated 5.0000 s (921M iterations) | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_5_len_1_datum_173: Analyzing | |
| bit_mask/set_bits/offset_write_0_offset_read_5_len_1_datum_173 | |
| time: [5.4306 ns 5.4315 ns 5.4326 ns] | |
| change: [-9.8378% -9.4007% -9.0935%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 4 (4.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_5_len_17_datum_0 | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_5_len_17_datum_0: Warming up for 3.0000 s | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_5_len_17_datum_0: Collecting 100 samples in estimated 5.0001 s (319M iterations) | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_5_len_17_datum_0: Analyzing | |
| bit_mask/set_bits/offset_write_0_offset_read_5_len_17_datum_0 | |
| time: [14.569 ns 14.592 ns 14.638 ns] | |
| change: [-17.831% -17.741% -17.603%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 1 (1.00%) low severe | |
| 1 (1.00%) high severe | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_5_len_17_datum_173 | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_5_len_17_datum_173: Warming up for 3.0000 s | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_5_len_17_datum_173: Collecting 100 samples in estimated 5.0001 s (319M iterations) | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_5_len_17_datum_173: Analyzing | |
| bit_mask/set_bits/offset_write_0_offset_read_5_len_17_datum_173 | |
| time: [14.566 ns 14.571 ns 14.577 ns] | |
| change: [-17.772% -17.733% -17.695%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 4 (4.00%) low mild | |
| 1 (1.00%) high mild | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_5_len_65_datum_0 | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_5_len_65_datum_0: Warming up for 3.0000 s | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_5_len_65_datum_0: Collecting 100 samples in estimated 5.0001 s (274M iterations) | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_5_len_65_datum_0: Analyzing | |
| bit_mask/set_bits/offset_write_0_offset_read_5_len_65_datum_0 | |
| time: [18.260 ns 18.262 ns 18.265 ns] | |
| change: [+0.1581% +0.3849% +0.5854%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 1 (1.00%) low mild | |
| 1 (1.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_5_len_65_datum_173 | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_5_len_65_datum_173: Warming up for 3.0000 s | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_5_len_65_datum_173: Collecting 100 samples in estimated 5.0000 s (274M iterations) | |
| Benchmarking bit_mask/set_bits/offset_write_0_offset_read_5_len_65_datum_173: Analyzing | |
| bit_mask/set_bits/offset_write_0_offset_read_5_len_65_datum_173 | |
| time: [18.263 ns 18.265 ns 18.268 ns] | |
| change: [-0.0297% +0.2415% +0.4840%] (p = 0.06 > 0.05) | |
| No change in performance detected. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 1 (1.00%) low mild | |
| 6 (6.00%) high mild | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_0_len_1_datum_0 | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_0_len_1_datum_0: Warming up for 3.0000 s | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_0_len_1_datum_0: Collecting 100 samples in estimated 5.0000 s (921M iterations) | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_0_len_1_datum_0: Analyzing | |
| bit_mask/set_bits/offset_write_5_offset_read_0_len_1_datum_0 | |
| time: [5.4306 ns 5.4319 ns 5.4339 ns] | |
| change: [-9.5451% -8.7404% -7.5247%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 5 (5.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_0_len_1_datum_173 | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_0_len_1_datum_173: Warming up for 3.0000 s | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_0_len_1_datum_173: Collecting 100 samples in estimated 5.0000 s (921M iterations) | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_0_len_1_datum_173: Analyzing | |
| bit_mask/set_bits/offset_write_5_offset_read_0_len_1_datum_173 | |
| time: [5.4298 ns 5.4305 ns 5.4313 ns] | |
| change: [-9.3146% -9.1625% -9.0761%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 4 (4.00%) high mild | |
| 7 (7.00%) high severe | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_0_len_17_datum_0 | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_0_len_17_datum_0: Warming up for 3.0000 s | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_0_len_17_datum_0: Collecting 100 samples in estimated 5.0000 s (319M iterations) | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_0_len_17_datum_0: Analyzing | |
| bit_mask/set_bits/offset_write_5_offset_read_0_len_17_datum_0 | |
| time: [14.569 ns 14.575 ns 14.580 ns] | |
| change: [-17.841% -17.803% -17.764%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 1 (1.00%) low mild | |
| 1 (1.00%) high mild | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_0_len_17_datum_173 | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_0_len_17_datum_173: Warming up for 3.0000 s | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_0_len_17_datum_173: Collecting 100 samples in estimated 5.0001 s (319M iterations) | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_0_len_17_datum_173: Analyzing | |
| bit_mask/set_bits/offset_write_5_offset_read_0_len_17_datum_173 | |
| time: [14.561 ns 14.566 ns 14.571 ns] | |
| change: [-17.844% -17.796% -17.748%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 3 (3.00%) low mild | |
| 1 (1.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_0_len_65_datum_0 | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_0_len_65_datum_0: Warming up for 3.0000 s | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_0_len_65_datum_0: Collecting 100 samples in estimated 5.0000 s (276M iterations) | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_0_len_65_datum_0: Analyzing | |
| bit_mask/set_bits/offset_write_5_offset_read_0_len_65_datum_0 | |
| time: [18.094 ns 18.097 ns 18.100 ns] | |
| change: [-2.0792% -2.0311% -1.9809%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 1 (1.00%) low severe | |
| 1 (1.00%) low mild | |
| 2 (2.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_0_len_65_datum_173 | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_0_len_65_datum_173: Warming up for 3.0000 s | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_0_len_65_datum_173: Collecting 100 samples in estimated 5.0001 s (276M iterations) | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_0_len_65_datum_173: Analyzing | |
| bit_mask/set_bits/offset_write_5_offset_read_0_len_65_datum_173 | |
| time: [18.096 ns 18.099 ns 18.102 ns] | |
| change: [-2.1208% -2.0816% -2.0410%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 1 (1.00%) low mild | |
| 4 (4.00%) high mild | |
| 7 (7.00%) high severe | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_5_len_1_datum_0 | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_5_len_1_datum_0: Warming up for 3.0000 s | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_5_len_1_datum_0: Collecting 100 samples in estimated 5.0000 s (921M iterations) | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_5_len_1_datum_0: Analyzing | |
| bit_mask/set_bits/offset_write_5_offset_read_5_len_1_datum_0 | |
| time: [5.4305 ns 5.4310 ns 5.4317 ns] | |
| change: [-9.1188% -9.0972% -9.0794%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 6 (6.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_5_len_1_datum_173 | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_5_len_1_datum_173: Warming up for 3.0000 s | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_5_len_1_datum_173: Collecting 100 samples in estimated 5.0000 s (920M iterations) | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_5_len_1_datum_173: Analyzing | |
| bit_mask/set_bits/offset_write_5_offset_read_5_len_1_datum_173 | |
| time: [5.4307 ns 5.4313 ns 5.4321 ns] | |
| change: [-9.1977% -9.0886% -8.9558%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 2 (2.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_5_len_17_datum_0 | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_5_len_17_datum_0: Warming up for 3.0000 s | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_5_len_17_datum_0: Collecting 100 samples in estimated 5.0000 s (320M iterations) | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_5_len_17_datum_0: Analyzing | |
| bit_mask/set_bits/offset_write_5_offset_read_5_len_17_datum_0 | |
