Spark to Pandas Conversion with Arrow Example

PySpark_to_Pandas_with_Arrow.ipynb

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   "source": [
    "## Create a Sample DataFrame in Spark\n",
    "To generate some sample data, we will make a DataFrame with 2 columns: 1 long and 1 double and 4,194,304 records"
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      "root\n",
      " |-- id: long (nullable = false)\n",
      " |-- x: double (nullable = false)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from pyspark.sql.functions import rand\n",
    "df = spark.range(1 << 22).toDF(\"id\").withColumn(\"x\", rand())\n",
    "df.printSchema()"
   ]
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    "## Conversion to a Pandas DataFrame without Arrow\n",
    "\n",
    "This uses the default Spark serializers to transfer the data and load it into Pandas 1 record at a time. It's a very inefficient process due to the high overhead of serialization and having to process individual scalar values."
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     "text": [
      "CPU times: user 17.1 s, sys: 552 ms, total: 17.6 s\n",
      "Wall time: 20.5 s\n"
     ]
    }
   ],
   "source": [
    "%time pdf = df.toPandas()"
   ]
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   "source": [
    "## Enable Arrow with a Spark property\n",
    "By default, Arrow is not enabled in Spark.  You can enable by setting the following SQLConf or adding \"spark.sql.execution.arrow.enabled=true\" to your Spark configuration at `conf/spark-defaults.conf`"
   ]
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   "source": [
    "spark.conf.set(\"spark.sql.execution.arrow.enabled\", \"true\")"
   ]
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   "cell_type": "markdown",
   "metadata": {},
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    "## Run the conversion again, this time with Arrow\n",
    "With Arrow enabled, the call `toPandas()` is optimized to use Arrow to transfer the data and avoid serialization costs. Arrow can then utilize zero-copy methods to produce a Pandas DataFrame on chunks of data at a time, making the entire process very efficient."
   ]
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   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "CPU times: user 20 ms, sys: 56 ms, total: 76 ms\n",
      "Wall time: 692 ms\n"
     ]
    }
   ],
   "source": [
    "%time pdf = df.toPandas()"
   ]
  }
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