Skip to content

Instantly share code, notes, and snippets.

@lordpretzel

lordpretzel/>.txt

Created June 11, 2020 20:19
Show Gist options
  • Save lordpretzel/4baa399d79dbd183a0645866e4e74ac6 to your computer and use it in GitHub Desktop.
Save lordpretzel/4baa399d79dbd183a0645866e4e74ac6 to your computer and use it in GitHub Desktop.
Download ZIP
Raw
>.txt
[info] Compiling 1 Scala source to /Users/lord_pretzel/Documents/workspace/mimir-caveats/target/scala-2.12/test-classes ...
[info] Done compiling.
[success] Total time: 1 s, completed Jun 11, 2020, 3:17:21 PM
sbt:mimir-caveats> testOnly org.mimirdb.caveats.LogicalPlanRangeSpec -- ex "certain inputs.aggregation - no group-by - aggregtion functions only"
[info] LogicalPlanRangeSpec
[info] DataFrame Range Annotations
[info] Certain inputs
WARNING: An illegal reflective access operation has occurred
WARNING: Illegal reflective access by org.apache.spark.unsafe.Platform (file:/Users/lord_pretzel/Library/Caches/Coursier/v1/https/repo1.maven.org/maven2/org/apache/spark/spark-unsafe_2.12/3.0.0-preview2/spark-unsafe_2.12-3.0.0-preview2.jar) to constructor java.nio.DirectByteBuffer(long,int)
WARNING: Please consider reporting this to the maintainers of org.apache.spark.unsafe.Platform
WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations
WARNING: All illegal access operations will be denied in a future release
REWRITING PLAN OPERATOR: Project [X#824]
+- Aggregate [avg(cast(A#14 as double)) AS X#824]
+- RelationV2[A#14, B#15, C#16] csv file:/Users/lord_pretzel/Documents/workspace/mimir-caveats/test_data/r.csv
REWRITING PLAN OPERATOR: Aggregate [avg(cast(A#14 as double)) AS X#824]
+- RelationV2[A#14, B#15, C#16] csv file:/Users/lord_pretzel/Documents/workspace/mimir-caveats/test_data/r.csv
REWRITING PLAN OPERATOR: RelationV2[A#14, B#15, C#16] csv file:/Users/lord_pretzel/Documents/workspace/mimir-caveats/test_data/r.csv
========================================
REWRITE OPERATOR TYPE LEAF NODE
========================================
--------------------------
REWRITTEN OPERATOR:
--------------------------
'Project [A#14, B#15, C#16, 1 AS __CAVEATS_ROW_LB#830, 1 AS __CAVEATS_ROW_BG#831, 1 AS __CAVEATS_ROW_UB#832, 'A AS __CAVEATS_A_LB#833, 'A AS __CAVEATS_A_UB#834, 'B AS __CAVEATS_B_LB#835, 'B AS __CAVEATS_B_UB#836, 'C AS __CAVEATS_C_LB#837, 'C AS __CAVEATS_C_UB#838]
+- RelationV2[A#14, B#15, C#16] csv file:/Users/lord_pretzel/Documents/workspace/mimir-caveats/test_data/r.csv
----------------------------------------
EXPR: avg(cast(A#14 as double)) AS X#824
GB: None
trace: true
----------------------------------------
EXPR: avg(cast(A#14 as double))
GB: None
trace: true
----------------------------------------
EXPR: sum(cast(A#14 as double))
GB: None
trace: true
===========> BG EQUALS: true
GROUP BY: None
----------------------------------------
EXPR: cast(A#14 as double)
GB: None
trace: true
----------------------------------------
EXPR: A#14
GB: None
trace: true
sum(CASE WHEN (`__CAVEATS_ROW_LB` > 0) THEN (CAST(`__CAVEATS_A_LB` AS DOUBLE) * CASE WHEN (CAST(`__CAVEATS_A_LB` AS DOUBLE) < 0) THEN `__CAVEATS_ROW_UB` ELSE `__CAVEATS_ROW_LB` END) ELSE least(0.0D, (CAST(`__CAVEATS_A_LB` AS DOUBLE) * CASE WHEN (CAST(`__CAVEATS_A_LB` AS DOUBLE) < 0) THEN `__CAVEATS_ROW_UB` ELSE `__CAVEATS_ROW_LB` END)) END)
sum(CASE WHEN true THEN (CAST(`A` AS DOUBLE) * `__CAVEATS_ROW_BG`) ELSE 0.0D END)
sum(CASE WHEN (`__CAVEATS_ROW_LB` > 0) THEN (CAST(`__CAVEATS_A_UB` AS DOUBLE) * CASE WHEN (CAST(`__CAVEATS_A_UB` AS DOUBLE) > 0) THEN `__CAVEATS_ROW_UB` ELSE `__CAVEATS_ROW_LB` END) ELSE greatest(0.0D, (CAST(`__CAVEATS_A_UB` AS DOUBLE) * CASE WHEN (CAST(`__CAVEATS_A_UB` AS DOUBLE) > 0) THEN `__CAVEATS_ROW_UB` ELSE `__CAVEATS_ROW_LB` END)) END)
----------------------------------------
EXPR: count(1)
GB: None
trace: true
===========> BG EQUALS: true
GROUP BY: None
--------------------------
REWRITTEN OPERATOR:
--------------------------
'Aggregate [CASE WHEN (sum('__CAVEATS_ROW_BG) = 0) THEN 0.0 ELSE (sum(CASE WHEN true THEN (cast('A as double) * '__CAVEATS_ROW_BG) ELSE 0.0 END) / cast(sum('__CAVEATS_ROW_BG) as double)) END AS X#843, 1 AS __CAVEATS_ROW_LB#839, 1 AS __CAVEATS_ROW_BG#840, 1 AS __CAVEATS_ROW_UB#841, CASE WHEN (sum(CASE WHEN ('__CAVEATS_ROW_LB > 0) THEN '__CAVEATS_ROW_LB ELSE least(0, '__CAVEATS_ROW_LB) END) = 0) THEN 0.0 ELSE (sum(CASE WHEN ('__CAVEATS_ROW_LB > 0) THEN (cast('__CAVEATS_A_LB as double) * CASE WHEN (cast('__CAVEATS_A_LB as double) < 0) THEN '__CAVEATS_ROW_UB ELSE '__CAVEATS_ROW_LB END) ELSE least(0.0, (cast('__CAVEATS_A_LB as double) * CASE WHEN (cast('__CAVEATS_A_LB as double) < 0) THEN '__CAVEATS_ROW_UB ELSE '__CAVEATS_ROW_LB END)) END) / cast(sum(CASE WHEN ('__CAVEATS_ROW_LB > 0) THEN '__CAVEATS_ROW_LB ELSE least(0, '__CAVEATS_ROW_LB) END) as double)) END AS __CAVEATS_X_LB#842, CASE WHEN (sum('__CAVEATS_ROW_UB) = 0) THEN 0.0 ELSE (sum(CASE WHEN ('__CAVEATS_ROW_LB > 0) THEN (cast('__CAVEATS_A_UB as double) * CASE WHEN (cast('__CAVEATS_A_UB as double) > 0) THEN '__CAVEATS_ROW_UB ELSE '__CAVEATS_ROW_LB END) ELSE greatest(0.0, (cast('__CAVEATS_A_UB as double) * CASE WHEN (cast('__CAVEATS_A_UB as double) > 0) THEN '__CAVEATS_ROW_UB ELSE '__CAVEATS_ROW_LB END)) END) / cast(sum('__CAVEATS_ROW_UB) as double)) END AS __CAVEATS_X_UB#844]
+- 'Project [A#14, B#15, C#16, 1 AS __CAVEATS_ROW_LB#830, 1 AS __CAVEATS_ROW_BG#831, 1 AS __CAVEATS_ROW_UB#832, 'A AS __CAVEATS_A_LB#833, 'A AS __CAVEATS_A_UB#834, 'B AS __CAVEATS_B_LB#835, 'B AS __CAVEATS_B_UB#836, 'C AS __CAVEATS_C_LB#837, 'C AS __CAVEATS_C_UB#838]
+- RelationV2[A#14, B#15, C#16] csv file:/Users/lord_pretzel/Documents/workspace/mimir-caveats/test_data/r.csv
----------------------------------------
EXPR: X#824
GB: None
trace: true
bestGuess: ArrayBuffer('X AS X#845)
========================================
REWRITE OPERATOR TYPE PROJECT
========================================
--------------------------
REWRITTEN OPERATOR:
--------------------------
'Project ['X AS X#845, '__CAVEATS_ROW_LB AS __CAVEATS_ROW_LB#846, '__CAVEATS_ROW_BG AS __CAVEATS_ROW_BG#847, '__CAVEATS_ROW_UB AS __CAVEATS_ROW_UB#848, '__CAVEATS_X_LB AS __CAVEATS_X_LB#849, '__CAVEATS_X_UB AS __CAVEATS_X_UB#850]
+- 'Aggregate [CASE WHEN (sum('__CAVEATS_ROW_BG) = 0) THEN 0.0 ELSE (sum(CASE WHEN true THEN (cast('A as double) * '__CAVEATS_ROW_BG) ELSE 0.0 END) / cast(sum('__CAVEATS_ROW_BG) as double)) END AS X#843, 1 AS __CAVEATS_ROW_LB#839, 1 AS __CAVEATS_ROW_BG#840, 1 AS __CAVEATS_ROW_UB#841, CASE WHEN (sum(CASE WHEN ('__CAVEATS_ROW_LB > 0) THEN '__CAVEATS_ROW_LB ELSE least(0, '__CAVEATS_ROW_LB) END) = 0) THEN 0.0 ELSE (sum(CASE WHEN ('__CAVEATS_ROW_LB > 0) THEN (cast('__CAVEATS_A_LB as double) * CASE WHEN (cast('__CAVEATS_A_LB as double) < 0) THEN '__CAVEATS_ROW_UB ELSE '__CAVEATS_ROW_LB END) ELSE least(0.0, (cast('__CAVEATS_A_LB as double) * CASE WHEN (cast('__CAVEATS_A_LB as double) < 0) THEN '__CAVEATS_ROW_UB ELSE '__CAVEATS_ROW_LB END)) END) / cast(sum(CASE WHEN ('__CAVEATS_ROW_LB > 0) THEN '__CAVEATS_ROW_LB ELSE least(0, '__CAVEATS_ROW_LB) END) as double)) END AS __CAVEATS_X_LB#842, CASE WHEN (sum('__CAVEATS_ROW_UB) = 0) THEN 0.0 ELSE (sum(CASE WHEN ('__CAVEATS_ROW_LB > 0) THEN (cast('__CAVEATS_A_UB as double) * CASE WHEN (cast('__CAVEATS_A_UB as double) > 0) THEN '__CAVEATS_ROW_UB ELSE '__CAVEATS_ROW_LB END) ELSE greatest(0.0, (cast('__CAVEATS_A_UB as double) * CASE WHEN (cast('__CAVEATS_A_UB as double) > 0) THEN '__CAVEATS_ROW_UB ELSE '__CAVEATS_ROW_LB END)) END) / cast(sum('__CAVEATS_ROW_UB) as double)) END AS __CAVEATS_X_UB#844]
+- 'Project [A#14, B#15, C#16, 1 AS __CAVEATS_ROW_LB#830, 1 AS __CAVEATS_ROW_BG#831, 1 AS __CAVEATS_ROW_UB#832, 'A AS __CAVEATS_A_LB#833, 'A AS __CAVEATS_A_UB#834, 'B AS __CAVEATS_B_LB#835, 'B AS __CAVEATS_B_UB#836, 'C AS __CAVEATS_C_LB#837, 'C AS __CAVEATS_C_UB#838]