| time: [14.551 ns 14.557 ns 14.563 ns] | |
| change: [-17.897% -17.855% -17.812%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_5_len_17_datum_173 | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_5_len_17_datum_173: Warming up for 3.0000 s | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_5_len_17_datum_173: Collecting 100 samples in estimated 5.0001 s (320M iterations) | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_5_len_17_datum_173: Analyzing | |
| bit_mask/set_bits/offset_write_5_offset_read_5_len_17_datum_173 | |
| time: [14.554 ns 14.561 ns 14.568 ns] | |
| change: [-18.208% -17.964% -17.797%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 2 (2.00%) low mild | |
| 1 (1.00%) high mild | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_5_len_65_datum_0 | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_5_len_65_datum_0: Warming up for 3.0000 s | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_5_len_65_datum_0: Collecting 100 samples in estimated 5.0001 s (335M iterations) | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_5_len_65_datum_0: Analyzing | |
| bit_mask/set_bits/offset_write_5_offset_read_5_len_65_datum_0 | |
| time: [14.934 ns 14.936 ns 14.938 ns] | |
| change: [-18.103% -17.960% -17.878%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 2 (2.00%) high mild | |
| 7 (7.00%) high severe | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_5_len_65_datum_173 | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_5_len_65_datum_173: Warming up for 3.0000 s | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_5_len_65_datum_173: Collecting 100 samples in estimated 5.0000 s (335M iterations) | |
| Benchmarking bit_mask/set_bits/offset_write_5_offset_read_5_len_65_datum_173: Analyzing | |
| bit_mask/set_bits/offset_write_5_offset_read_5_len_65_datum_173 | |
| time: [14.934 ns 14.936 ns 14.938 ns] | |
| change: [-17.935% -17.913% -17.895%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 3 (3.00%) high mild | |
| 7 (7.00%) high severe | |
| Running benches/i256.rs (target/x86_64-unknown-linux-gnu/release/deps/i256-03bd191f05eb4d69) | |
| Benchmarking i256_parse(0) | |
| Benchmarking i256_parse(0): Warming up for 3.0000 s | |
| Benchmarking i256_parse(0): Collecting 100 samples in estimated 5.0000 s (438M iterations) | |
| Benchmarking i256_parse(0): Analyzing | |
| i256_parse(0) time: [11.416 ns 11.418 ns 11.420 ns] | |
| change: [-1.6687% -1.1852% -0.7650%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 4 (4.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking i256_parse(1) | |
| Benchmarking i256_parse(1): Warming up for 3.0000 s | |
| Benchmarking i256_parse(1): Collecting 100 samples in estimated 5.0000 s (438M iterations) | |
| Benchmarking i256_parse(1): Analyzing | |
| i256_parse(1) time: [11.416 ns 11.417 ns 11.419 ns] | |
| change: [-0.0012% +0.0218% +0.0497%] (p = 0.09 > 0.05) | |
| No change in performance detected. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 7 (7.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking i256_parse(-1) | |
| Benchmarking i256_parse(-1): Warming up for 3.0000 s | |
| Benchmarking i256_parse(-1): Collecting 100 samples in estimated 5.0001 s (438M iterations) | |
| Benchmarking i256_parse(-1): Analyzing | |
| i256_parse(-1) time: [11.414 ns 11.416 ns 11.417 ns] | |
| change: [-0.0561% -0.0245% +0.0021%] (p = 0.09 > 0.05) | |
| No change in performance detected. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 3 (3.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking i256_parse(1233456789) | |
| Benchmarking i256_parse(1233456789): Warming up for 3.0000 s | |
| Benchmarking i256_parse(1233456789): Collecting 100 samples in estimated 5.0000 s (322M iterations) | |
| Benchmarking i256_parse(1233456789): Analyzing | |
| i256_parse(1233456789) time: [15.492 ns 15.493 ns 15.495 ns] | |
| change: [-0.0182% +0.0009% +0.0201%] (p = 0.93 > 0.05) | |
| No change in performance detected. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 4 (4.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking i256_parse(-1233456789) | |
| Benchmarking i256_parse(-1233456789): Warming up for 3.0000 s | |
| Benchmarking i256_parse(-1233456789): Collecting 100 samples in estimated 5.0000 s (306M iterations) | |
| Benchmarking i256_parse(-1233456789): Analyzing | |
| i256_parse(-1233456789) time: [16.310 ns 16.312 ns 16.314 ns] | |
| change: [-0.7617% -0.4425% -0.2193%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 2 (2.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking i256_parse(170141183460469231731687303715884105727) | |
| Benchmarking i256_parse(170141183460469231731687303715884105727): Warming up for 3.0000 s | |
| Benchmarking i256_parse(170141183460469231731687303715884105727): Collecting 100 samples in estimated 5.0003 s (49M iterations) | |
| Benchmarking i256_parse(170141183460469231731687303715884105727): Analyzing | |
| i256_parse(170141183460469231731687303715884105727) | |
| time: [101.12 ns 101.77 ns 102.57 ns] | |
| change: [-1.5929% -1.0116% -0.2293%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 1 (1.00%) low mild | |
| 1 (1.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking i256_parse(-170141183460469231731687303715884105728) | |
| Benchmarking i256_parse(-170141183460469231731687303715884105728): Warming up for 3.0000 s | |
| Benchmarking i256_parse(-170141183460469231731687303715884105728): Collecting 100 samples in estimated 5.0001 s (50M iterations) | |
| Benchmarking i256_parse(-170141183460469231731687303715884105728): Analyzing | |
| i256_parse(-170141183460469231731687303715884105728) | |
| time: [100.70 ns 101.16 ns 101.77 ns] | |
| change: [-2.1026% -1.6291% -0.9999%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 4 (4.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking i256_parse(-57896044618658097711785492504343953926634992332820282019728792003956564819968) | |
| Benchmarking i256_parse(-57896044618658097711785492504343953926634992332820282019728792003956564819968): Warming up for 3.0000 s | |
| Benchmarking i256_parse(-57896044618658097711785492504343953926634992332820282019728792003956564819968): Collecting 100 samples in estimated 5.0007 s (25M iterations) | |
| Benchmarking i256_parse(-57896044618658097711785492504343953926634992332820282019728792003956564819968): Analyzing | |
| i256_parse(-57896044618658097711785492504343953926634992332820282019728792003956564819968) | |
| time: [196.93 ns 197.43 ns 198.35 ns] | |
| change: [-1.9106% -1.4986% -1.1092%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 2 (2.00%) low severe | |
| 2 (2.00%) low mild | |
| 1 (1.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking i256_parse(57896044618658097711785492504343953926634992332820282019728792003956564819967) | |
| Benchmarking i256_parse(57896044618658097711785492504343953926634992332820282019728792003956564819967): Warming up for 3.0000 s | |
| Benchmarking i256_parse(57896044618658097711785492504343953926634992332820282019728792003956564819967): Collecting 100 samples in estimated 5.0003 s (25M iterations) | |
| Benchmarking i256_parse(57896044618658097711785492504343953926634992332820282019728792003956564819967): Analyzing | |
| i256_parse(57896044618658097711785492504343953926634992332820282019728792003956564819967) | |
| time: [196.77 ns 197.81 ns 199.18 ns] | |
| change: [-1.0524% -0.6455% -0.1088%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 2 (2.00%) low mild | |
| 3 (3.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking i256_div_rem small quotient | |
| Benchmarking i256_div_rem small quotient: Warming up for 3.0000 s | |
| Benchmarking i256_div_rem small quotient: Collecting 100 samples in estimated 5.1157 s (167k iterations) | |
| Benchmarking i256_div_rem small quotient: Analyzing | |
| i256_div_rem small quotient | |
| time: [30.687 µs 30.742 µs 30.848 µs] | |
| change: [+10.478% +11.079% +11.777%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 13 outliers among 100 measurements (13.00%) | |
| 3 (3.00%) low severe | |
| 5 (5.00%) low mild | |
| 2 (2.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking i256_div_rem small divisor | |
| Benchmarking i256_div_rem small divisor: Warming up for 3.0000 s | |
| Benchmarking i256_div_rem small divisor: Collecting 100 samples in estimated 5.0494 s (333k iterations) | |
| Benchmarking i256_div_rem small divisor: Analyzing | |
| i256_div_rem small divisor | |
