org.apache.spark.sql.execution.python.EvalPythonExec.scala Maven / Gradle / Ivy

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/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * 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
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.apache.spark.sql.execution.python

import java.io.File

import scala.collection.mutable.ArrayBuffer

import org.apache.spark.{SparkEnv, TaskContext}
import org.apache.spark.api.python.ChainedPythonFunctions
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.execution.{SparkPlan, UnaryExecNode}
import org.apache.spark.sql.types.{DataType, StructField, StructType}
import org.apache.spark.util.Utils


/**
 * A physical plan that evaluates a [[PythonUDF]], one partition of tuples at a time.
 *
 * Python evaluation works by sending the necessary (projected) input data via a socket to an
 * external Python process, and combine the result from the Python process with the original row.
 *
 * For each row we send to Python, we also put it in a queue first. For each output row from Python,
 * we drain the queue to find the original input row. Note that if the Python process is way too
 * slow, this could lead to the queue growing unbounded and spill into disk when run out of memory.
 *
 * Here is a diagram to show how this works:
 *
 *            Downstream (for parent)
 *             /      \
 *            /     socket  (output of UDF)
 *           /         \
 *        RowQueue    Python
 *           \         /
 *            \     socket  (input of UDF)
 *             \     /
 *          upstream (from child)
 *
 * The rows sent to and received from Python are packed into batches (100 rows) and serialized,
 * there should be always some rows buffered in the socket or Python process, so the pulling from
 * RowQueue ALWAYS happened after pushing into it.
 */
abstract class EvalPythonExec(udfs: Seq[PythonUDF], resultAttrs: Seq[Attribute], child: SparkPlan)
  extends UnaryExecNode {

  override def output: Seq[Attribute] = child.output ++ resultAttrs

  override def producedAttributes: AttributeSet = AttributeSet(resultAttrs)

  private def collectFunctions(udf: PythonUDF): (ChainedPythonFunctions, Seq[Expression]) = {
    udf.children match {
      case Seq(u: PythonUDF) =>
        val (chained, children) = collectFunctions(u)
        (ChainedPythonFunctions(chained.funcs ++ Seq(udf.func)), children)
      case children =>
        // There should not be any other UDFs, or the children can't be evaluated directly.
        assert(children.forall(_.find(_.isInstanceOf[PythonUDF]).isEmpty))
        (ChainedPythonFunctions(Seq(udf.func)), udf.children)
    }
  }

  protected def evaluate(
      funcs: Seq[ChainedPythonFunctions],
      argOffsets: Array[Array[Int]],
      iter: Iterator[InternalRow],
      schema: StructType,
      context: TaskContext): Iterator[InternalRow]

  protected override def doExecute(): RDD[InternalRow] = {
    val inputRDD = child.execute().map(_.copy())

    inputRDD.mapPartitions { iter =>
      val context = TaskContext.get()

      // The queue used to buffer input rows so we can drain it to
      // combine input with output from Python.
      val queue = HybridRowQueue(context.taskMemoryManager(),
        new File(Utils.getLocalDir(SparkEnv.get.conf)), child.output.length)
      context.addTaskCompletionListener[Unit] { ctx =>
        queue.close()
      }

      val (pyFuncs, inputs) = udfs.map(collectFunctions).unzip

      // flatten all the arguments
      val allInputs = new ArrayBuffer[Expression]
      val dataTypes = new ArrayBuffer[DataType]
      val argOffsets = inputs.map { input =>
        input.map { e =>
          if (allInputs.exists(_.semanticEquals(e))) {
            allInputs.indexWhere(_.semanticEquals(e))
          } else {
            allInputs += e
            dataTypes += e.dataType
            allInputs.length - 1
          }
        }.toArray
      }.toArray
      val projection = MutableProjection.create(allInputs, child.output)
      val schema = StructType(dataTypes.zipWithIndex.map { case (dt, i) =>
        StructField(s"_$i", dt)
      })

      // Add rows to queue to join later with the result.
      val projectedRowIter = iter.map { inputRow =>
        queue.add(inputRow.asInstanceOf[UnsafeRow])
        projection(inputRow)
      }

      val outputRowIterator = evaluate(
        pyFuncs, argOffsets, projectedRowIter, schema, context)

      val joined = new JoinedRow
      val resultProj = UnsafeProjection.create(output, output)

      outputRowIterator.map { outputRow =>
        resultProj(joined(queue.remove(), outputRow))
      }
    }
  }
}