+- RelationV2[A#14, B#15, C#16] csv file:/Users/lord_pretzel/Documents/workspace/mimir-caveats/test_data/r.csv
is already annotated? false
base schema: StructType(StructField(X,DoubleType,true))
row encoder StructType(StructField(X,DoubleType,true), StructField(__CAVEATS_ROW_LB,IntegerType,false), StructField(__CAVEATS_ROW_BG,IntegerType,false), StructField(__CAVEATS_ROW_UB,IntegerType,false), StructField(__CAVEATS_X_LB,DoubleType,true), StructField(__CAVEATS_X_UB,DoubleType,true))
================================================================================
FINAL
================================================================================
============================== QUERY EXECUTION (PLANS) ==============================
== Parsed Logical Plan ==
'Project ['X AS X#845, '__CAVEATS_ROW_LB AS __CAVEATS_ROW_LB#846, '__CAVEATS_ROW_BG AS __CAVEATS_ROW_BG#847, '__CAVEATS_ROW_UB AS __CAVEATS_ROW_UB#848, '__CAVEATS_X_LB AS __CAVEATS_X_LB#849, '__CAVEATS_X_UB AS __CAVEATS_X_UB#850]
+- 'Aggregate [CASE WHEN (sum('__CAVEATS_ROW_BG) = 0) THEN 0.0 ELSE (sum(CASE WHEN true THEN (cast('A as double) * '__CAVEATS_ROW_BG) ELSE 0.0 END) / cast(sum('__CAVEATS_ROW_BG) as double)) END AS X#843, 1 AS __CAVEATS_ROW_LB#839, 1 AS __CAVEATS_ROW_BG#840, 1 AS __CAVEATS_ROW_UB#841, CASE WHEN (sum(CASE WHEN ('__CAVEATS_ROW_LB > 0) THEN '__CAVEATS_ROW_LB ELSE least(0, '__CAVEATS_ROW_LB) END) = 0) THEN 0.0 ELSE (sum(CASE WHEN ('__CAVEATS_ROW_LB > 0) THEN (cast('__CAVEATS_A_LB as double) * CASE WHEN (cast('__CAVEATS_A_LB as double) < 0) THEN '__CAVEATS_ROW_UB ELSE '__CAVEATS_ROW_LB END) ELSE least(0.0, (cast('__CAVEATS_A_LB as double) * CASE WHEN (cast('__CAVEATS_A_LB as double) < 0) THEN '__CAVEATS_ROW_UB ELSE '__CAVEATS_ROW_LB END)) END) / cast(sum(CASE WHEN ('__CAVEATS_ROW_LB > 0) THEN '__CAVEATS_ROW_LB ELSE least(0, '__CAVEATS_ROW_LB) END) as double)) END AS __CAVEATS_X_LB#842, CASE WHEN (sum('__CAVEATS_ROW_UB) = 0) THEN 0.0 ELSE (sum(CASE WHEN ('__CAVEATS_ROW_LB > 0) THEN (cast('__CAVEATS_A_UB as double) * CASE WHEN (cast('__CAVEATS_A_UB as double) > 0) THEN '__CAVEATS_ROW_UB ELSE '__CAVEATS_ROW_LB END) ELSE greatest(0.0, (cast('__CAVEATS_A_UB as double) * CASE WHEN (cast('__CAVEATS_A_UB as double) > 0) THEN '__CAVEATS_ROW_UB ELSE '__CAVEATS_ROW_LB END)) END) / cast(sum('__CAVEATS_ROW_UB) as double)) END AS __CAVEATS_X_UB#844]
+- 'Project [A#14, B#15, C#16, 1 AS __CAVEATS_ROW_LB#830, 1 AS __CAVEATS_ROW_BG#831, 1 AS __CAVEATS_ROW_UB#832, 'A AS __CAVEATS_A_LB#833, 'A AS __CAVEATS_A_UB#834, 'B AS __CAVEATS_B_LB#835, 'B AS __CAVEATS_B_UB#836, 'C AS __CAVEATS_C_LB#837, 'C AS __CAVEATS_C_UB#838]
+- RelationV2[A#14, B#15, C#16] csv file:/Users/lord_pretzel/Documents/workspace/mimir-caveats/test_data/r.csv
== Analyzed Logical Plan ==
X: double, __CAVEATS_ROW_LB: int, __CAVEATS_ROW_BG: int, __CAVEATS_ROW_UB: int, __CAVEATS_X_LB: double, __CAVEATS_X_UB: double
Project [X#843 AS X#845, __CAVEATS_ROW_LB#839 AS __CAVEATS_ROW_LB#846, __CAVEATS_ROW_BG#840 AS __CAVEATS_ROW_BG#847, __CAVEATS_ROW_UB#841 AS __CAVEATS_ROW_UB#848, __CAVEATS_X_LB#842 AS __CAVEATS_X_LB#849, __CAVEATS_X_UB#844 AS __CAVEATS_X_UB#850]
+- Aggregate [CASE WHEN (sum(cast(__CAVEATS_ROW_BG#831 as bigint)) = cast(0 as bigint)) THEN 0.0 ELSE (sum(CASE WHEN true THEN (cast(A#14 as double) * cast(__CAVEATS_ROW_BG#831 as double)) ELSE 0.0 END) / cast(sum(cast(__CAVEATS_ROW_BG#831 as bigint)) as double)) END AS X#843, 1 AS __CAVEATS_ROW_LB#839, 1 AS __CAVEATS_ROW_BG#840, 1 AS __CAVEATS_ROW_UB#841, CASE WHEN (sum(cast(CASE WHEN (__CAVEATS_ROW_LB#830 > 0) THEN __CAVEATS_ROW_LB#830 ELSE least(0, __CAVEATS_ROW_LB#830) END as bigint)) = cast(0 as bigint)) THEN 0.0 ELSE (sum(CASE WHEN (__CAVEATS_ROW_LB#830 > 0) THEN (cast(__CAVEATS_A_LB#833 as double) * cast(CASE WHEN (cast(__CAVEATS_A_LB#833 as double) < cast(0 as double)) THEN __CAVEATS_ROW_UB#832 ELSE __CAVEATS_ROW_LB#830 END as double)) ELSE least(0.0, (cast(__CAVEATS_A_LB#833 as double) * cast(CASE WHEN (cast(__CAVEATS_A_LB#833 as double) < cast(0 as double)) THEN __CAVEATS_ROW_UB#832 ELSE __CAVEATS_ROW_LB#830 END as double))) END) / cast(sum(cast(CASE WHEN (__CAVEATS_ROW_LB#830 > 0) THEN __CAVEATS_ROW_LB#830 ELSE least(0, __CAVEATS_ROW_LB#830) END as bigint)) as double)) END AS __CAVEATS_X_LB#842, CASE WHEN (sum(cast(__CAVEATS_ROW_UB#832 as bigint)) = cast(0 as bigint)) THEN 0.0 ELSE (sum(CASE WHEN (__CAVEATS_ROW_LB#830 > 0) THEN (cast(__CAVEATS_A_UB#834 as double) * cast(CASE WHEN (cast(__CAVEATS_A_UB#834 as double) > cast(0 as double)) THEN __CAVEATS_ROW_UB#832 ELSE __CAVEATS_ROW_LB#830 END as double)) ELSE greatest(0.0, (cast(__CAVEATS_A_UB#834 as double) * cast(CASE WHEN (cast(__CAVEATS_A_UB#834 as double) > cast(0 as double)) THEN __CAVEATS_ROW_UB#832 ELSE __CAVEATS_ROW_LB#830 END as double))) END) / cast(sum(cast(__CAVEATS_ROW_UB#832 as bigint)) as double)) END AS __CAVEATS_X_UB#844]
+- Project [A#14, B#15, C#16, 1 AS __CAVEATS_ROW_LB#830, 1 AS __CAVEATS_ROW_BG#831, 1 AS __CAVEATS_ROW_UB#832, A#14 AS __CAVEATS_A_LB#833, A#14 AS __CAVEATS_A_UB#834, B#15 AS __CAVEATS_B_LB#835, B#15 AS __CAVEATS_B_UB#836, C#16 AS __CAVEATS_C_LB#837, C#16 AS __CAVEATS_C_UB#838]
+- RelationV2[A#14, B#15, C#16] csv file:/Users/lord_pretzel/Documents/workspace/mimir-caveats/test_data/r.csv
== Optimized Logical Plan ==
Aggregate [CASE WHEN (sum(1) = 0) THEN 0.0 ELSE (sum((cast(A#14 as double) * 1.0)) / cast(sum(1) as double)) END AS X#845, 1 AS __CAVEATS_ROW_LB#846, 1 AS __CAVEATS_ROW_BG#847, 1 AS __CAVEATS_ROW_UB#848, CASE WHEN (sum(1) = 0) THEN 0.0 ELSE (sum((cast(__CAVEATS_A_LB#833 as double) * 1.0)) / cast(sum(1) as double)) END AS __CAVEATS_X_LB#849, CASE WHEN (sum(1) = 0) THEN 0.0 ELSE (sum((cast(__CAVEATS_A_UB#834 as double) * 1.0)) / cast(sum(1) as double)) END AS __CAVEATS_X_UB#850]
+- Project [A#14, A#14 AS __CAVEATS_A_LB#833, A#14 AS __CAVEATS_A_UB#834]
+- RelationV2[A#14] csv file:/Users/lord_pretzel/Documents/workspace/mimir-caveats/test_data/r.csv
== Physical Plan ==
*(1) HashAggregate(keys=[], functions=[sum(1), sum((cast(A#14 as double) * 1.0)), sum((cast(__CAVEATS_A_LB#833 as double) * 1.0)), sum((cast(__CAVEATS_A_UB#834 as double) * 1.0))], output=[X#845, __CAVEATS_ROW_LB#846, __CAVEATS_ROW_BG#847, __CAVEATS_ROW_UB#848, __CAVEATS_X_LB#849, __CAVEATS_X_UB#850])
+- *(1) HashAggregate(keys=[], functions=[partial_sum(1), partial_sum((cast(A#14 as double) * 1.0)), partial_sum((cast(__CAVEATS_A_LB#833 as double) * 1.0)), partial_sum((cast(__CAVEATS_A_UB#834 as double) * 1.0))], output=[sum#862L, sum#863, sum#864, sum#865])
+- *(1) Project [A#14, A#14 AS __CAVEATS_A_LB#833, A#14 AS __CAVEATS_A_UB#834]
+- BatchScan[A#14] CSVScan Location: InMemoryFileIndex[file:/Users/lord_pretzel/Documents/workspace/mimir-caveats/test_data/r.csv], ReadSchema: struct<A:string>
============================== SCHEMA ==============================
StructType(StructField(X,DoubleType,true), StructField(__CAVEATS_ROW_LB,IntegerType,false), StructField(__CAVEATS_ROW_BG,IntegerType,false), StructField(__CAVEATS_ROW_UB,IntegerType,false), StructField(__CAVEATS_X_LB,DoubleType,true), StructField(__CAVEATS_X_UB,DoubleType,true))
============================== RESULT ==============================
+---+----------------+----------------+----------------+--------------+--------------+
| X|__CAVEATS_ROW_LB|__CAVEATS_ROW_BG|__CAVEATS_ROW_UB|__CAVEATS_X_LB|__CAVEATS_X_UB|
+---+----------------+----------------+----------------+--------------+--------------+
|1.0| 1| 1| 1| 1.0| 1.0|
+---+----------------+----------------+----------------+--------------+--------------+
================================================================================
QUERY
================================================================================
Project [X#843 AS X#845, __CAVEATS_ROW_LB#839 AS __CAVEATS_ROW_LB#846, __CAVEATS_ROW_BG#840 AS __CAVEATS_ROW_BG#847, __CAVEATS_ROW_UB#841 AS __CAVEATS_ROW_UB#848, __CAVEATS_X_LB#842 AS __CAVEATS_X_LB#849, __CAVEATS_X_UB#844 AS __CAVEATS_X_UB#850]