| time: [15.144 µs 15.149 µs 15.156 µs] | |
| change: [-9.4219% -9.4039% -9.3825%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 4 (4.00%) high mild | |
| 4 (4.00%) high severe | |
| Running benches/offset.rs (target/x86_64-unknown-linux-gnu/release/deps/offset-308e4e03a6a15364) | |
| Benchmarking OffsetBuffer::from_lengths | |
| Benchmarking OffsetBuffer::from_lengths: Warming up for 3.0000 s | |
| Benchmarking OffsetBuffer::from_lengths: Collecting 100 samples in estimated 5.0003 s (6.9M iterations) | |
| Benchmarking OffsetBuffer::from_lengths: Analyzing | |
| OffsetBuffer::from_lengths | |
| time: [724.32 ns 724.68 ns 725.04 ns] | |
| change: [+15.896% +16.095% +16.254%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) high mild | |
| Benchmarking OffsetBufferBuilder::push_length | |
| Benchmarking OffsetBufferBuilder::push_length: Warming up for 3.0000 s | |
| Benchmarking OffsetBufferBuilder::push_length: Collecting 100 samples in estimated 5.0022 s (5.1M iterations) | |
| Benchmarking OffsetBufferBuilder::push_length: Analyzing | |
| OffsetBufferBuilder::push_length | |
| time: [971.68 ns 975.31 ns 980.98 ns] | |
| change: [-2.6900% -2.3437% -1.8649%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 1 (1.00%) high mild | |
| 10 (10.00%) high severe | |
| Benchmarking OffsetBuffer::new | |
| Benchmarking OffsetBuffer::new: Warming up for 3.0000 s | |
| Benchmarking OffsetBuffer::new: Collecting 100 samples in estimated 5.0021 s (11M iterations) | |
| Benchmarking OffsetBuffer::new: Analyzing | |
| OffsetBuffer::new time: [447.25 ns 447.75 ns 448.27 ns] | |
| change: [-20.424% -20.336% -20.233%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 15 outliers among 100 measurements (15.00%) | |
| 7 (7.00%) high mild | |
| 8 (8.00%) high severe | |
| Running benches/parse_date.rs (target/x86_64-unknown-linux-gnu/release/deps/parse_date-b5269d92f1120d61) | |
| Benchmarking 2020-09-08 | |
| Benchmarking 2020-09-08: Warming up for 3.0000 s | |
| Benchmarking 2020-09-08: Collecting 100 samples in estimated 5.0000 s (293M iterations) | |
| Benchmarking 2020-09-08: Analyzing | |
| 2020-09-08 time: [16.989 ns 17.021 ns 17.057 ns] | |
| change: [-50.335% -50.240% -50.133%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 4 (4.00%) high mild | |
| 8 (8.00%) high severe | |
| Benchmarking 2020-9-8 | |
| Benchmarking 2020-9-8: Warming up for 3.0000 s | |
| Benchmarking 2020-9-8: Collecting 100 samples in estimated 5.0000 s (373M iterations) | |
| Benchmarking 2020-9-8: Analyzing | |
| 2020-9-8 time: [13.353 ns 13.436 ns 13.518 ns] | |
| change: [+3.6585% +4.3629% +5.0170%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 3 (3.00%) high mild | |
| Benchmarking 2020-09-8 | |
| Benchmarking 2020-09-8: Warming up for 3.0000 s | |
| Benchmarking 2020-09-8: Collecting 100 samples in estimated 5.0000 s (351M iterations) | |
| Benchmarking 2020-09-8: Analyzing | |
| 2020-09-8 time: [14.176 ns 14.268 ns 14.360 ns] | |
| change: [+5.6609% +6.5020% +7.3079%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 6 (6.00%) low mild | |
| 1 (1.00%) high severe | |
| Benchmarking 2020-9-08 | |
| Benchmarking 2020-9-08: Warming up for 3.0000 s | |
| Benchmarking 2020-9-08: Collecting 100 samples in estimated 5.0001 s (332M iterations) | |
| Benchmarking 2020-9-08: Analyzing | |
| 2020-9-08 time: [14.898 ns 14.969 ns 15.043 ns] | |
| change: [+11.672% +12.292% +12.926%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 1 (1.00%) low severe | |
| 5 (5.00%) low mild | |
| 1 (1.00%) high mild | |
| 1 (1.00%) high severe | |
| Running benches/parse_decimal.rs (target/x86_64-unknown-linux-gnu/release/deps/parse_decimal-0ff91e5875f2eb6b) | |
| Benchmarking 123.123 | |
| Benchmarking 123.123: Warming up for 3.0000 s | |
| Benchmarking 123.123: Collecting 100 samples in estimated 5.0000 s (283M iterations) | |
| Benchmarking 123.123: Analyzing | |
| 123.123 time: [17.613 ns 17.617 ns 17.622 ns] | |
| change: [+6.1771% +6.2083% +6.2394%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 6 (6.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking 123.1234 | |
| Benchmarking 123.1234: Warming up for 3.0000 s | |
| Benchmarking 123.1234: Collecting 100 samples in estimated 5.0001 s (272M iterations) | |
| Benchmarking 123.1234: Analyzing | |
| 123.1234 time: [18.319 ns 18.328 ns 18.337 ns] | |
| change: [+8.7122% +8.7595% +8.8078%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 1 (1.00%) low mild | |
| 1 (1.00%) high severe | |
| Benchmarking 123.1 | |
| Benchmarking 123.1: Warming up for 3.0000 s | |
| Benchmarking 123.1: Collecting 100 samples in estimated 5.0001 s (238M iterations) | |
| Benchmarking 123.1: Analyzing | |
| 123.1 time: [21.010 ns 21.017 ns 21.024 ns] | |
| change: [-16.521% -16.315% -16.129%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 1 (1.00%) low severe | |
| 2 (2.00%) low mild | |
| 5 (5.00%) high mild | |
| Benchmarking 123 | |
| Benchmarking 123: Warming up for 3.0000 s | |
| Benchmarking 123: Collecting 100 samples in estimated 5.0000 s (286M iterations) | |
| Benchmarking 123: Analyzing | |
| 123 time: [17.453 ns 17.462 ns 17.471 ns] | |
| change: [-32.182% -32.061% -31.988%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 16 outliers among 100 measurements (16.00%) | |
| 8 (8.00%) low severe | |
| 5 (5.00%) low mild | |
| 3 (3.00%) high mild | |
| Benchmarking -123.123 | |
| Benchmarking -123.123: Warming up for 3.0000 s | |
| Benchmarking -123.123: Collecting 100 samples in estimated 5.0001 s (272M iterations) | |
| Benchmarking -123.123: Analyzing | |
| -123.123 time: [18.409 ns 18.413 ns 18.418 ns] | |
| change: [+6.5201% +7.0779% +7.4921%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 1 (1.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking -123.1234 | |
| Benchmarking -123.1234: Warming up for 3.0000 s | |
| Benchmarking -123.1234: Collecting 100 samples in estimated 5.0000 s (261M iterations) | |
| Benchmarking -123.1234: Analyzing | |
| -123.1234 time: [19.115 ns 19.118 ns 19.121 ns] | |
| change: [+11.490% +11.552% +11.624%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 3 (3.00%) low mild | |
| 2 (2.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking -123.1 | |
| Benchmarking -123.1: Warming up for 3.0000 s | |
| Benchmarking -123.1: Collecting 100 samples in estimated 5.0001 s (232M iterations) | |
| Benchmarking -123.1: Analyzing | |
| -123.1 time: [21.517 ns 21.523 ns 21.530 ns] | |
| change: [-16.333% -16.166% -16.055%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 1 (1.00%) low severe | |
| 3 (3.00%) low mild | |
| 5 (5.00%) high mild | |
| Benchmarking -123 | |
| Benchmarking -123: Warming up for 3.0000 s | |
| Benchmarking -123: Collecting 100 samples in estimated 5.0000 s (277M iterations) | |
| Benchmarking -123: Analyzing | |
| -123 time: [18.038 ns 18.046 ns 18.054 ns] | |
| change: [-31.070% -30.920% -30.830%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 20 outliers among 100 measurements (20.00%) | |
| 7 (7.00%) low severe | |
| 5 (5.00%) low mild | |
| 6 (6.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking 0.0000123 | |
| Benchmarking 0.0000123: Warming up for 3.0000 s | |
| Benchmarking 0.0000123: Collecting 100 samples in estimated 5.0000 s (314M iterations) | |
| Benchmarking 0.0000123: Analyzing | |
| 0.0000123 time: [15.950 ns 15.959 ns 15.968 ns] | |
| change: [+10.742% +10.839% +10.951%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 13 outliers among 100 measurements (13.00%) | |
| 2 (2.00%) low mild | |
| 6 (6.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking 12. | |
| Benchmarking 12.: Warming up for 3.0000 s | |
| Benchmarking 12.: Collecting 100 samples in estimated 5.0000 s (303M iterations) | |
| Benchmarking 12.: Analyzing | |
| 12. time: [16.492 ns 16.496 ns 16.500 ns] | |
| change: [-35.336% -35.266% -35.191%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 1 (1.00%) low mild | |
| 4 (4.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking -12. | |
| Benchmarking -12.: Warming up for 3.0000 s | |
| Benchmarking -12.: Collecting 100 samples in estimated 5.0001 s (291M iterations) | |
| Benchmarking -12.: Analyzing | |
| -12. time: [17.181 ns 17.188 ns 17.195 ns] | |
| change: [-34.379% -34.021% -33.808%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Benchmarking 00.1 | |
| Benchmarking 00.1: Warming up for 3.0000 s | |
| Benchmarking 00.1: Collecting 100 samples in estimated 5.0000 s (327M iterations) | |
