org.apache.spark.sql.execution.python.BatchEvalPythonExec.scala Maven / Gradle / Ivy
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* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
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package org.apache.spark.sql.execution.python
import scala.collection.JavaConverters._
import net.razorvine.pickle.{Pickler, Unpickler}
import org.apache.spark.TaskContext
import org.apache.spark.api.python.{ChainedPythonFunctions, PythonEvalType}
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.execution.SparkPlan
import org.apache.spark.sql.types.{StructField, StructType}
/**
* A physical plan that evaluates a [[PythonUDF]]
*/
case class BatchEvalPythonExec(udfs: Seq[PythonUDF], output: Seq[Attribute], child: SparkPlan)
extends EvalPythonExec(udfs, output, child) {
protected override def evaluate(
funcs: Seq[ChainedPythonFunctions],
bufferSize: Int,
reuseWorker: Boolean,
argOffsets: Array[Array[Int]],
iter: Iterator[InternalRow],
schema: StructType,
context: TaskContext): Iterator[InternalRow] = {
EvaluatePython.registerPicklers() // register pickler for Row
val dataTypes = schema.map(_.dataType)
val needConversion = dataTypes.exists(EvaluatePython.needConversionInPython)
// enable memo iff we serialize the row with schema (schema and class should be memorized)
val pickle = new Pickler(needConversion)
// Input iterator to Python: input rows are grouped so we send them in batches to Python.
// For each row, add it to the queue.
val inputIterator = iter.map { row =>
if (needConversion) {
EvaluatePython.toJava(row, schema)
} else {
// fast path for these types that does not need conversion in Python
val fields = new Array[Any](row.numFields)
var i = 0
while (i < row.numFields) {
val dt = dataTypes(i)
fields(i) = EvaluatePython.toJava(row.get(i, dt), dt)
i += 1
}
fields
}
}.grouped(100).map(x => pickle.dumps(x.toArray))
// Output iterator for results from Python.
val outputIterator = new PythonUDFRunner(
funcs, bufferSize, reuseWorker, PythonEvalType.SQL_BATCHED_UDF, argOffsets)
.compute(inputIterator, context.partitionId(), context)
val unpickle = new Unpickler
val mutableRow = new GenericInternalRow(1)
val resultType = if (udfs.length == 1) {
udfs.head.dataType
} else {
StructType(udfs.map(u => StructField("", u.dataType, u.nullable)))
}
val fromJava = EvaluatePython.makeFromJava(resultType)
outputIterator.flatMap { pickedResult =>
val unpickledBatch = unpickle.loads(pickedResult)
unpickledBatch.asInstanceOf[java.util.ArrayList[Any]].asScala
}.map { result =>
if (udfs.length == 1) {
// fast path for single UDF
mutableRow(0) = fromJava(result)
mutableRow
} else {
fromJava(result).asInstanceOf[InternalRow]
}
}
}
}