+- Aggregate [CASE WHEN (sum(cast(__CAVEATS_ROW_BG#831 as bigint)) = cast(0 as bigint)) THEN 0.0 ELSE (sum(CASE WHEN true THEN (cast(A#14 as double) * cast(__CAVEATS_ROW_BG#831 as double)) ELSE 0.0 END) / cast(sum(cast(__CAVEATS_ROW_BG#831 as bigint)) as double)) END AS X#843, 1 AS __CAVEATS_ROW_LB#839, 1 AS __CAVEATS_ROW_BG#840, 1 AS __CAVEATS_ROW_UB#841, CASE WHEN (sum(cast(CASE WHEN (__CAVEATS_ROW_LB#830 > 0) THEN __CAVEATS_ROW_LB#830 ELSE least(0, __CAVEATS_ROW_LB#830) END as bigint)) = cast(0 as bigint)) THEN 0.0 ELSE (sum(CASE WHEN (__CAVEATS_ROW_LB#830 > 0) THEN (cast(__CAVEATS_A_LB#833 as double) * cast(CASE WHEN (cast(__CAVEATS_A_LB#833 as double) < cast(0 as double)) THEN __CAVEATS_ROW_UB#832 ELSE __CAVEATS_ROW_LB#830 END as double)) ELSE least(0.0, (cast(__CAVEATS_A_LB#833 as double) * cast(CASE WHEN (cast(__CAVEATS_A_LB#833 as double) < cast(0 as double)) THEN __CAVEATS_ROW_UB#832 ELSE __CAVEATS_ROW_LB#830 END as double))) END) / cast(sum(cast(CASE WHEN (__CAVEATS_ROW_LB#830 > 0) THEN __CAVEATS_ROW_LB#830 ELSE least(0, __CAVEATS_ROW_LB#830) END as bigint)) as double)) END AS __CAVEATS_X_LB#842, CASE WHEN (sum(cast(__CAVEATS_ROW_UB#832 as bigint)) = cast(0 as bigint)) THEN 0.0 ELSE (sum(CASE WHEN (__CAVEATS_ROW_LB#830 > 0) THEN (cast(__CAVEATS_A_UB#834 as double) * cast(CASE WHEN (cast(__CAVEATS_A_UB#834 as double) > cast(0 as double)) THEN __CAVEATS_ROW_UB#832 ELSE __CAVEATS_ROW_LB#830 END as double)) ELSE greatest(0.0, (cast(__CAVEATS_A_UB#834 as double) * cast(CASE WHEN (cast(__CAVEATS_A_UB#834 as double) > cast(0 as double)) THEN __CAVEATS_ROW_UB#832 ELSE __CAVEATS_ROW_LB#830 END as double))) END) / cast(sum(cast(__CAVEATS_ROW_UB#832 as bigint)) as double)) END AS __CAVEATS_X_UB#844]
+- Project [A#14, B#15, C#16, 1 AS __CAVEATS_ROW_LB#830, 1 AS __CAVEATS_ROW_BG#831, 1 AS __CAVEATS_ROW_UB#832, A#14 AS __CAVEATS_A_LB#833, A#14 AS __CAVEATS_A_UB#834, B#15 AS __CAVEATS_B_LB#835, B#15 AS __CAVEATS_B_UB#836, C#16 AS __CAVEATS_C_LB#837, C#16 AS __CAVEATS_C_UB#838]
+- RelationV2[A#14, B#15, C#16] csv file:/Users/lord_pretzel/Documents/workspace/mimir-caveats/test_data/r.csv
================================================================================
RESULT
================================================================================
+---+----------------+----------------+----------------+--------------+--------------+
| X|__CAVEATS_ROW_LB|__CAVEATS_ROW_BG|__CAVEATS_ROW_UB|__CAVEATS_X_LB|__CAVEATS_X_UB|
+---+----------------+----------------+----------------+--------------+--------------+
|1.0| 1| 1| 1| 1.0| 1.0|
+---+----------------+----------------+----------------+--------------+--------------+
================================================================================
QUERY
================================================================================
Aggregate [CASE WHEN (sum(cast(__CAVEATS_ROW_BG#831 as bigint)) = cast(0 as bigint)) THEN 0.0 ELSE (sum(CASE WHEN true THEN (cast(A#14 as double) * cast(__CAVEATS_ROW_BG#831 as double)) ELSE 0.0 END) / cast(sum(cast(__CAVEATS_ROW_BG#831 as bigint)) as double)) END AS X#843, 1 AS __CAVEATS_ROW_LB#839, 1 AS __CAVEATS_ROW_BG#840, 1 AS __CAVEATS_ROW_UB#841, CASE WHEN (sum(cast(CASE WHEN (__CAVEATS_ROW_LB#830 > 0) THEN __CAVEATS_ROW_LB#830 ELSE least(0, __CAVEATS_ROW_LB#830) END as bigint)) = cast(0 as bigint)) THEN 0.0 ELSE (sum(CASE WHEN (__CAVEATS_ROW_LB#830 > 0) THEN (cast(__CAVEATS_A_LB#833 as double) * cast(CASE WHEN (cast(__CAVEATS_A_LB#833 as double) < cast(0 as double)) THEN __CAVEATS_ROW_UB#832 ELSE __CAVEATS_ROW_LB#830 END as double)) ELSE least(0.0, (cast(__CAVEATS_A_LB#833 as double) * cast(CASE WHEN (cast(__CAVEATS_A_LB#833 as double) < cast(0 as double)) THEN __CAVEATS_ROW_UB#832 ELSE __CAVEATS_ROW_LB#830 END as double))) END) / cast(sum(cast(CASE WHEN (__CAVEATS_ROW_LB#830 > 0) THEN __CAVEATS_ROW_LB#830 ELSE least(0, __CAVEATS_ROW_LB#830) END as bigint)) as double)) END AS __CAVEATS_X_LB#842, CASE WHEN (sum(cast(__CAVEATS_ROW_UB#832 as bigint)) = cast(0 as bigint)) THEN 0.0 ELSE (sum(CASE WHEN (__CAVEATS_ROW_LB#830 > 0) THEN (cast(__CAVEATS_A_UB#834 as double) * cast(CASE WHEN (cast(__CAVEATS_A_UB#834 as double) > cast(0 as double)) THEN __CAVEATS_ROW_UB#832 ELSE __CAVEATS_ROW_LB#830 END as double)) ELSE greatest(0.0, (cast(__CAVEATS_A_UB#834 as double) * cast(CASE WHEN (cast(__CAVEATS_A_UB#834 as double) > cast(0 as double)) THEN __CAVEATS_ROW_UB#832 ELSE __CAVEATS_ROW_LB#830 END as double))) END) / cast(sum(cast(__CAVEATS_ROW_UB#832 as bigint)) as double)) END AS __CAVEATS_X_UB#844]
+- Project [A#14, B#15, C#16, 1 AS __CAVEATS_ROW_LB#830, 1 AS __CAVEATS_ROW_BG#831, 1 AS __CAVEATS_ROW_UB#832, A#14 AS __CAVEATS_A_LB#833, A#14 AS __CAVEATS_A_UB#834, B#15 AS __CAVEATS_B_LB#835, B#15 AS __CAVEATS_B_UB#836, C#16 AS __CAVEATS_C_LB#837, C#16 AS __CAVEATS_C_UB#838]
+- RelationV2[A#14, B#15, C#16] csv file:/Users/lord_pretzel/Documents/workspace/mimir-caveats/test_data/r.csv
================================================================================
RESULT
================================================================================
+---+----------------+----------------+----------------+--------------+--------------+
| X|__CAVEATS_ROW_LB|__CAVEATS_ROW_BG|__CAVEATS_ROW_UB|__CAVEATS_X_LB|__CAVEATS_X_UB|
+---+----------------+----------------+----------------+--------------+--------------+
|1.0| 1| 1| 1| 1.0| 1.0|
+---+----------------+----------------+----------------+--------------+--------------+
================================================================================
QUERY
================================================================================
Project [A#14, B#15, C#16, 1 AS __CAVEATS_ROW_LB#830, 1 AS __CAVEATS_ROW_BG#831, 1 AS __CAVEATS_ROW_UB#832, A#14 AS __CAVEATS_A_LB#833, A#14 AS __CAVEATS_A_UB#834, B#15 AS __CAVEATS_B_LB#835, B#15 AS __CAVEATS_B_UB#836, C#16 AS __CAVEATS_C_LB#837, C#16 AS __CAVEATS_C_UB#838]
+- RelationV2[A#14, B#15, C#16] csv file:/Users/lord_pretzel/Documents/workspace/mimir-caveats/test_data/r.csv
================================================================================
RESULT
================================================================================
+---+----+----+----------------+----------------+----------------+--------------+--------------+--------------+--------------+--------------+--------------+
| A| B| C|__CAVEATS_ROW_LB|__CAVEATS_ROW_BG|__CAVEATS_ROW_UB|__CAVEATS_A_LB|__CAVEATS_A_UB|__CAVEATS_B_LB|__CAVEATS_B_UB|__CAVEATS_C_LB|__CAVEATS_C_UB|
+---+----+----+----------------+----------------+----------------+--------------+--------------+--------------+--------------+--------------+--------------+
| 1| 2| 3| 1| 1| 1| 1| 1| 2| 2| 3| 3|
| 1| 3| 1| 1| 1| 1| 1| 1| 3| 3| 1| 1|
| 2|null| 1| 1| 1| 1| 2| 2| null| null| 1| 1|
| 1| 2|null| 1| 1| 1| 1| 1| 2| 2| null| null|
| 1| 4| 2| 1| 1| 1| 1| 1| 4| 4| 2| 2|
| 2| 2| 1| 1| 1| 1| 2| 2| 2| 2| 1| 1|
| 4| 2| 4| 1| 1| 1| 4| 4| 2| 2| 4| 4|
+---+----+----+----------------+----------------+----------------+--------------+--------------+--------------+--------------+--------------+--------------+
================================================================================
QUERY
================================================================================
RelationV2[A#14, B#15, C#16] csv file:/Users/lord_pretzel/Documents/workspace/mimir-caveats/test_data/r.csv
================================================================================
RESULT
================================================================================
+---+----+----+
| A| B| C|
+---+----+----+
| 1| 2| 3|
| 1| 3| 1|
| 2|null| 1|
| 1| 2|null|
| 1| 4| 2|
| 2| 2| 1|
| 4| 2| 4|
+---+----+----+
Found 1 WholeStageCodegen subtrees.