| Benchmarking 00.1: Analyzing | |
| 00.1 time: [15.306 ns 15.322 ns 15.339 ns] | |
| change: [-24.872% -24.779% -24.682%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 12 (12.00%) high mild | |
| Benchmarking -00.1 | |
| Benchmarking -00.1: Warming up for 3.0000 s | |
| Benchmarking -00.1: Collecting 100 samples in estimated 5.0000 s (303M iterations) | |
| Benchmarking -00.1: Analyzing | |
| -00.1 time: [16.544 ns 16.549 ns 16.555 ns] | |
| change: [-21.872% -21.805% -21.741%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 4 (4.00%) low mild | |
| Benchmarking 12345678912345678.1234 | |
| Benchmarking 12345678912345678.1234: Warming up for 3.0000 s | |
| Benchmarking 12345678912345678.1234: Collecting 100 samples in estimated 5.0002 s (103M iterations) | |
| Benchmarking 12345678912345678.1234: Analyzing | |
| 12345678912345678.1234 time: [48.621 ns 48.627 ns 48.633 ns] | |
| change: [+2.8277% +2.8485% +2.8669%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 3 (3.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking -12345678912345678.1234 | |
| Benchmarking -12345678912345678.1234: Warming up for 3.0000 s | |
| Benchmarking -12345678912345678.1234: Collecting 100 samples in estimated 5.0001 s (102M iterations) | |
| Benchmarking -12345678912345678.1234: Analyzing | |
| -12345678912345678.1234 time: [49.253 ns 49.265 ns 49.282 ns] | |
| change: [+0.7568% +1.1036% +1.3004%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 13 outliers among 100 measurements (13.00%) | |
| 4 (4.00%) low mild | |
| 5 (5.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking 99999999999999999.999 | |
| Benchmarking 99999999999999999.999: Warming up for 3.0000 s | |
| Benchmarking 99999999999999999.999: Collecting 100 samples in estimated 5.0002 s (105M iterations) | |
| Benchmarking 99999999999999999.999: Analyzing | |
| 99999999999999999.999 time: [47.750 ns 47.762 ns 47.773 ns] | |
| change: [+1.5417% +1.5759% +1.6065%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 1 (1.00%) low mild | |
| 1 (1.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking -99999999999999999.999 | |
| Benchmarking -99999999999999999.999: Warming up for 3.0000 s | |
| Benchmarking -99999999999999999.999: Collecting 100 samples in estimated 5.0000 s (103M iterations) | |
| Benchmarking -99999999999999999.999: Analyzing | |
| -99999999999999999.999 time: [48.647 ns 48.652 ns 48.659 ns] | |
| change: [+8.6568% +8.6884% +8.7190%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 14 outliers among 100 measurements (14.00%) | |
| 6 (6.00%) high mild | |
| 8 (8.00%) high severe | |
| Benchmarking .123 | |
| Benchmarking .123: Warming up for 3.0000 s | |
| Benchmarking .123: Collecting 100 samples in estimated 5.0000 s (410M iterations) | |
| Benchmarking .123: Analyzing | |
| .123 time: [12.183 ns 12.193 ns 12.203 ns] | |
| change: [-6.6155% -6.5387% -6.4673%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 20 outliers among 100 measurements (20.00%) | |
| 7 (7.00%) low severe | |
| 5 (5.00%) low mild | |
| 8 (8.00%) high mild | |
| Benchmarking -.123 | |
| Benchmarking -.123: Warming up for 3.0000 s | |
| Benchmarking -.123: Collecting 100 samples in estimated 5.0001 s (398M iterations) | |
| Benchmarking -.123: Analyzing | |
| -.123 time: [12.587 ns 12.604 ns 12.622 ns] | |
| change: [-7.3427% -7.2520% -7.1396%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 1 (1.00%) low mild | |
| 4 (4.00%) high mild | |
| Benchmarking 123. | |
| Benchmarking 123.: Warming up for 3.0000 s | |
| Benchmarking 123.: Collecting 100 samples in estimated 5.0000 s (270M iterations) | |
| Benchmarking 123.: Analyzing | |
| 123. time: [18.550 ns 18.554 ns 18.558 ns] | |
| change: [-30.140% -30.077% -30.010%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 1 (1.00%) low severe | |
| 1 (1.00%) low mild | |
| 3 (3.00%) high mild | |
| Benchmarking -123. | |
| Benchmarking -123.: Warming up for 3.0000 s | |
| Benchmarking -123.: Collecting 100 samples in estimated 5.0000 s (258M iterations) | |
| Benchmarking -123.: Analyzing | |
| -123. time: [19.346 ns 19.351 ns 19.356 ns] | |
| change: [-28.670% -28.575% -28.516%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 6 (6.00%) low mild | |
| 2 (2.00%) high mild | |
| 3 (3.00%) high severe | |
| Running benches/parse_time.rs (target/x86_64-unknown-linux-gnu/release/deps/parse_time-d4a4dab6bcb48847) | |
| Benchmarking 9:50 | |
| Benchmarking 9:50: Warming up for 3.0000 s | |
| Benchmarking 9:50: Collecting 100 samples in estimated 5.0000 s (877M iterations) | |
| Benchmarking 9:50: Analyzing | |
| 9:50 time: [5.7024 ns 5.7032 ns 5.7041 ns] | |
| change: [-12.525% -12.499% -12.473%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 14 outliers among 100 measurements (14.00%) | |
| 1 (1.00%) low severe | |
| 3 (3.00%) high mild | |
| 10 (10.00%) high severe | |
| Benchmarking 09:50 | |
| Benchmarking 09:50: Warming up for 3.0000 s | |
| Benchmarking 09:50: Collecting 100 samples in estimated 5.0000 s (837M iterations) | |
| Benchmarking 09:50: Analyzing | |
| 09:50 time: [5.9736 ns 5.9742 ns 5.9749 ns] | |
| change: [-12.418% -12.141% -11.994%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 1 (1.00%) low mild | |
| 4 (4.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking 09:50 PM | |
| Benchmarking 09:50 PM: Warming up for 3.0000 s | |
| Benchmarking 09:50 PM: Collecting 100 samples in estimated 5.0000 s (767M iterations) | |
| Benchmarking 09:50 PM: Analyzing | |
| 09:50 PM time: [6.5168 ns 6.5175 ns 6.5184 ns] | |
| change: [-14.649% -14.622% -14.598%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 3 (3.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking 9:50:12 AM | |
| Benchmarking 9:50:12 AM: Warming up for 3.0000 s | |
| Benchmarking 9:50:12 AM: Collecting 100 samples in estimated 5.0000 s (708M iterations) | |
| Benchmarking 9:50:12 AM: Analyzing | |
| 9:50:12 AM time: [7.0599 ns 7.0606 ns 7.0616 ns] | |
| change: [-7.2284% -7.1678% -7.1306%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 3 (3.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking 09:50:12 PM | |
| Benchmarking 09:50:12 PM: Warming up for 3.0000 s | |
| Benchmarking 09:50:12 PM: Collecting 100 samples in estimated 5.0000 s (707M iterations) | |
| Benchmarking 09:50:12 PM: Analyzing | |
| 09:50:12 PM time: [7.0601 ns 7.0615 ns 7.0634 ns] | |
| change: [-11.104% -10.593% -10.322%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 9 outliers among 100 measurements (9.00%) | |
| 2 (2.00%) high mild | |
| 7 (7.00%) high severe | |
| Benchmarking 09:50:12.123456789 | |
| Benchmarking 09:50:12.123456789: Warming up for 3.0000 s | |
| Benchmarking 09:50:12.123456789: Collecting 100 samples in estimated 5.0000 s (419M iterations) | |
| Benchmarking 09:50:12.123456789: Analyzing | |
| 09:50:12.123456789 time: [11.930 ns 11.938 ns 11.945 ns] | |
| change: [-12.186% -12.094% -12.017%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 22 outliers among 100 measurements (22.00%) | |
| 11 (11.00%) low severe | |
| 1 (1.00%) low mild | |
| 3 (3.00%) high mild | |
| 7 (7.00%) high severe | |
| Benchmarking 9:50:12.123456789 | |
| Benchmarking 9:50:12.123456789: Warming up for 3.0000 s | |
| Benchmarking 9:50:12.123456789: Collecting 100 samples in estimated 5.0000 s (419M iterations) | |
| Benchmarking 9:50:12.123456789: Analyzing | |
| 9:50:12.123456789 time: [11.925 ns 11.928 ns 11.932 ns] | |
| change: [-8.5332% -8.4973% -8.4661%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) low mild | |
| Benchmarking 09:50:12.123456789 PM | |
| Benchmarking 09:50:12.123456789 PM: Warming up for 3.0000 s | |
| Benchmarking 09:50:12.123456789 PM: Collecting 100 samples in estimated 5.0000 s (393M iterations) | |
| Benchmarking 09:50:12.123456789 PM: Analyzing | |
| 09:50:12.123456789 PM time: [12.733 ns 12.738 ns 12.743 ns] | |
| change: [-11.489% -11.428% -11.357%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 3 (3.00%) low severe | |
| 6 (6.00%) low mild | |
| 2 (2.00%) high mild | |
| Running benches/parse_timestamp.rs (target/x86_64-unknown-linux-gnu/release/deps/parse_timestamp-6ddad87cc0bbb283) | |
| Benchmarking 2020-09-08 | |
| Benchmarking 2020-09-08: Warming up for 3.0000 s | |
| Benchmarking 2020-09-08: Collecting 100 samples in estimated 5.0001 s (157M iterations) | |
| Benchmarking 2020-09-08: Analyzing | |
| 2020-09-08 time: [31.873 ns 31.914 ns 31.960 ns] | |