== Subtree 1 / 1 (maxMethodCodeSize:636; maxConstantPoolSize:289(0.44% used); numInnerClasses:0) ==
*(1) HashAggregate(keys=[], functions=[sum(1), sum((cast(A#14 as double) * 1.0)), sum((cast(__CAVEATS_A_LB#833 as double) * 1.0)), sum((cast(__CAVEATS_A_UB#834 as double) * 1.0))], output=[X#845, __CAVEATS_ROW_LB#846, __CAVEATS_ROW_BG#847, __CAVEATS_ROW_UB#848, __CAVEATS_X_LB#849, __CAVEATS_X_UB#850])
+- *(1) HashAggregate(keys=[], functions=[partial_sum(1), partial_sum((cast(A#14 as double) * 1.0)), partial_sum((cast(__CAVEATS_A_LB#833 as double) * 1.0)), partial_sum((cast(__CAVEATS_A_UB#834 as double) * 1.0))], output=[sum#862L, sum#863, sum#864, sum#865])
+- *(1) Project [A#14, A#14 AS __CAVEATS_A_LB#833, A#14 AS __CAVEATS_A_UB#834]
+- BatchScan[A#14] CSVScan Location: InMemoryFileIndex[file:/Users/lord_pretzel/Documents/workspace/mimir-caveats/test_data/r.csv], ReadSchema: struct<A:string>
Generated code:
/* 001 */ public Object generate(Object[] references) {
/* 002 */ return new GeneratedIteratorForCodegenStage1(references);
/* 003 */ }
/* 004 */
/* 005 */ // codegenStageId=1
/* 006 */ final class GeneratedIteratorForCodegenStage1 extends org.apache.spark.sql.execution.BufferedRowIterator {
/* 007 */ private Object[] references;
/* 008 */ private scala.collection.Iterator[] inputs;
/* 009 */ private boolean agg_initAgg_0;
/* 010 */ private boolean agg_bufIsNull_0;
/* 011 */ private long agg_bufValue_0;
/* 012 */ private boolean agg_bufIsNull_1;
/* 013 */ private double agg_bufValue_1;
/* 014 */ private boolean agg_bufIsNull_2;
/* 015 */ private double agg_bufValue_2;
/* 016 */ private boolean agg_bufIsNull_3;
/* 017 */ private double agg_bufValue_3;
/* 018 */ private boolean agg_initAgg_1;
/* 019 */ private boolean agg_bufIsNull_4;
/* 020 */ private long agg_bufValue_4;
/* 021 */ private boolean agg_bufIsNull_5;
/* 022 */ private double agg_bufValue_5;
/* 023 */ private boolean agg_bufIsNull_6;
/* 024 */ private double agg_bufValue_6;
/* 025 */ private boolean agg_bufIsNull_7;
/* 026 */ private double agg_bufValue_7;
/* 027 */ private scala.collection.Iterator inputadapter_input_0;
/* 028 */ private boolean agg_agg_isNull_46_0;
/* 029 */ private boolean agg_agg_isNull_50_0;
/* 030 */ private boolean agg_agg_isNull_52_0;
/* 031 */ private boolean agg_agg_isNull_60_0;
/* 032 */ private boolean agg_agg_isNull_62_0;
/* 033 */ private boolean agg_agg_isNull_70_0;
/* 034 */ private boolean agg_agg_isNull_72_0;
/* 035 */ private boolean agg_agg_isNull_88_0;
/* 036 */ private boolean agg_agg_isNull_90_0;
/* 037 */ private boolean agg_agg_isNull_95_0;
/* 038 */ private boolean agg_agg_isNull_97_0;
/* 039 */ private boolean agg_agg_isNull_102_0;
/* 040 */ private boolean agg_agg_isNull_104_0;
/* 041 */ private boolean agg_agg_isNull_109_0;
/* 042 */ private boolean agg_agg_isNull_111_0;
/* 043 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[] project_mutableStateArray_0 = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[5];
/* 044 */
/* 045 */ public GeneratedIteratorForCodegenStage1(Object[] references) {
/* 046 */ this.references = references;
/* 047 */ }
/* 048 */
/* 049 */ public void init(int index, scala.collection.Iterator[] inputs) {
/* 050 */ partitionIndex = index;
/* 051 */ this.inputs = inputs;
/* 052 */
/* 053 */ inputadapter_input_0 = inputs[0];
/* 054 */ project_mutableStateArray_0[0] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(3, 96);
/* 055 */ project_mutableStateArray_0[1] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(3, 96);
/* 056 */ project_mutableStateArray_0[2] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(4, 0);
/* 057 */ project_mutableStateArray_0[3] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(4, 0);
/* 058 */ project_mutableStateArray_0[4] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(6, 0);
/* 059 */
/* 060 */ }
/* 061 */
/* 062 */ private void agg_doAggregate_sum_6(double agg_expr_2_1, boolean agg_exprIsNull_2_1) throws java.io.IOException {
/* 063 */ // do aggregate for sum
/* 064 */ // evaluate aggregate function
/* 065 */ agg_agg_isNull_102_0 = true;
/* 066 */ double agg_value_102 = -1.0;
/* 067 */ do {
/* 068 */ boolean agg_isNull_103 = true;
/* 069 */ double agg_value_103 = -1.0;
/* 070 */ agg_agg_isNull_104_0 = true;
/* 071 */ double agg_value_104 = -1.0;
/* 072 */ do {
/* 073 */ if (!agg_bufIsNull_2) {
/* 074 */ agg_agg_isNull_104_0 = false;
/* 075 */ agg_value_104 = agg_bufValue_2;
/* 076 */ continue;
/* 077 */ }
/* 078 */
/* 079 */ if (!false) {
/* 080 */ agg_agg_isNull_104_0 = false;
/* 081 */ agg_value_104 = 0.0D;
/* 082 */ continue;
/* 083 */ }
/* 084 */
/* 085 */ } while (false);
/* 086 */
/* 087 */ if (!agg_exprIsNull_2_1) {
/* 088 */ agg_isNull_103 = false; // resultCode could change nullability.
/* 089 */
/* 090 */ agg_value_103 = agg_value_104 + agg_expr_2_1;
/* 091 */
/* 092 */ }
/* 093 */ if (!agg_isNull_103) {
/* 094 */ agg_agg_isNull_102_0 = false;
/* 095 */ agg_value_102 = agg_value_103;
/* 096 */ continue;
/* 097 */ }
/* 098 */
/* 099 */ if (!agg_bufIsNull_2) {
/* 100 */ agg_agg_isNull_102_0 = false;
/* 101 */ agg_value_102 = agg_bufValue_2;
/* 102 */ continue;
/* 103 */ }
/* 104 */
/* 105 */ } while (false);
/* 106 */ // update aggregation buffers
/* 107 */ agg_bufIsNull_2 = agg_agg_isNull_102_0;
/* 108 */ agg_bufValue_2 = agg_value_102;
/* 109 */ }
/* 110 */
/* 111 */ private void agg_doAggregate_sum_0() throws java.io.IOException {
/* 112 */ // do aggregate for sum
/* 113 */ // evaluate aggregate function
/* 114 */ agg_agg_isNull_46_0 = true;
/* 115 */ long agg_value_46 = -1L;
/* 116 */ do {
/* 117 */ if (!agg_bufIsNull_4) {
/* 118 */ agg_agg_isNull_46_0 = false;
/* 119 */ agg_value_46 = agg_bufValue_4;
/* 120 */ continue;
/* 121 */ }
/* 122 */
/* 123 */ if (!false) {
/* 124 */ agg_agg_isNull_46_0 = false;
/* 125 */ agg_value_46 = 0L;
/* 126 */ continue;
/* 127 */ }
/* 128 */
/* 129 */ } while (false);
/* 130 */
/* 131 */ long agg_value_45 = -1L;
/* 132 */
/* 133 */ agg_value_45 = agg_value_46 + 1L;
/* 134 */ // update aggregation buffers
/* 135 */ agg_bufIsNull_4 = false;
/* 136 */ agg_bufValue_4 = agg_value_45;
/* 137 */ }
/* 138 */
/* 139 */ private void agg_doAggregate_sum_3(boolean agg_exprIsNull_2_0, org.apache.spark.unsafe.types.UTF8String agg_expr_2_0) throws java.io.IOException {
/* 140 */ // do aggregate for sum
/* 141 */ // evaluate aggregate function
/* 142 */ agg_agg_isNull_70_0 = true;
/* 143 */ double agg_value_70 = -1.0;
/* 144 */ do {
/* 145 */ boolean agg_isNull_71 = true;
/* 146 */ double agg_value_71 = -1.0;
/* 147 */ agg_agg_isNull_72_0 = true;
/* 148 */ double agg_value_72 = -1.0;
/* 149 */ do {
/* 150 */ if (!agg_bufIsNull_7) {
/* 151 */ agg_agg_isNull_72_0 = false;
/* 152 */ agg_value_72 = agg_bufValue_7;
/* 153 */ continue;
/* 154 */ }
/* 155 */
/* 156 */ if (!false) {
/* 157 */ agg_agg_isNull_72_0 = false;
/* 158 */ agg_value_72 = 0.0D;
/* 159 */ continue;
/* 160 */ }
/* 161 */
/* 162 */ } while (false);
/* 163 */ boolean agg_isNull_75 = true;
/* 164 */ double agg_value_75 = -1.0;
/* 165 */ boolean agg_isNull_76 = agg_exprIsNull_2_0;
/* 166 */ double agg_value_76 = -1.0;
/* 167 */ if (!agg_exprIsNull_2_0) {
/* 168 */ final String agg_doubleStr_2 = agg_expr_2_0.toString();
/* 169 */ try {
/* 170 */ agg_value_76 = Double.valueOf(agg_doubleStr_2);
/* 171 */ } catch (java.lang.NumberFormatException e) {
/* 172 */ final Double d = (Double) Cast.processFloatingPointSpecialLiterals(agg_doubleStr_2, false);
/* 173 */ if (d == null) {
/* 174 */ agg_isNull_76 = true;
/* 175 */ } else {
/* 176 */ agg_value_76 = d.doubleValue();
/* 177 */ }
/* 178 */ }
/* 179 */ }
/* 180 */ if (!agg_isNull_76) {
/* 181 */ agg_isNull_75 = false; // resultCode could change nullability.