| change: [+86.887% +87.321% +87.711%] (p = 0.00 < 0.05) | |
| Performance has regressed. | |
| Found 15 outliers among 100 measurements (15.00%) | |
| 2 (2.00%) low mild | |
| 3 (3.00%) high mild | |
| 10 (10.00%) high severe | |
| Benchmarking 2020-09-08T13:42:29 | |
| Benchmarking 2020-09-08T13:42:29: Warming up for 3.0000 s | |
| Benchmarking 2020-09-08T13:42:29: Collecting 100 samples in estimated 5.0001 s (113M iterations) | |
| Benchmarking 2020-09-08T13:42:29: Analyzing | |
| 2020-09-08T13:42:29 time: [43.599 ns 43.673 ns 43.754 ns] | |
| change: [-5.5153% -5.3865% -5.2530%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 14 outliers among 100 measurements (14.00%) | |
| 1 (1.00%) low mild | |
| 4 (4.00%) high mild | |
| 9 (9.00%) high severe | |
| Benchmarking 2020-09-08T13:42:29.190 | |
| Benchmarking 2020-09-08T13:42:29.190: Warming up for 3.0000 s | |
| Benchmarking 2020-09-08T13:42:29.190: Collecting 100 samples in estimated 5.0002 s (111M iterations) | |
| Benchmarking 2020-09-08T13:42:29.190: Analyzing | |
| 2020-09-08T13:42:29.190 time: [44.434 ns 44.469 ns 44.510 ns] | |
| change: [-6.9815% -6.8587% -6.7398%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 2 (2.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking 2020-09-08T13:42:29.190855 | |
| Benchmarking 2020-09-08T13:42:29.190855: Warming up for 3.0000 s | |
| Benchmarking 2020-09-08T13:42:29.190855: Collecting 100 samples in estimated 5.0001 s (117M iterations) | |
| Benchmarking 2020-09-08T13:42:29.190855: Analyzing | |
| 2020-09-08T13:42:29.190855 | |
| time: [43.172 ns 43.242 ns 43.322 ns] | |
| change: [-6.3462% -6.1555% -5.9364%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 13 outliers among 100 measurements (13.00%) | |
| 8 (8.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking 2020-09-08T13:42:29.190855999 | |
| Benchmarking 2020-09-08T13:42:29.190855999: Warming up for 3.0000 s | |
| Benchmarking 2020-09-08T13:42:29.190855999: Collecting 100 samples in estimated 5.0002 s (116M iterations) | |
| Benchmarking 2020-09-08T13:42:29.190855999: Analyzing | |
| 2020-09-08T13:42:29.190855999 | |
| time: [43.624 ns 43.667 ns 43.712 ns] | |
| change: [-5.2712% -5.1103% -4.9452%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 5 (5.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking 2020-09-08T13:42:29+00:00 | |
| Benchmarking 2020-09-08T13:42:29+00:00: Warming up for 3.0000 s | |
| Benchmarking 2020-09-08T13:42:29+00:00: Collecting 100 samples in estimated 5.0001 s (115M iterations) | |
| Benchmarking 2020-09-08T13:42:29+00:00: Analyzing | |
| 2020-09-08T13:42:29+00:00 | |
| time: [43.697 ns 43.734 ns 43.777 ns] | |
| change: [-8.2479% -8.0708% -7.9023%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 13 outliers among 100 measurements (13.00%) | |
| 3 (3.00%) low mild | |
| 5 (5.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking 2020-09-08T13:42:29.190+00:00 | |
| Benchmarking 2020-09-08T13:42:29.190+00:00: Warming up for 3.0000 s | |
| Benchmarking 2020-09-08T13:42:29.190+00:00: Collecting 100 samples in estimated 5.0001 s (110M iterations) | |
| Benchmarking 2020-09-08T13:42:29.190+00:00: Analyzing | |
| 2020-09-08T13:42:29.190+00:00 | |
| time: [45.469 ns 45.485 ns 45.502 ns] | |
| change: [-6.0667% -6.0059% -5.9464%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 7 (7.00%) high mild | |
| Benchmarking 2020-09-08T13:42:29.190855+00:00 | |
| Benchmarking 2020-09-08T13:42:29.190855+00:00: Warming up for 3.0000 s | |
| Benchmarking 2020-09-08T13:42:29.190855+00:00: Collecting 100 samples in estimated 5.0001 s (109M iterations) | |
| Benchmarking 2020-09-08T13:42:29.190855+00:00: Analyzing | |
| 2020-09-08T13:42:29.190855+00:00 | |
| time: [45.802 ns 45.926 ns 46.083 ns] | |
| change: [-6.1658% -5.9478% -5.7096%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 1 (1.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking 2020-09-08T13:42:29.190855999-05:00 | |
| Benchmarking 2020-09-08T13:42:29.190855999-05:00: Warming up for 3.0000 s | |
| Benchmarking 2020-09-08T13:42:29.190855999-05:00: Collecting 100 samples in estimated 5.0001 s (106M iterations) | |
| Benchmarking 2020-09-08T13:42:29.190855999-05:00: Analyzing | |
| 2020-09-08T13:42:29.190855999-05:00 | |
| time: [46.858 ns 46.920 ns 47.013 ns] | |
| change: [-4.8899% -4.7966% -4.6419%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 4 (4.00%) low mild | |
| 1 (1.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking 2020-09-08T13:42:29.190855Z | |
| Benchmarking 2020-09-08T13:42:29.190855Z: Warming up for 3.0000 s | |
| Benchmarking 2020-09-08T13:42:29.190855Z: Collecting 100 samples in estimated 5.0001 s (117M iterations) | |
| Benchmarking 2020-09-08T13:42:29.190855Z: Analyzing | |
| 2020-09-08T13:42:29.190855Z | |
| time: [42.975 ns 43.057 ns 43.152 ns] | |
| change: [-1.7236% -1.5211% -1.3191%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 8 (8.00%) high mild | |
| 2 (2.00%) high severe | |
| Running unittests src/lib.rs (target/x86_64-unknown-linux-gnu/release/deps/arrow_flight-486bd9a0f6ede507) | |
| Running unittests src/lib.rs (target/x86_64-unknown-linux-gnu/release/deps/arrow_integration_testing-93fe23df54dd264c) | |
| running 28 tests | |
| test encode::tests::flight_data_size_even ... ignored | |
| test encode::tests::flight_data_size_large_dictionary ... ignored | |
| test encode::tests::flight_data_size_large_dictionary_repeated_non_uniform ... ignored | |
| test encode::tests::flight_data_size_large_row ... ignored | |
| test encode::tests::flight_data_size_multiple_dictionaries ... ignored | |
| test encode::tests::flight_data_size_string_dictionary ... ignored | |
| test encode::tests::flight_data_size_uneven_variable_lengths ... ignored | |
| test encode::tests::test_dictionary_hydration ... ignored | |
| test encode::tests::test_dictionary_list_hydration ... ignored | |
| test encode::tests::test_dictionary_list_resend ... ignored | |
| test encode::tests::test_dictionary_resend ... ignored | |
| test encode::tests::test_dictionary_struct_hydration ... ignored | |
| test encode::tests::test_dictionary_struct_resend ... ignored | |
| test encode::tests::test_dictionary_union_hydration ... ignored | |
| test encode::tests::test_dictionary_union_resend ... ignored | |
| test encode::tests::test_encode_flight_data ... ignored | |
| test encode::tests::test_encode_no_column_batch ... ignored | |
| test encode::tests::test_multiple_dictionaries_resend ... ignored | |
| test encode::tests::test_schema_metadata_encoded ... ignored | |
| test encode::tests::test_split_batch_for_grpc_response ... ignored | |
| test encode::tests::test_split_batch_for_grpc_response_sizes ... ignored | |
| test error::test::error_source ... ignored | |
| test error::test::error_through_arrow ... ignored | |
| test tests::it_accepts_equal_output ... ignored | |
| test tests::it_accepts_long_output ... ignored | |
| test tests::it_accepts_short_output ... ignored | |
| test tests::it_creates_flight_descriptor_command ... ignored | |
| test tests::ser_deser_schema_result ... ignored | |
| test result: ok. 0 passed; 0 failed; 28 ignored; 0 measured; 0 filtered out; finished in 0.00s | |
| running 0 tests | |
| test result: ok. 0 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s | |
| Running unittests src/bin/arrow-file-to-stream.rs (target/x86_64-unknown-linux-gnu/release/deps/arrow_file_to_stream-c71878274a7a921c) | |
| Running unittests src/bin/arrow-json-integration-test.rs (target/x86_64-unknown-linux-gnu/release/deps/arrow_json_integration_test-294d4410663dd35e) | |
| running 0 tests | |
| test result: ok. 0 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s | |
| running 0 tests | |
| test result: ok. 0 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s | |
| Running unittests src/bin/arrow-stream-to-file.rs (target/x86_64-unknown-linux-gnu/release/deps/arrow_stream_to_file-c19be7ca56c39ed4) | |
| Running unittests src/bin/flight-test-integration-client.rs (target/x86_64-unknown-linux-gnu/release/deps/flight_test_integration_client-57d0e8cd31175c0e) | |
| running 0 tests | |
| test result: ok. 0 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s | |
| running 0 tests | |
| test result: ok. 0 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s | |
| Running unittests src/bin/flight-test-integration-server.rs (target/x86_64-unknown-linux-gnu/release/deps/flight_test_integration_server-38e10f81c48574f7) | |