/* 182 */
/* 183 */ agg_value_75 = agg_value_76 * 1.0D;
/* 184 */
/* 185 */ }
/* 186 */ if (!agg_isNull_75) {
/* 187 */ agg_isNull_71 = false; // resultCode could change nullability.
/* 188 */
/* 189 */ agg_value_71 = agg_value_72 + agg_value_75;
/* 190 */
/* 191 */ }
/* 192 */ if (!agg_isNull_71) {
/* 193 */ agg_agg_isNull_70_0 = false;
/* 194 */ agg_value_70 = agg_value_71;
/* 195 */ continue;
/* 196 */ }
/* 197 */
/* 198 */ if (!agg_bufIsNull_7) {
/* 199 */ agg_agg_isNull_70_0 = false;
/* 200 */ agg_value_70 = agg_bufValue_7;
/* 201 */ continue;
/* 202 */ }
/* 203 */
/* 204 */ } while (false);
/* 205 */ // update aggregation buffers
/* 206 */ agg_bufIsNull_7 = agg_agg_isNull_70_0;
/* 207 */ agg_bufValue_7 = agg_value_70;
/* 208 */ }
/* 209 */
/* 210 */ private void agg_doAggregateWithoutKey_0() throws java.io.IOException {
/* 211 */ // initialize aggregation buffer
/* 212 */ agg_bufIsNull_0 = true;
/* 213 */ agg_bufValue_0 = -1L;
/* 214 */ agg_bufIsNull_1 = true;
/* 215 */ agg_bufValue_1 = -1.0;
/* 216 */ agg_bufIsNull_2 = true;
/* 217 */ agg_bufValue_2 = -1.0;
/* 218 */ agg_bufIsNull_3 = true;
/* 219 */ agg_bufValue_3 = -1.0;
/* 220 */
/* 221 */ while (!agg_initAgg_1) {
/* 222 */ agg_initAgg_1 = true;
/* 223 */ long agg_beforeAgg_0 = System.nanoTime();
/* 224 */ agg_doAggregateWithoutKey_1();
/* 225 */ ((org.apache.spark.sql.execution.metric.SQLMetric) references[1] /* aggTime */).add((System.nanoTime() - agg_beforeAgg_0) / 1000000);
/* 226 */
/* 227 */ // output the result
/* 228 */
/* 229 */ ((org.apache.spark.sql.execution.metric.SQLMetric) references[0] /* numOutputRows */).add(1);
/* 230 */ agg_doConsume_1(agg_bufValue_4, agg_bufIsNull_4, agg_bufValue_5, agg_bufIsNull_5, agg_bufValue_6, agg_bufIsNull_6, agg_bufValue_7, agg_bufIsNull_7);
/* 231 */ }
/* 232 */
/* 233 */ }
/* 234 */
/* 235 */ private void agg_doAggregate_sum_2(boolean agg_exprIsNull_1_0, org.apache.spark.unsafe.types.UTF8String agg_expr_1_0) throws java.io.IOException {
/* 236 */ // do aggregate for sum
/* 237 */ // evaluate aggregate function
/* 238 */ agg_agg_isNull_60_0 = true;
/* 239 */ double agg_value_60 = -1.0;
/* 240 */ do {
/* 241 */ boolean agg_isNull_61 = true;
/* 242 */ double agg_value_61 = -1.0;
/* 243 */ agg_agg_isNull_62_0 = true;
/* 244 */ double agg_value_62 = -1.0;
/* 245 */ do {
/* 246 */ if (!agg_bufIsNull_6) {
/* 247 */ agg_agg_isNull_62_0 = false;
/* 248 */ agg_value_62 = agg_bufValue_6;
/* 249 */ continue;
/* 250 */ }
/* 251 */
/* 252 */ if (!false) {
/* 253 */ agg_agg_isNull_62_0 = false;
/* 254 */ agg_value_62 = 0.0D;
/* 255 */ continue;
/* 256 */ }
/* 257 */
/* 258 */ } while (false);
/* 259 */ boolean agg_isNull_65 = true;
/* 260 */ double agg_value_65 = -1.0;
/* 261 */ boolean agg_isNull_66 = agg_exprIsNull_1_0;
/* 262 */ double agg_value_66 = -1.0;
/* 263 */ if (!agg_exprIsNull_1_0) {
/* 264 */ final String agg_doubleStr_1 = agg_expr_1_0.toString();
/* 265 */ try {
/* 266 */ agg_value_66 = Double.valueOf(agg_doubleStr_1);
/* 267 */ } catch (java.lang.NumberFormatException e) {
/* 268 */ final Double d = (Double) Cast.processFloatingPointSpecialLiterals(agg_doubleStr_1, false);
/* 269 */ if (d == null) {
/* 270 */ agg_isNull_66 = true;
/* 271 */ } else {
/* 272 */ agg_value_66 = d.doubleValue();
/* 273 */ }
/* 274 */ }
/* 275 */ }
/* 276 */ if (!agg_isNull_66) {
/* 277 */ agg_isNull_65 = false; // resultCode could change nullability.
/* 278 */
/* 279 */ agg_value_65 = agg_value_66 * 1.0D;
/* 280 */
/* 281 */ }
/* 282 */ if (!agg_isNull_65) {
/* 283 */ agg_isNull_61 = false; // resultCode could change nullability.
/* 284 */
/* 285 */ agg_value_61 = agg_value_62 + agg_value_65;
/* 286 */
/* 287 */ }
/* 288 */ if (!agg_isNull_61) {
/* 289 */ agg_agg_isNull_60_0 = false;
/* 290 */ agg_value_60 = agg_value_61;
/* 291 */ continue;
/* 292 */ }
/* 293 */
/* 294 */ if (!agg_bufIsNull_6) {
/* 295 */ agg_agg_isNull_60_0 = false;
/* 296 */ agg_value_60 = agg_bufValue_6;
/* 297 */ continue;
/* 298 */ }
/* 299 */
/* 300 */ } while (false);
/* 301 */ // update aggregation buffers
/* 302 */ agg_bufIsNull_6 = agg_agg_isNull_60_0;
/* 303 */ agg_bufValue_6 = agg_value_60;
/* 304 */ }
/* 305 */
/* 306 */ private void agg_doConsume_1(long agg_expr_0_1, boolean agg_exprIsNull_0_1, double agg_expr_1_1, boolean agg_exprIsNull_1_1, double agg_expr_2_1, boolean agg_exprIsNull_2_1, double agg_expr_3_0, boolean agg_exprIsNull_3_0) throws java.io.IOException {
/* 307 */ // do aggregate
/* 308 */ // common sub-expressions
/* 309 */
/* 310 */ // evaluate aggregate functions and update aggregation buffers
/* 311 */ agg_doAggregate_sum_4(agg_exprIsNull_0_1, agg_expr_0_1);
/* 312 */ agg_doAggregate_sum_5(agg_expr_1_1, agg_exprIsNull_1_1);
/* 313 */ agg_doAggregate_sum_6(agg_expr_2_1, agg_exprIsNull_2_1);
/* 314 */ agg_doAggregate_sum_7(agg_exprIsNull_3_0, agg_expr_3_0);
/* 315 */
/* 316 */ }
/* 317 */
/* 318 */ private void project_doConsume_0(InternalRow inputadapter_row_0, UTF8String project_expr_0_0, boolean project_exprIsNull_0_0) throws java.io.IOException {
/* 319 */ agg_doConsume_0(project_expr_0_0, project_exprIsNull_0_0, project_expr_0_0, project_exprIsNull_0_0, project_expr_0_0, project_exprIsNull_0_0);
/* 320 */
/* 321 */ }
/* 322 */
/* 323 */ private void agg_doAggregate_sum_5(double agg_expr_1_1, boolean agg_exprIsNull_1_1) throws java.io.IOException {
/* 324 */ // do aggregate for sum
/* 325 */ // evaluate aggregate function
/* 326 */ agg_agg_isNull_95_0 = true;
/* 327 */ double agg_value_95 = -1.0;
/* 328 */ do {
/* 329 */ boolean agg_isNull_96 = true;
/* 330 */ double agg_value_96 = -1.0;
/* 331 */ agg_agg_isNull_97_0 = true;
/* 332 */ double agg_value_97 = -1.0;
/* 333 */ do {
/* 334 */ if (!agg_bufIsNull_1) {
/* 335 */ agg_agg_isNull_97_0 = false;
/* 336 */ agg_value_97 = agg_bufValue_1;
/* 337 */ continue;
/* 338 */ }
/* 339 */
/* 340 */ if (!false) {
/* 341 */ agg_agg_isNull_97_0 = false;
/* 342 */ agg_value_97 = 0.0D;
/* 343 */ continue;
/* 344 */ }
/* 345 */
/* 346 */ } while (false);
/* 347 */
/* 348 */ if (!agg_exprIsNull_1_1) {
/* 349 */ agg_isNull_96 = false; // resultCode could change nullability.