| Running benches/serde.rs (target/x86_64-unknown-linux-gnu/release/deps/serde-3d0fc105fb9873ac) | |
| running 0 tests | |
| test result: ok. 0 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s | |
| Benchmarking small_i32 | |
| Benchmarking small_i32: Warming up for 3.0000 s | |
| Benchmarking small_i32: Collecting 100 samples in estimated 5.0215 s (773k iterations) | |
| Benchmarking small_i32: Analyzing | |
| small_i32 time: [6.4922 µs 6.4936 µs 6.4951 µs] | |
| change: [+0.3613% +0.4485% +0.5367%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 1 (1.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking large_i32 | |
| Benchmarking large_i32: Warming up for 3.0000 s | |
| Benchmarking large_i32: Collecting 100 samples in estimated 5.0094 s (793k iterations) | |
| Benchmarking large_i32: Analyzing | |
| large_i32 time: [6.3156 µs 6.3167 µs 6.3179 µs] | |
| change: [+0.0363% +0.3782% +0.7194%] (p = 0.03 < 0.05) | |
| Change within noise threshold. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 1 (1.00%) low mild | |
| 5 (5.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking small_i64 | |
| Benchmarking small_i64: Warming up for 3.0000 s | |
| Benchmarking small_i64: Collecting 100 samples in estimated 5.0269 s (616k iterations) | |
| Benchmarking small_i64: Analyzing | |
| small_i64 time: [8.1630 µs 8.1639 µs 8.1651 µs] | |
| change: [-2.7796% -2.7580% -2.7370%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 5 (5.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking medium_i64 | |
| Benchmarking medium_i64: Warming up for 3.0000 s | |
| Benchmarking medium_i64: Collecting 100 samples in estimated 5.0112 s (621k iterations) | |
| Benchmarking medium_i64: Analyzing | |
| medium_i64 time: [8.0592 µs 8.0859 µs 8.1209 µs] | |
| change: [-3.5131% -3.0890% -2.5010%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 3 (3.00%) high mild | |
| 8 (8.00%) high severe | |
| Benchmarking large_i64 | |
| Benchmarking large_i64: Warming up for 3.0000 s | |
| Benchmarking large_i64: Collecting 100 samples in estimated 5.0153 s (621k iterations) | |
| Benchmarking large_i64: Analyzing | |
| large_i64 time: [8.0448 µs 8.0651 µs 8.0932 µs] | |
| change: [-1.9885% -1.8579% -1.6784%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 14 outliers among 100 measurements (14.00%) | |
| 1 (1.00%) low mild | |
| 5 (5.00%) high mild | |
| 8 (8.00%) high severe | |
| Benchmarking small_f32 | |
| Benchmarking small_f32: Warming up for 3.0000 s | |
| Benchmarking small_f32: Collecting 100 samples in estimated 5.0237 s (980k iterations) | |
| Benchmarking small_f32: Analyzing | |
| small_f32 time: [5.1196 µs 5.1437 µs 5.1736 µs] | |
| change: [-12.227% -11.077% -9.8478%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 10 outliers among 100 measurements (10.00%) | |
| 4 (4.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking large_f32 | |
| Benchmarking large_f32: Warming up for 3.0000 s | |
| Benchmarking large_f32: Collecting 100 samples in estimated 5.0139 s (975k iterations) | |
| Benchmarking large_f32: Analyzing | |
| large_f32 time: [5.1262 µs 5.1425 µs 5.1670 µs] | |
| change: [-11.471% -10.164% -8.7585%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 14 outliers among 100 measurements (14.00%) | |
| 7 (7.00%) high mild | |
| 7 (7.00%) high severe | |
| Running unittests src/main.rs (target/x86_64-unknown-linux-gnu/release/deps/gen-aedb983bc4f016ce) | |
| Running benches/arrow_statistics.rs (target/x86_64-unknown-linux-gnu/release/deps/arrow_statistics-6bb1dff1667c2fda) | |
| running 0 tests | |
| test result: ok. 0 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s | |
| Benchmarking Extract row group statistics for Int64/extract_statistics/Int64 | |
| Benchmarking Extract row group statistics for Int64/extract_statistics/Int64: Warming up for 3.0000 s | |
| Benchmarking Extract row group statistics for Int64/extract_statistics/Int64: Collecting 100 samples in estimated 5.0035 s (6.7M iterations) | |
| Benchmarking Extract row group statistics for Int64/extract_statistics/Int64: Analyzing | |
| Extract row group statistics for Int64/extract_statistics/Int64 | |
| time: [745.11 ns 748.94 ns 753.60 ns] | |
| change: [-5.8483% -5.6130% -5.2600%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 1 (1.00%) low mild | |
| 3 (3.00%) high severe | |
| Benchmarking Extract data page statistics for Int64/extract_statistics/Int64 | |
| Benchmarking Extract data page statistics for Int64/extract_statistics/Int64: Warming up for 3.0000 s | |
| Benchmarking Extract data page statistics for Int64/extract_statistics/Int64: Collecting 100 samples in estimated 5.0223 s (500k iterations) | |
| Benchmarking Extract data page statistics for Int64/extract_statistics/Int64: Analyzing | |
| Extract data page statistics for Int64/extract_statistics/Int64 | |
| time: [10.061 µs 10.106 µs 10.171 µs] | |
| change: [-18.502% -18.353% -18.140%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 4 (4.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking Extract row group statistics for UInt64/extract_statistics/UInt64 | |
| Benchmarking Extract row group statistics for UInt64/extract_statistics/UInt64: Warming up for 3.0000 s | |
| Benchmarking Extract row group statistics for UInt64/extract_statistics/UInt64: Collecting 100 samples in estimated 5.0000 s (7.3M iterations) | |
| Benchmarking Extract row group statistics for UInt64/extract_statistics/UInt64: Analyzing | |
| Extract row group statistics for UInt64/extract_statistics/UInt64 | |
| time: [684.52 ns 685.74 ns 688.11 ns] | |
| change: [-14.563% -14.299% -14.089%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 2 outliers among 100 measurements (2.00%) | |
| 2 (2.00%) high severe | |
| Benchmarking Extract data page statistics for UInt64/extract_statistics/UInt64 | |
| Benchmarking Extract data page statistics for UInt64/extract_statistics/UInt64: Warming up for 3.0000 s | |
| Benchmarking Extract data page statistics for UInt64/extract_statistics/UInt64: Collecting 100 samples in estimated 5.0168 s (470k iterations) | |
| Benchmarking Extract data page statistics for UInt64/extract_statistics/UInt64: Analyzing | |
| Extract data page statistics for UInt64/extract_statistics/UInt64 | |
| time: [10.681 µs 10.686 µs 10.694 µs] | |
| change: [-10.152% -9.6427% -8.6680%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 5 (5.00%) low severe | |
| 2 (2.00%) high mild | |
| 1 (1.00%) high severe | |
| Benchmarking Extract row group statistics for F64/extract_statistics/F64 | |
| Benchmarking Extract row group statistics for F64/extract_statistics/F64: Warming up for 3.0000 s | |
| Benchmarking Extract row group statistics for F64/extract_statistics/F64: Collecting 100 samples in estimated 5.0017 s (7.3M iterations) | |
| Benchmarking Extract row group statistics for F64/extract_statistics/F64: Analyzing | |
| Extract row group statistics for F64/extract_statistics/F64 | |
| time: [680.49 ns 680.88 ns 681.28 ns] | |
| change: [-16.254% -16.208% -16.160%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 3 (3.00%) high mild | |
| Benchmarking Extract data page statistics for F64/extract_statistics/F64 | |
| Benchmarking Extract data page statistics for F64/extract_statistics/F64: Warming up for 3.0000 s | |
| Benchmarking Extract data page statistics for F64/extract_statistics/F64: Collecting 100 samples in estimated 5.0267 s (485k iterations) | |
| Benchmarking Extract data page statistics for F64/extract_statistics/F64: Analyzing | |
| Extract data page statistics for F64/extract_statistics/F64 | |
| time: [10.384 µs 10.388 µs 10.393 µs] | |
| change: [-16.507% -16.468% -16.434%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 4 (4.00%) high mild | |
| Benchmarking Extract row group statistics for String/extract_statistics/String | |
| Benchmarking Extract row group statistics for String/extract_statistics/String: Warming up for 3.0000 s | |
| Benchmarking Extract row group statistics for String/extract_statistics/String: Collecting 100 samples in estimated 5.0044 s (5.4M iterations) | |
| Benchmarking Extract row group statistics for String/extract_statistics/String: Analyzing | |
| Extract row group statistics for String/extract_statistics/String | |
| time: [918.89 ns 919.63 ns 920.79 ns] | |
| change: [-5.4258% -5.2138% -4.8484%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 2 (2.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking Extract data page statistics for String/extract_statistics/String | |
| Benchmarking Extract data page statistics for String/extract_statistics/String: Warming up for 3.0000 s | |
| Benchmarking Extract data page statistics for String/extract_statistics/String: Collecting 100 samples in estimated 5.0685 s (177k iterations) | |
| Benchmarking Extract data page statistics for String/extract_statistics/String: Analyzing | |
| Extract data page statistics for String/extract_statistics/String | |
| time: [28.397 µs 28.401 µs 28.405 µs] | |
| change: [+0.2864% +0.3463% +0.4002%] (p = 0.00 < 0.05) | |
| Change within noise threshold. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 6 (6.00%) high mild | |
| 6 (6.00%) high severe | |
| Benchmarking Extract row group statistics for Dictionary(Int32, String)/extract_statistics/Dictionary(Int32, Stri... | |
| Benchmarking Extract row group statistics for Dictionary(Int32, String)/extract_statistics/Dictionary(Int32, Stri...: Warming up for 3.0000 s | |
| Benchmarking Extract row group statistics for Dictionary(Int32, String)/extract_statistics/Dictionary(Int32, Stri...: Collecting 100 samples in estimated 5.0020 s (5.5M iterations) | |
| Benchmarking Extract row group statistics for Dictionary(Int32, String)/extract_statistics/Dictionary(Int32, Stri...: Analyzing | |
| Extract row group statistics for Dictionary(Int32, String)/extract_statistics/Dictionary(Int32, Stri... | |
| time: [916.87 ns 917.23 ns 917.58 ns] | |
| change: [-6.9203% -6.8577% -6.7941%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high mild | |
| Benchmarking Extract data page statistics for Dictionary(Int32, String)/extract_statistics/Dictionary(Int32, Stri... | |
| Benchmarking Extract data page statistics for Dictionary(Int32, String)/extract_statistics/Dictionary(Int32, Stri...: Warming up for 3.0000 s | |
| Benchmarking Extract data page statistics for Dictionary(Int32, String)/extract_statistics/Dictionary(Int32, Stri...: Collecting 100 samples in estimated 5.1182 s (182k iterations) | |
| Benchmarking Extract data page statistics for Dictionary(Int32, String)/extract_statistics/Dictionary(Int32, Stri...: Analyzing | |
| Extract data page statistics for Dictionary(Int32, String)/extract_statistics/Dictionary(Int32, Stri... | |
| time: [27.882 µs 27.888 µs 27.895 µs] | |
| change: [-1.1528% -1.0938% -1.0387%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 3 outliers among 100 measurements (3.00%) | |
| 2 (2.00%) high mild | |
| 1 (1.00%) high severe | |
| Running benches/arrow_writer.rs (target/x86_64-unknown-linux-gnu/release/deps/arrow_writer-59123d25b5ca7a30) | |
| Benchmarking write_batch primitive/4096 values primitive | |
| Benchmarking write_batch primitive/4096 values primitive: Warming up for 3.0000 s | |
| Benchmarking write_batch primitive/4096 values primitive: Collecting 100 samples in estimated 9.3096 s (10k iterations) | |
| Benchmarking write_batch primitive/4096 values primitive: Analyzing | |
| write_batch primitive/4096 values primitive | |
| time: [920.90 µs 921.39 µs 922.14 µs] | |
| thrpt: [190.79 MiB/s 190.94 MiB/s 191.05 MiB/s] | |
| change: | |
| time: [-14.843% -14.733% -14.558%] (p = 0.00 < 0.05) | |
| thrpt: [+17.038% +17.279% +17.430%] | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 2 (2.00%) low mild | |
| 1 (1.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking write_batch primitive/4096 values primitive with bloom filter | |
| Benchmarking write_batch primitive/4096 values primitive with bloom filter: Warming up for 3.0000 s | |
| Benchmarking write_batch primitive/4096 values primitive with bloom filter: Collecting 100 samples in estimated 5.9899 s (600 iterations) | |
| Benchmarking write_batch primitive/4096 values primitive with bloom filter: Analyzing | |
| write_batch primitive/4096 values primitive with bloom filter | |
| time: [9.9806 ms 9.9892 ms 9.9991 ms] | |
| thrpt: [17.595 MiB/s 17.612 MiB/s 17.628 MiB/s] | |
| change: | |
| time: [-1.3152% -1.0248% -0.7909%] (p = 0.00 < 0.05) | |
| thrpt: [+0.7972% +1.0354% +1.3327%] | |
| Change within noise threshold. | |
| Found 16 outliers among 100 measurements (16.00%) | |
| 3 (3.00%) high mild | |
| 13 (13.00%) high severe | |
| Benchmarking write_batch primitive/4096 values primitive non-null | |
| Benchmarking write_batch primitive/4096 values primitive non-null: Warming up for 3.0000 s | |
| Benchmarking write_batch primitive/4096 values primitive non-null: Collecting 100 samples in estimated 6.9538 s (10k iterations) | |
| Benchmarking write_batch primitive/4096 values primitive non-null: Analyzing | |
| write_batch primitive/4096 values primitive non-null | |
| time: [687.62 µs 687.73 µs 687.86 µs] | |
| thrpt: [250.80 MiB/s 250.85 MiB/s 250.89 MiB/s] | |
| change: | |
| time: [-16.271% -16.219% -16.164%] (p = 0.00 < 0.05) | |
| thrpt: [+19.281% +19.359% +19.433%] | |
| Performance has improved. | |
| Found 8 outliers among 100 measurements (8.00%) | |
| 1 (1.00%) low mild | |
| 2 (2.00%) high mild | |
| 5 (5.00%) high severe | |
| Benchmarking write_batch primitive/4096 values primitive non-null with bloom filter | |
| Benchmarking write_batch primitive/4096 values primitive non-null with bloom filter: Warming up for 3.0000 s | |
| Benchmarking write_batch primitive/4096 values primitive non-null with bloom filter: Collecting 100 samples in estimated 5.8477 s (600 iterations) | |
| Benchmarking write_batch primitive/4096 values primitive non-null with bloom filter: Analyzing | |
| write_batch primitive/4096 values primitive non-null with bloom filter | |
| time: [9.7397 ms 9.7469 ms 9.7548 ms] | |
| thrpt: [17.685 MiB/s 17.700 MiB/s 17.713 MiB/s] | |
| change: | |
| time: [-2.5737% -2.4440% -2.3189%] (p = 0.00 < 0.05) | |
| thrpt: [+2.3739% +2.5053% +2.6417%] | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 3 (3.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking write_batch primitive/4096 values bool | |
| Benchmarking write_batch primitive/4096 values bool: Warming up for 3.0000 s | |
| Benchmarking write_batch primitive/4096 values bool: Collecting 100 samples in estimated 5.2137 s (71k iterations) | |
| Benchmarking write_batch primitive/4096 values bool: Analyzing | |
| write_batch primitive/4096 values bool | |
| time: [73.838 µs 73.946 µs 74.055 µs] | |
| thrpt: [14.320 MiB/s 14.341 MiB/s 14.362 MiB/s] | |
| change: | |
| time: [-15.959% -15.824% -15.676%] (p = 0.00 < 0.05) | |
| thrpt: [+18.590% +18.799% +18.990%] | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 4 (4.00%) high mild | |
| Benchmarking write_batch primitive/4096 values bool non-null | |
| Benchmarking write_batch primitive/4096 values bool non-null: Warming up for 3.0000 s | |
| Benchmarking write_batch primitive/4096 values bool non-null: Collecting 100 samples in estimated 5.0516 s (136k iterations) | |
| Benchmarking write_batch primitive/4096 values bool non-null: Analyzing | |
| write_batch primitive/4096 values bool non-null | |
| time: [37.124 µs 37.141 µs 37.156 µs] | |
| thrpt: [15.400 MiB/s 15.406 MiB/s 15.414 MiB/s] | |
| change: | |
| time: [-28.198% -28.096% -27.994%] (p = 0.00 < 0.05) | |
| thrpt: [+38.878% +39.074% +39.271%] | |
| Performance has improved. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high mild | |
| Benchmarking write_batch primitive/4096 values string | |
| Benchmarking write_batch primitive/4096 values string: Warming up for 3.0000 s | |
| Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 7.0s, enable flat sampling, or reduce sample count to 50. | |
| Benchmarking write_batch primitive/4096 values string: Collecting 100 samples in estimated 6.9839 s (5050 iterations) | |
| Benchmarking write_batch primitive/4096 values string: Analyzing | |
| write_batch primitive/4096 values string | |
| time: [1.3827 ms 1.3869 ms 1.3917 ms] | |
| thrpt: [1.4372 GiB/s 1.4423 GiB/s 1.4466 GiB/s] | |
| change: | |
| time: [-5.7047% -5.5052% -5.2574%] (p = 0.00 < 0.05) | |
| thrpt: [+5.5492% +5.8259% +6.0499%] | |
| Performance has improved. | |
| Found 12 outliers among 100 measurements (12.00%) | |
| 1 (1.00%) low mild | |
| 3 (3.00%) high mild | |
| 8 (8.00%) high severe | |
| Benchmarking write_batch primitive/4096 values string with bloom filter | |
| Benchmarking write_batch primitive/4096 values string with bloom filter: Warming up for 3.0000 s | |
| Benchmarking write_batch primitive/4096 values string with bloom filter: Collecting 100 samples in estimated 5.4340 s (1100 iterations) | |