/* 350 */
/* 351 */ agg_value_96 = agg_value_97 + agg_expr_1_1;
/* 352 */
/* 353 */ }
/* 354 */ if (!agg_isNull_96) {
/* 355 */ agg_agg_isNull_95_0 = false;
/* 356 */ agg_value_95 = agg_value_96;
/* 357 */ continue;
/* 358 */ }
/* 359 */
/* 360 */ if (!agg_bufIsNull_1) {
/* 361 */ agg_agg_isNull_95_0 = false;
/* 362 */ agg_value_95 = agg_bufValue_1;
/* 363 */ continue;
/* 364 */ }
/* 365 */
/* 366 */ } while (false);
/* 367 */ // update aggregation buffers
/* 368 */ agg_bufIsNull_1 = agg_agg_isNull_95_0;
/* 369 */ agg_bufValue_1 = agg_value_95;
/* 370 */ }
/* 371 */
/* 372 */ private void agg_doAggregate_sum_1(boolean agg_e[error] ! certain inputs.aggregation - no group-by - aggregtion functions only
xprIsNull_0_0, org.apache.spark.unsafe.types.UTF8String agg_expr_0_0) throws java.io.IOException {
/* 373 */ // do aggregate for sum
/* 374 */ // evaluate aggregate function
/* 375 */ agg_agg_isNull_50_0 = true;
/* 376 */ double agg_value_50 = -1.0;
/* 377 */ do {
/* 378 */ boolean agg_isNull_51 = true;
/* 379 */ double agg_value_51 = -1.0;
/* 380 */ agg_agg_isNull_52_0 = true;
/* 381 */ double agg_value_52 = -1.0;
/* 382 */ do {
/* 383 */ if (!agg_bufIsNull_5) {
/* 384 */ agg_agg_isNull_52_0 = false;
/* 385 */ agg_value_52 = agg_bufValue_5;
/* 386 */ continue;
/* 387 */ }
/* 388 */
/* 389 */ if (!false) {
/* 390 */ agg_agg_isNull_52_0 = false;
/* 391 */ agg_value_52 = 0.0D;
/* 392 */ continue;
/* 393 */ }
/* 394 */
/* 395 */ } while (false);
/* 396 */ boolean agg_isNull_55 = true;
/* 397 */ double agg_value_55 = -1.0;
/* 398 */ boolean agg_isNull_56 = agg_exprIsNull_0_0;
/* 399 */ double agg_value_56 = -1.0;
/* 400 */ if (!agg_exprIsNull_0_0) {
/* 401 */ final String agg_doubleStr_0 = agg_expr_0_0.toString();
/* 402 */ try {
/* 403 */ agg_value_56 = Double.valueOf(agg_doubleStr_0);
/* 404 */ } catch (java.lang.NumberFormatException e) {
/* 405 */ final Double d = (Double) Cast.processFloatingPointSpecialLiterals(agg_doubleStr_0, false);
/* 406 */ if (d == null) {
/* 407 */ agg_isNull_56 = true;
/* 408 */ } else {
/* 409 */ agg_value_56 = d.doubleValue();
/* 410 */ }
/* 411 */ }
/* 412 */ }
/* 413 */ if (!agg_isNull_56) {
/* 414 */ agg_isNull_55 = false; // resultCode could change nullability.
/* 415 */
/* 416 */ agg_value_55 = agg_value_56 * 1.0D;
/* 417 */
/* 418 */ }
/* 419 */ if (!agg_isNull_55) {
/* 420 */ agg_isNull_51 = false; // resultCode could change nullability.
/* 421 */
/* 422 */ agg_value_51 = agg_value_52 + agg_value_55;
/* 423 */
/* 424 */ }
/* 425 */ if (!agg_isNull_51) {
/* 426 */ agg_agg_isNull_50_0 = false;
/* 427 */ agg_value_50 = agg_value_51;
/* 428 */ continue;
/* 429 */ }
/* 430 */
/* 431 */ if (!agg_bufIsNull_5) {
/* 432 */ agg_agg_isNull_50_0 = false;
/* 433 */ agg_value_50 = agg_bufValue_5;
/* 434 */ continue;
/* 435 */ }
/* 436 */
/* 437 */ } while (false);
/* 438 */ // update aggregation buffers
/* 439 */ agg_bufIsNull_5 = agg_agg_isNull_50_0;
/* 440 */ agg_bufValue_5 = agg_value_50;
/* 441 */ }
/* 442 */
/* 443 */ private void agg_doConsume_0(UTF8String agg_expr_0_0, boolean agg_exprIsNull_0_0, UTF8String agg_expr_1_0, boolean agg_exprIsNull_1_0, UTF8String agg_expr_2_0, boolean agg_exprIsNull_2_0) throws java.io.IOException {
/* 444 */ // do aggregate
/* 445 */ // common sub-expressions
/* 446 */
/* 447 */ // evaluate aggregate functions and update aggregation buffers
/* 448 */ agg_doAggregate_sum_0();
/* 449 */ agg_doAggregate_sum_1(agg_exprIsNull_0_0, agg_expr_0_0);
/* 450 */ agg_doAggregate_sum_2(agg_exprIsNull_1_0, agg_expr_1_0);
/* 451 */ agg_doAggregate_sum_3(agg_exprIsNull_2_0, agg_expr_2_0);
/* 452 */
/* 453 */ }
/* 454 */
/* 455 */ private void agg_doAggregate_sum_4(boolean agg_exprIsNull_0_1, long agg_expr_0_1) throws java.io.IOException {
/* 456 */ // do aggregate for sum
/* 457 */ // evaluate aggregate function
/* 458 */ agg_agg_isNull_88_0 = true;
/* 459 */ long agg_value_88 = -1L;
/* 460 */ do {
/* 461 */ boolean agg_isNull_89 = true;
/* 462 */ long agg_value_89 = -1L;
/* 463 */ agg_agg_isNull_90_0 = true;
/* 464 */ long agg_value_90 = -1L;
/* 465 */ do {
/* 466 */ if (!agg_bufIsNull_0) {
/* 467 */ agg_agg_isNull_90_0 = false;
/* 468 */ agg_value_90 = agg_bufValue_0;
/* 469 */ continue;
/* 470 */ }
/* 471 */
/* 472 */ if (!false) {
/* 473 */ agg_agg_isNull_90_0 = false;
/* 474 */ agg_value_90 = 0L;
/* 475 */ continue;
/* 476 */ }
/* 477 */
/* 478 */ } while (false);
/* 479 */
/* 480 */ if (!agg_exprIsNull_0_1) {
/* 481 */ agg_isNull_89 = false; // resultCode could change nullability.
/* 482 */
/* 483 */ agg_value_89 = agg_value_90 + agg_expr_0_1;
/* 484 */
/* 485 */ }
/* 486 */ if (!agg_isNull_89) {
/* 487 */ agg_agg_isNull_88_0 = false;
/* 488 */ agg_value_88 = agg_value_89;
/* 489 */ continue;
/* 490 */ }
/* 491 */
/* 492 */ if (!agg_bufIsNull_0) {
/* 493 */ agg_agg_isNull_88_0 = false;
/* 494 */ agg_value_88 = agg_bufValue_0;
/* 495 */ continue;
/* 496 */ }
/* 497 */
/* 498 */ } while (false);
/* 499 */ // update aggregation buffers
/* 500 */ agg_bufIsNull_0 = agg_agg_isNull_88_0;
/* 501 */ agg_bufValue_0 = agg_value_88;
/* 502 */ }
/* 503 */
/* 504 */ private void agg_doAggregateWithoutKey_1() throws java.io.IOException {
/* 505 */ // initialize aggregation buffer
/* 506 */ agg_bufIsNull_4 = true;
/* 507 */ agg_bufValue_4 = -1L;
/* 508 */ agg_bufIsNull_5 = true;
/* 509 */ agg_bufValue_5 = -1.0;
/* 510 */ agg_bufIsNull_6 = true;
/* 511 */ agg_bufValue_6 = -1.0;
/* 512 */ agg_bufIsNull_7 = true;
/* 513 */ agg_bufValue_7 = -1.0;
/* 514 */
/* 515 */ while ( inputadapter_input_0.hasNext()) {
/* 516 */ InternalRow inputadapter_row_0 = (InternalRow) inputadapter_input_0.next();
/* 517 */
/* 518 */ boolean inputadapter_isNull_0 = inputadapter_row_0.isNullAt(0);
/* 519 */ UTF8String inputadapter_value_0 = inputadapter_isNull_0 ?
/* 520 */ null : (inputadapter_row_0.getUTF8String(0));
/* 521 */
/* 522 */ project_doConsume_0(inputadapter_row_0, inputadapter_value_0, inputadapter_isNull_0);
/* 523 */ // shouldStop check is eliminated
/* 524 */ }
/* 525 */
/* 526 */ }
/* 527 */
/* 528 */ private void agg_doAggregate_sum_7(boolean agg_exprIsNull_3_0, double agg_expr_3_0) throws java.io.IOException {
/* 529 */ // do aggregate for sum
/* 530 */ // evaluate aggregate function
/* 531 */ agg_agg_isNull_109_0 = true;
/* 532 */ double agg_value_109 = -1.0;
/* 533 */ do {
/* 534 */ boolean agg_isNull_110 = true;
/* 535 */ double agg_value_110 = -1.0;
/* 536 */ agg_agg_isNull_111_0 = true;
/* 537 */ double agg_value_111 = -1.0;
/* 538 */ do {
/* 539 */ if (!agg_bufIsNull_3) {
/* 540 */ agg_agg_isNull_111_0 = false;
/* 541 */ agg_value_111 = agg_bufValue_3;
/* 542 */ continue;
/* 543 */ }
/* 544 */
/* 545 */ if (!false) {
/* 546 */ agg_agg_isNull_111_0 = false;
/* 547 */ agg_value_111 = 0.0D;
/* 548 */ continue;
/* 549 */ }
/* 550 */
/* 551 */ } while (false);
/* 552 */
/* 553 */ if (!agg_exprIsNull_3_0) {
/* 554 */ agg_isNull_110 = false; // resultCode could change nullability.
/* 555 */
/* 556 */ agg_value_110 = agg_value_111 + agg_expr_3_0;
/* 557 */
/* 558 */ }
/* 559 */ if (!agg_isNull_110) {
/* 560 */ agg_agg_isNull_109_0 = false;
/* 561 */ agg_value_109 = agg_value_110;
/* 562 */ continue;
/* 563 */ }
/* 564 */
/* 565 */ if (!agg_bufIsNull_3) {
/* 566 */ agg_agg_isNull_109_0 = false;
/* 567 */ agg_value_109 = agg_bufValue_3;
/* 568 */ continue;
/* 569 */ }
/* 570 */
/* 571 */ } while (false);
/* 572 */ // update aggregation buffers
/* 573 */ agg_bufIsNull_3 = agg_agg_isNull_109_0;
/* 574 */ agg_bufValue_3 = agg_value_109;
/* 575 */ }
/* 576 */
/* 577 */ protected void processNext() throws java.io.IOException {
/* 578 */ while (!agg_initAgg_0) {
/* 579 */ agg_initAgg_0 = true;
/* 580 */ long agg_beforeAgg_1 = System.nanoTime();
/* 581 */ agg_doAggregateWithoutKey_0();
/* 582 */ ((org.apache.spark.sql.executio[error] org.apache.spark.SparkException: Job aborted due to stage failure: Task 5 in stage 39.0 failed 1 times, most recent failure: Lost task 5.0 in stage 39.0 (TID 168, 192.168.0.7, executor driver): java.lang.RuntimeException: Error while encoding: java.lang.ArrayIndexOutOfBoundsException: Index 1 out of bounds for length 1
[error] if (assertnotnull(input[0, org.apache.spark.sql.Row, true]).isNullAt) null else staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 0, X), StringType), true, false) AS X#1129
[error] if (assertnotnull(input[0, org.apache.spark.sql.Row, true]).isNullAt) null else staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 1, __CAVEATS_ROW_LB), StringType), true, false) AS __CAVEATS_ROW_LB#1130
[error] if (assertnotnull(input[0, org.apache.spark.sql.Row, true]).isNullAt) null else staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 2, __CAVEATS_ROW_BG), StringType), true, false) AS __CAVEATS_ROW_BG#1131
[error] if (assertnotnull(input[0, org.apache.spark.sql.Row, true]).isNullAt) null else staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 3, __CAVEATS_ROW_UB), StringType), true, false) AS __CAVEATS_ROW_UB#1132
[error] if (assertnotnull(input[0, org.apache.spark.sql.Row, true]).isNullAt) null else staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 4, __CAVEATS_X_LB), StringType), true, false) AS __CAVEATS_X_LB#1133
[error] if (assertnotnull(input[0, org.apache.spark.sql.Row, true]).isNullAt) null else staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 5, __CAVEATS_X_UB), StringType), true, false) AS __CAVEATS_X_UB#1134
[error] at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.toRow(ExpressionEncoder.scala:344)
[error] at org.apache.spark.sql.SparkSession.$anonfun$createDataFrame$1(SparkSession.scala:350)
[error] at scala.collection.Iterator$$anon$10.next(Iterator.scala:459)
[error] at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
[error] at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
[error] at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:726)
[error] at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:321)
[error] at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:872)
[error] at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:872)
[error] at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
[error] at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
[error] at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
[error] at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
[error] at org.apache.spark.scheduler.Task.run(Task.scala:127)
[error] at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:441)
[error] at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
[error] at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:444)
[error] at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)
[error] at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
[error] at java.base/java.lang.Thread.run(Thread.java:830)
[error] Caused by: java.lang.ArrayIndexOutOfBoundsException: Index 1 out of bounds for length 1
[error] at org.apache.spark.sql.catalyst.expressions.GenericRow.get(rows.scala:174)
[error] at org.apache.spark.sql.Row.isNullAt(Row.scala:204)
[error] at org.apache.spark.sql.Row.isNullAt$(Row.scala:204)
[error] at org.apache.spark.sql.catalyst.expressions.GenericRow.isNullAt(rows.scala:166)
[error] at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.If_1$(Unknown Source)
[error] at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
[error] at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.toRow(ExpressionEncoder.scala:340)
[error] ... 19 more
[error]
[error] Driver stacktrace: (DAGScheduler.scala:1989)
n.metric.SQLMetric) references[3] /* aggTime */).add((System.nanoTime() - agg_beforeAgg_1) / 1000000);
/* 583 */
/* 584 */ // output the result
/* 585 */ boolean agg_isNull_9 = true;
/* 586 */ boolean agg_value_9 = false;
/* 587 */
/* 588 */ if (!agg_bufIsNull_0) {
/* 589 */ agg_isNull_9 = false; // resultCode could change nullability.
/* 590 */ agg_value_9 = agg_bufValue_0 == 0L;
/* 591 */
/* 592 */ }
/* 593 */ boolean agg_isNull_8 = false;
/* 594 */ double agg_value_8 = -1.0;
/* 595 */ if (!agg_isNull_9 && agg_value_9) {
/* 596 */ agg_isNull_8 = false;
/* 597 */ agg_value_8 = 0.0D;
/* 598 */ } else {
/* 599 */ boolean agg_isNull_15 = agg_bufIsNull_0;
/* 600 */ double agg_value_15 = -1.0;
/* 601 */ if (!agg_bufIsNull_0) {
/* 602 */ agg_value_15 = (double) agg_bufValue_0;
/* 603 */ }
/* 604 */ boolean agg_isNull_13 = false;
/* 605 */ double agg_value_13 = -1.0;
/* 606 */ if (agg_isNull_15 || agg_value_15 == 0) {
/* 607 */ agg_isNull_13 = true;
/* 608 */ } else {
/* 609 */ if (agg_bufIsNull_0) {
/* 610 */ agg_isNull_13 = true;
/* 611 */ } else {
/* 612 */ agg_value_13 = (double)(agg_bufValue_0 / agg_value_15);
/* 613 */ }
/* 614 */ }
/* 615 */ agg_isNull_8 = agg_isNull_13;
/* 616 */ agg_value_8 = agg_value_13;
/* 617 */ }
/* 618 */ boolean agg_isNull_21 = true;
/* 619 */ boolean agg_value_21 = false;
/* 620 */
/* 621 */ if (!agg_bufIsNull_0) {
/* 622 */ agg_isNull_21 = false; // resultCode could change nullability.
/* 623 */ agg_value_21 = agg_bufValue_0 == 0L;
/* 624 */
/* 625 */ }
/* 626 */ boolean agg_isNull_20 = false;
/* 627 */ double agg_value_20 = -1.0;
/* 628 */ if (!agg_isNull_21 && agg_value_21) {
/* 629 */ agg_isNull_20 = false;
/* 630 */ agg_value_20 = 0.0D;
/* 631 */ } else {
/* 632 */ boolean agg_isNull_27 = agg_bufIsNull_0;
/* 633 */ double agg_value_27 = -1.0;
/* 634 */ if (!agg_bufIsNull_0) {
/* 635 */ agg_value_27 = (double) agg_bufValue_0;
/* 636 */ }
/* 637 */ boolean agg_isNull_25 = false;
/* 638 */ double agg_value_25 = -1.0;
/* 639 */ if (agg_isNull_27 || agg_value_27 == 0) {
/* 640 */ agg_isNull_25 = true;
/* 641 */ } else {
/* 642 */ if (agg_bufIsNull_0) {
/* 643 */ agg_isNull_25 = true;
/* 644 */ } else {
/* 645 */ agg_value_25 = (double)(agg_bufValue_0 / agg_value_27);
/* 646 */ }
/* 647 */ }
/* 648 */ agg_isNull_20 = agg_isNull_25;
/* 649 */ agg_value_20 = agg_value_25;
/* 650 */ }
/* 651 */ boolean agg_isNull_30 = true;
/* 652 */ boolean agg_value_30 = false;
/* 653 */
/* 654 */ if (!agg_bufIsNull_0) {
/* 655 */ agg_isNull_30 = false; // resultCode could change nullability.
/* 656 */ agg_value_30 = agg_bufValue_0 == 0L;
/* 657 */
/* 658 */ }
/* 659 */ boolean agg_isNull_29 = false;
/* 660 */ double agg_value_29 = -1.0;
/* 661 */ if (!agg_isNull_30 && agg_value_30) {
/* 662 */ agg_isNull_29 = false;
/* 663 */ agg_value_29 = 0.0D;
/* 664 */ } else {
/* 665 */ boolean agg_isNull_36 = agg_bufIsNull_0;
/* 666 */ double agg_value_36 = -1.0;
/* 667 */ if (!agg_bufIsNull_0) {
/* 668 */ agg_value_36 = (double) agg_bufValue_0;
/* 669 */ }
/* 670 */ boolean agg_isNull_34 = false;
/* 671 */ double agg_value_34 = -1.0;
/* 672 */ if (agg_isNull_36 || agg_value_36 == 0) {
/* 673 */ agg_isNull_34 = true;
/* 674 */ } else {
/* 675 */ if (agg_bufIsNull_0) {
/* 676 */ agg_isNull_34 = true;
/* 677 */ } else {
/* 678 */ agg_value_34 = (double)(agg_bufValue_0 / agg_value_36);
/* 679 */ }
/* 680 */ }
/* 681 */ agg_isNull_29 = agg_isNull_34;
/* 682 */ agg_value_29 = agg_value_34;
/* 683 */ }
/* 684 */
/* 685 */ ((org.apache.spark.sql.execution.metric.SQLMetric) references[2] /* numOutputRows */).add(1);
/* 686 */ project_mutableStateArray_0[4].reset();
/* 687 */
/* 688 */ project_mutableStateArray_0[4].zeroOutNullBytes();
/* 689 */
/* 690 */ if (agg_isNull_8) {
/* 691 */ project_mutableStateArray_0[4].setNullAt(0);
/* 692 */ } else {
/* 693 */ project_mutableStateArray_0[4].write(0, agg_value_8);
/* 694 */ }
/* 695 */
/* 696 */ project_mutableStateArray_0[4].write(1, 1);
/* 697 */
/* 698 */ project_mutableStateArray_0[4].write(2, 1);
/* 699 */
/* 700 */ project_mutableStateArray_0[4].write(3, 1);
/* 701 */
/* 702 */ if (agg_isNull_20) {
/* 703 */ project_mutableStateArray_0[4].setNullAt(4);
/* 704 */ } else {
/* 705 */ project_mutableStateArray_0[4].write(4, agg_value_20);
/* 706 */ }
/* 707 */
/* 708 */ if (agg_isNull_29) {
/* 709 */ project_mutableStateArray_0[4].setNullAt(5);
/* 710 */ } else {
/* 711 */ project_mutableStateArray_0[4].write(5, agg_value_29);
/* 712 */ }
/* 713 */ append((project_mutableStateArray_0[4].getRow()));
/* 714 */ }
/* 715 */ }
/* 716 */
/* 717 */ }
15:17:36.185 [Executor task launch worker for task 168] ERROR org.apache.spark.executor.Executor - Exception in task 5.0 in stage 39.0 (TID 168)
java.lang.RuntimeException: Error while encoding: java.lang.ArrayIndexOutOfBoundsException: Index 1 out of bounds for length 1
if (assertnotnull(input[0, org.apache.spark.sql.Row, true]).isNullAt) null else staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 0, X), StringType), true, false) AS X#1129
if (assertnotnull(input[0, org.apache.spark.sql.Row, true]).isNullAt) null else staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 1, __CAVEATS_ROW_LB), StringType), true, false) AS __CAVEATS_ROW_LB#1130
if (assertnotnull(input[0, org.apache.spark.sql.Row, true]).isNullAt) null else staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 2, __CAVEATS_ROW_BG), StringType), true, false) AS __CAVEATS_ROW_BG#1131
if (assertnotnull(input[0, org.apache.spark.sql.Row, true]).isNullAt) null else staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 3, __CAVEATS_ROW_UB), StringType), true, false) AS __CAVEATS_ROW_UB#1132
if (assertnotnull(input[0, org.apache.spark.sql.Row, true]).isNullAt) null else staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 4, __CAVEATS_X_LB), StringType), true, false) AS __CAVEATS_X_LB#1133
if (assertnotnull(input[0, org.apache.spark.sql.Row, true]).isNullAt) null else staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 5, __CAVEATS_X_UB), StringType), true, false) AS __CAVEATS_X_UB#1134
at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.toRow(ExpressionEncoder.scala:344)
at org.apache.spark.sql.SparkSession.$anonfun$createDataFrame$1(SparkSession.scala:350)
at scala.collection.Iterator$$anon$10.next(Iterator.scala:459)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.a[error] org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:1989)
pache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:726)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:321)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:872)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:872)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:127)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:441)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:444)
at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)
at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
at java.base/java.lang.Thread.run(Thread.java:830)
Caused by: java.lang.ArrayIndexOutOfBoundsException: Index 1 out of bounds for length 1
at org.apache.spark.sql.catalyst.expressions.GenericRow.get(rows.scala:174)
at org.apache.spark.sql.Row.isNullAt(Row.scala:204)
at org.apache.spark.sql.Row.isNullAt$(Row.scala:204)
at org.apache.spark.sql.catalyst.expressions.GenericRow.isNullAt(rows.scala:166)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.If_1$(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.toRow(ExpressionEncoder.scala:340)
... 19 common frames omitted
15:17:36.185 [Executor task launch worker for task 174] ERROR org.apache.spark.executor.Executor - Exception in task 11.0 in stage 39.0 (TID 174)
java.lang.RuntimeException: Error while encoding: java.lang.ArrayIndexOutOfBoundsException: Index 5 out of bounds for length 5
if (assertnotnull(input[0, org.apache.spark.sql.Row, true]).isNullAt) null else staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 0, X), StringType), true, false) AS X#1129
if (assertnotnull(input[0, org.apache.spark.sql.Row, true]).isNullAt) null else staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 1, __CAVEATS_ROW_LB), StringType), true, false) AS __CAVEATS_ROW_LB#1130
if (assertnotnull(input[0, org.apache.spark.sql.Row, true]).isNullAt) null else staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 2, __CAVEATS_ROW_BG), StringType), true, false) AS __CAVEATS_ROW_BG#1131
if (assertnotnull(input[0, org.apache.spark.sql.Row, true]).isNullAt) null else staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 3, __CAVEATS_ROW_UB), StringType), true, false) AS __CAVEATS_ROW_UB#1132
if (assertnotnull(input[0, org.apache.spark.sql.Row, true]).isNullAt) null else staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 4, __CAVEATS_X_LB), StringType), true, false) AS __CAVEATS_X_LB#1133
if (assertnotnull(input[0, org.apache.spark.sql.Row, true]).isNullAt) null else staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 5, __CAVEATS_X_UB), StringType), true, false) AS __CAVEATS_X_UB#1134
at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.toRow(ExpressionEncoder.scala:344)
at org.apache.spark.sql.SparkSession.$anonfun$createDataFrame$1(SparkSession.scala:350)
at scala.collection.Iterator$$anon$10.next(Iterator.scala:459)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:726)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:321)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:872)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:872)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:127)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:441)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:444)
at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)
at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
at java.base/java.lang.Thread.run(Thread.java:830)
Caused by: java.lang.ArrayIndexOutOfBoundsException: Index 5 out of bounds for length 5
at org.apache.spark.sql.catalyst.expressions.GenericRow.get(rows.scala:174)
at org.apache.spark.sql.Row.isNullAt(Row.scala:204)
at org.apache.spark.sql.Row.isNullAt$(Row.scala:204)
at org.apache.spark.sql.catalyst.expressions.GenericRow.isNullAt(rows.scala:166)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.If_5$(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.toRow(ExpressionEncoder.scala:340)
... 19 common frames omitted
15:17:36.217 [task-result-getter-2] ERROR o.a.spark.scheduler.TaskSetManager - Task 5 in stage 39.0 failed 1 times; aborting job
[error] org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:1977)
[error] org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:1976)
[error] org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1976)
[error] org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:956)
[error] org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:956)
[error] org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:956)
[error] org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2206)
[error] org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2155)
[error] org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2144)
[error] org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
[error] org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:758)