| Benchmarking write_batch primitive/4096 values string with bloom filter: Analyzing | |
| write_batch primitive/4096 values string with bloom filter | |
| time: [4.9347 ms 4.9361 ms 4.9376 ms] | |
| thrpt: [414.82 MiB/s 414.94 MiB/s 415.07 MiB/s] | |
| change: | |
| time: [-3.1345% -3.0961% -3.0608%] (p = 0.00 < 0.05) | |
| thrpt: [+3.1574% +3.1950% +3.2359%] | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 5 (5.00%) high mild | |
| Benchmarking write_batch primitive/4096 values string #2 | |
| Benchmarking write_batch primitive/4096 values string #2: Warming up for 3.0000 s | |
| Benchmarking write_batch primitive/4096 values string #2: Collecting 100 samples in estimated 5.6332 s (15k iterations) | |
| Benchmarking write_batch primitive/4096 values string #2: Analyzing | |
| write_batch primitive/4096 values string #2 | |
| time: [370.86 µs 371.15 µs 371.43 µs] | |
| thrpt: [339.78 MiB/s 340.04 MiB/s 340.30 MiB/s] | |
| change: | |
| time: [-17.025% -16.960% -16.898%] (p = 0.00 < 0.05) | |
| thrpt: [+20.334% +20.424% +20.518%] | |
| Performance has improved. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) low mild | |
| Benchmarking write_batch primitive/4096 values string with bloom filter #2 | |
| Benchmarking write_batch primitive/4096 values string with bloom filter #2: Warming up for 3.0000 s | |
| Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 9.7s, enable flat sampling, or reduce sample count to 50. | |
| Benchmarking write_batch primitive/4096 values string with bloom filter #2: Collecting 100 samples in estimated 9.7014 s (5050 iterations) | |
| Benchmarking write_batch primitive/4096 values string with bloom filter #2: Analyzing | |
| write_batch primitive/4096 values string with bloom filter #2 | |
| time: [1.9160 ms 1.9176 ms 1.9203 ms] | |
| thrpt: [65.722 MiB/s 65.814 MiB/s 65.869 MiB/s] | |
| change: | |
| time: [-3.8267% -3.7725% -3.7068%] (p = 0.00 < 0.05) | |
| thrpt: [+3.8495% +3.9203% +3.9790%] | |
| Performance has improved. | |
| Found 7 outliers among 100 measurements (7.00%) | |
| 1 (1.00%) low mild | |
| 4 (4.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking write_batch primitive/4096 values string dictionary | |
| Benchmarking write_batch primitive/4096 values string dictionary: Warming up for 3.0000 s | |
| Benchmarking write_batch primitive/4096 values string dictionary: Collecting 100 samples in estimated 7.2361 s (10k iterations) | |
| Benchmarking write_batch primitive/4096 values string dictionary: Analyzing | |
| write_batch primitive/4096 values string dictionary | |
| time: [714.64 µs 714.87 µs 715.11 µs] | |
| thrpt: [1.4096 GiB/s 1.4101 GiB/s 1.4105 GiB/s] | |
| change: | |
| time: [-4.7085% -4.6410% -4.5651%] (p = 0.00 < 0.05) | |
| thrpt: [+4.7835% +4.8669% +4.9412%] | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 3 (3.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking write_batch primitive/4096 values string dictionary with bloom filter | |
| Benchmarking write_batch primitive/4096 values string dictionary with bloom filter: Warming up for 3.0000 s | |
| Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 7.9s, enable flat sampling, or reduce sample count to 50. | |
| Benchmarking write_batch primitive/4096 values string dictionary with bloom filter: Collecting 100 samples in estimated 7.9295 s (5050 iterations) | |
| Benchmarking write_batch primitive/4096 values string dictionary with bloom filter: Analyzing | |
| write_batch primitive/4096 values string dictionary with bloom filter | |
| time: [1.5684 ms 1.5727 ms 1.5789 ms] | |
| thrpt: [653.76 MiB/s 656.36 MiB/s 658.15 MiB/s] | |
| change: | |
| time: [-2.3752% -2.1633% -1.9034%] (p = 0.00 < 0.05) | |
| thrpt: [+1.9403% +2.2112% +2.4330%] | |
| Performance has improved. | |
| Found 6 outliers among 100 measurements (6.00%) | |
| 2 (2.00%) high mild | |
| 4 (4.00%) high severe | |
| Benchmarking write_batch primitive/4096 values string non-null | |
| Benchmarking write_batch primitive/4096 values string non-null: Warming up for 3.0000 s | |
| Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 9.3s, enable flat sampling, or reduce sample count to 50. | |
| Benchmarking write_batch primitive/4096 values string non-null: Collecting 100 samples in estimated 9.2979 s (5050 iterations) | |
| Benchmarking write_batch primitive/4096 values string non-null: Analyzing | |
| write_batch primitive/4096 values string non-null | |
| time: [1.8352 ms 1.8371 ms 1.8399 ms] | |
| thrpt: [1.0866 GiB/s 1.0883 GiB/s 1.0894 GiB/s] | |
| change: | |
| time: [-6.5277% -6.4149% -6.2836%] (p = 0.00 < 0.05) | |
| thrpt: [+6.7049% +6.8547% +6.9836%] | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 1 (1.00%) high mild | |
| 3 (3.00%) high severe | |
| Benchmarking write_batch primitive/4096 values string non-null with bloom filter | |
| Benchmarking write_batch primitive/4096 values string non-null with bloom filter: Warming up for 3.0000 s | |
| Benchmarking write_batch primitive/4096 values string non-null with bloom filter: Collecting 100 samples in estimated 5.1242 s (900 iterations) | |
| Benchmarking write_batch primitive/4096 values string non-null with bloom filter: Analyzing | |
| write_batch primitive/4096 values string non-null with bloom filter | |
| time: [5.6930 ms 5.6948 ms 5.6967 ms] | |
| thrpt: [359.37 MiB/s 359.49 MiB/s 359.60 MiB/s] | |
| change: | |
| time: [-3.1342% -3.0835% -3.0351%] (p = 0.00 < 0.05) | |
| thrpt: [+3.1301% +3.1816% +3.2356%] | |
| Performance has improved. | |
| Found 11 outliers among 100 measurements (11.00%) | |
| 9 (9.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking write_batch nested/4096 values primitive list | |
| Benchmarking write_batch nested/4096 values primitive list: Warming up for 3.0000 s | |
| Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 10.0s, enable flat sampling, or reduce sample count to 40. | |
| Benchmarking write_batch nested/4096 values primitive list: Collecting 100 samples in estimated 9.9972 s (5050 iterations) | |
| Benchmarking write_batch nested/4096 values primitive list: Analyzing | |
| write_batch nested/4096 values primitive list | |
| time: [1.9764 ms 1.9767 ms 1.9771 ms] | |
| thrpt: [1.0531 GiB/s 1.0533 GiB/s 1.0534 GiB/s] | |
| change: | |
| time: [-5.1452% -5.1139% -5.0824%] (p = 0.00 < 0.05) | |
| thrpt: [+5.3545% +5.3895% +5.4242%] | |
| Performance has improved. | |
| Found 4 outliers among 100 measurements (4.00%) | |
| 1 (1.00%) low mild | |
| 1 (1.00%) high mild | |
| 2 (2.00%) high severe | |
| Benchmarking write_batch nested/4096 values primitive list non-null | |
| Benchmarking write_batch nested/4096 values primitive list non-null: Warming up for 3.0000 s | |
| Benchmarking write_batch nested/4096 values primitive list non-null: Collecting 100 samples in estimated 5.0861 s (2000 iterations) | |
| Benchmarking write_batch nested/4096 values primitive list non-null: Analyzing | |
| write_batch nested/4096 values primitive list non-null | |
| time: [2.5405 ms 2.5410 ms 2.5416 ms] | |
| thrpt: [837.06 MiB/s 837.26 MiB/s 837.45 MiB/s] | |
| change: | |
| time: [-5.3638% -5.3355% -5.3099%] (p = 0.00 < 0.05) | |
| thrpt: [+5.6077% +5.6362% +5.6678%] | |
| Performance has improved. | |
| Found 5 outliers among 100 measurements (5.00%) | |
| 4 (4.00%) high mild | |
| 1 (1.00%) high severe | |
| Running benches/metadata.rs (target/x86_64-unknown-linux-gnu/release/deps/metadata-457613142f835056) | |
| Benchmarking open(default) | |
| Benchmarking open(default): Warming up for 3.0000 s | |
| Benchmarking open(default): Collecting 100 samples in estimated 5.0660 s (273k iterations) | |
| Benchmarking open(default): Analyzing | |
| open(default) time: [18.380 µs 18.384 µs 18.389 µs] | |
| change: [-8.3813% -8.0597% -7.7366%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 13 outliers among 100 measurements (13.00%) | |
| 2 (2.00%) high mild | |
| 11 (11.00%) high severe | |
| Benchmarking open(page index) | |
| Benchmarking open(page index): Warming up for 3.0000 s | |
| Benchmarking open(page index): Collecting 100 samples in estimated 7.3997 s (10k iterations) | |
| Benchmarking open(page index): Analyzing | |
| open(page index) time: [736.84 µs 737.86 µs 739.43 µs] | |
| change: [-23.659% -23.598% -23.496%] (p = 0.00 < 0.05) | |
| Performance has improved. | |
| Found 1 outliers among 100 measurements (1.00%) | |
| 1 (1.00%) high severe |
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