[error] org.apache.spark.SparkContext.runJob(SparkContext.scala:2116)
[error] org.apache.spark.SparkContext.runJob(SparkContext.scala:2137)
[error] org.apache.spark.SparkContext.runJob(SparkContext.scala:2156)
[error] org.apache.spark.SparkContext.runJob(SparkContext.scala:2181)
[error] org.apache.spark.rdd.RDD.$anonfun$collect$1(RDD.scala:1004)
[error] org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
[error] org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
[error] org.apache.spark.rdd.RDD.withScope(RDD.scala:388)
[error] org.apache.spark.rdd.RDD.collect(RDD.scala:1003)
[error] org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:365)
[error] org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3482)
[error] org.apache.spark.sql.Dataset.$anonfun$collect$1(Dataset.scala:2812)
[error] org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3472)
[error] org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$4(SQLExecution.scala:100)
[error] org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:160)
[error] org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:87)
[error] org.apache.spark.sql.Dataset.withAction(Dataset.scala:3468)
[error] org.apache.spark.sql.Dataset.collect(Dataset.scala:2812)
[error] org.mimirdb.utility.Bag$.apply(Bag.scala:41)
[error] org.mimirdb.test.DataFrameMatchers.dfBagEquals(DataFrameMatchers.scala:14)
[error] org.mimirdb.test.DataFrameMatchers.dfBagEquals$(DataFrameMatchers.scala:12)
[error] org.mimirdb.caveats.LogicalPlanRangeSpec.dfBagEquals(LogicalPlanRangeSpec.scala:22)
[error] org.mimirdb.test.DataFrameMatchers.$anonfun$beBagEqualsTo$2(DataFrameMatchers.scala:48)
[error] org.mimirdb.caveats.LogicalPlanRangeSpec.$anonfun$annotBagEqualToDF$1(LogicalPlanRangeSpec.scala:81)
[error] org.mimirdb.caveats.LogicalPlanRangeSpec.annotBagEqualToDF(LogicalPlanRangeSpec.scala:81)
[error] org.mimirdb.caveats.LogicalPlanRangeSpec.$anonfun$new$8(LogicalPlanRangeSpec.scala:368)
[error] org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.toRow(ExpressionEncoder.scala:344)
[error] org.apache.spark.sql.SparkSession.$anonfun$createDataFrame$1(SparkSession.scala:350)
[error] org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
[error] org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
[error] org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:726)
[error] org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:321)
[error] org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:872)
[error] org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:872)
[error] org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
[error] org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
[error] org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
[error] org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
[error] org.apache.spark.scheduler.Task.run(Task.scala:127)
[error] org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:441)
[error] org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
[error] org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:444)
[error] org.apache.spark.sql.catalyst.expressions.GenericRow.get(rows.scala:174)
[error] org.apache.spark.sql.Row.isNullAt(Row.scala:204)
[error] org.apache.spark.sql.Row.isNullAt$(Row.scala:204)
[error] org.apache.spark.sql.catalyst.expressions.GenericRow.isNullAt(rows.scala:166)
[error] org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.If_1$(Unknown Source)
[error] org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
[error] org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.toRow(ExpressionEncoder.scala:340)
[error] org.apache.spark.sql.SparkSession.$anonfun$createDataFrame$1(SparkSession.scala:350)
[error] org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
[error] org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
[error] org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:726)
[error] org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:321)
[error] org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:872)
[error] org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:872)
[error] org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
[error] org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
[error] org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
[error] org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
[error] org.apache.spark.scheduler.Task.run(Task.scala:127)
[error] org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:441)
[error] org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
[error] org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:444)
[error] CAUSED BY
[error] java.lang.RuntimeException: Error while encoding: java.lang.ArrayIndexOutOfBoundsException: Index 1 out of bounds for length 1
[error] if (assertnotnull(input[0, org.apache.spark.sql.Row, true]).isNullAt) null else staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 0, X), StringType), true, false) AS X#1129
[error] if (assertnotnull(input[0, org.apache.spark.sql.Row, true]).isNullAt) null else staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 1, __CAVEATS_ROW_LB), StringType), true, false) AS __CAVEATS_ROW_LB#1130
[error] if (assertnotnull(input[0, org.apache.spark.sql.Row, true]).isNullAt) null else staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 2, __CAVEATS_ROW_BG), StringType), true, false) AS __CAVEATS_ROW_BG#1131
[error] if (assertnotnull(input[0, org.apache.spark.sql.Row, true]).isNullAt) null else staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 3, __CAVEATS_ROW_UB), StringType), true, false) AS __CAVEATS_ROW_UB#1132
[error] if (assertnotnull(input[0, org.apache.spark.sql.Row, true]).isNullAt) null else staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 4, __CAVEATS_X_LB), StringType), true, false) AS __CAVEATS_X_LB#1133
[error] if (assertnotnull(input[0, org.apache.spark.sql.Row, true]).isNullAt) null else staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 5, __CAVEATS_X_UB), StringType), true, false) AS __CAVEATS_X_UB#1134 (ExpressionEncoder.scala:344)
[error] org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.toRow(ExpressionEncoder.scala:344)
[error] org.apache.spark.sql.SparkSession.$anonfun$createDataFrame$1(SparkSession.scala:350)
[error] org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
[error] org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
[error] org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:726)
[error] org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:321)
[error] org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:872)
[error] org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:872)
[error] org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
[error] org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
[error] org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
[error] org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
[error] org.apache.spark.scheduler.Task.run(Task.scala:127)
[error] org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:441)
[error] org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
[error] org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:444)
[error] org.apache.spark.sql.catalyst.expressions.GenericRow.get(rows.scala:174)
[error] org.apache.spark.sql.Row.isNullAt(Row.scala:204)
[error] org.apache.spark.sql.Row.isNullAt$(Row.scala:204)
[error] org.apache.spark.sql.catalyst.expressions.GenericRow.isNullAt(rows.scala:166)
[error] org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.If_1$(Unknown Source)
[error] org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
[error] org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.toRow(ExpressionEncoder.scala:340)
[error] org.apache.spark.sql.SparkSession.$anonfun$createDataFrame$1(SparkSession.scala:350)
[error] org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
[error] org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
[error] org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:726)
[error] org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:321)
[error] org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:872)
[error] org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:872)
[error] org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
[error] org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
[error] org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
[error] org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
[error] org.apache.spark.scheduler.Task.run(Task.scala:127)
[error] org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:441)
[error] org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
[error] org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:444)
[error] CAUSED BY
[error] java.lang.ArrayIndexOutOfBoundsException: Index 1 out of bounds for length 1 (rows.scala:174)
[error] org.apache.spark.sql.catalyst.expressions.GenericRow.get(rows.scala:174)
[error] org.apache.spark.sql.Row.isNullAt(Row.scala:204)
[error] org.apache.spark.sql.Row.isNullAt$(Row.scala:204)
[error] org.apache.spark.sql.catalyst.expressions.GenericRow.isNullAt(rows.scala:166)
[error] org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.If_1$(Unknown Source)
[error] org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
[error] org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.toRow(ExpressionEncoder.scala:340)
[error] org.apache.spark.sql.SparkSession.$anonfun$createDataFrame$1(SparkSession.scala:350)
[error] org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
[error] org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
[error] org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:726)
[error] org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:321)
[error] org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:872)
[error] org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:872)
[error] org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
[error] org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
[error] org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
[error] org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
[error] org.apache.spark.scheduler.Task.run(Task.scala:127)
[error] org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:441)
[error] org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
[error] org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:444)
[info] TIP inputs
[info] Caveated inputs
[info] Total for specification LogicalPlanRangeSpec
[info] Finished in 8 seconds, 31 ms
[info] 1 example, 0 failure, 1 error
[error] Error: Total 1, Failed 0, Errors 1, Passed 0
[error] Error during tests:
[error] org.mimirdb.caveats.LogicalPlanRangeSpec
[error] (Test / testOnly) sbt.TestsFailedException: Tests unsuccessful
[error] Total time: 17 s, completed Jun 11, 2020, 3:17:44 PM
sbt:mimir-caveats>
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment