spark SerDeUtil 源码

spark SerDeUtil 代码

文件路径：/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala

/*
 * 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.api.python

import java.util.{ArrayList => JArrayList}

import scala.collection.JavaConverters._
import scala.collection.mutable
import scala.util.Failure
import scala.util.Try

import net.razorvine.pickle.{Pickler, Unpickler}

import org.apache.spark.SparkException
import org.apache.spark.api.java.JavaRDD
import org.apache.spark.internal.Logging
import org.apache.spark.rdd.RDD

/** Utilities for serialization / deserialization between Python and Java, using Pickle. */
private[spark] object SerDeUtil extends Logging {
  class ByteArrayConstructor extends net.razorvine.pickle.objects.ByteArrayConstructor {
    override def construct(args: Array[Object]): Object = {
      // Deal with an empty byte array pickled by Python 3.
      if (args.length == 0) {
        Array.emptyByteArray
      } else {
        super.construct(args)
      }
    }
  }

  private var initialized = false
  // This should be called before trying to unpickle array.array from Python
  // In cluster mode, this should be put in closure
  def initialize(): Unit = {
    synchronized {
      if (!initialized) {
        Unpickler.registerConstructor("__builtin__", "bytearray", new ByteArrayConstructor())
        Unpickler.registerConstructor("builtins", "bytearray", new ByteArrayConstructor())
        Unpickler.registerConstructor("__builtin__", "bytes", new ByteArrayConstructor())
        Unpickler.registerConstructor("_codecs", "encode", new ByteArrayConstructor())
        initialized = true
      }
    }
  }
  initialize()


  /**
   * Convert an RDD of Java objects to Array (no recursive conversions).
   * It is only used by pyspark.sql.
   */
  def toJavaArray(jrdd: JavaRDD[Any]): JavaRDD[Array[_]] = {
    jrdd.rdd.map {
      case objs: JArrayList[_] =>
        objs.toArray
      case obj if obj.getClass.isArray =>
        obj.asInstanceOf[Array[_]].toArray
    }.toJavaRDD()
  }

  /**
   * Choose batch size based on size of objects
   */
  private[spark] class AutoBatchedPickler(iter: Iterator[Any]) extends Iterator[Array[Byte]] {
    private val pickle = new Pickler(/* useMemo = */ true,
      /* valueCompare = */ false)
    private var batch = 1
    private val buffer = new mutable.ArrayBuffer[Any]

    override def hasNext: Boolean = iter.hasNext

    override def next(): Array[Byte] = {
      while (iter.hasNext && buffer.length < batch) {
        buffer += iter.next()
      }
      val bytes = pickle.dumps(buffer.toArray)
      val size = bytes.length
      // let  1M < size < 10M
      if (size < 1024 * 1024) {
        batch *= 2
      } else if (size > 1024 * 1024 * 10 && batch > 1) {
        batch /= 2
      }
      buffer.clear()
      bytes
    }
  }

  /**
   * Convert an RDD of Java objects to an RDD of serialized Python objects, that is usable by
   * PySpark.
   */
  def javaToPython(jRDD: JavaRDD[_]): JavaRDD[Array[Byte]] = {
    jRDD.rdd.mapPartitions { iter => new AutoBatchedPickler(iter) }
  }

  /**
   * Convert an RDD of serialized Python objects to RDD of objects, that is usable by PySpark.
   */
  def pythonToJava(pyRDD: JavaRDD[Array[Byte]], batched: Boolean): JavaRDD[Any] = {
    pyRDD.rdd.mapPartitions { iter =>
      initialize()
      val unpickle = new Unpickler
      iter.flatMap { row =>
        val obj = unpickle.loads(row)
        if (batched) {
          obj match {
            case array: Array[Any] => array.toSeq
            case _ => obj.asInstanceOf[JArrayList[_]].asScala
          }
        } else {
          Seq(obj)
        }
      }
    }.toJavaRDD()
  }

  private def checkPickle(t: (Any, Any)): (Boolean, Boolean) = {
    val pickle = new Pickler(/* useMemo = */ true,
      /* valueCompare = */ false)
    val kt = Try {
      pickle.dumps(t._1)
    }
    val vt = Try {
      pickle.dumps(t._2)
    }
    (kt, vt) match {
      case (Failure(kf), Failure(vf)) =>
        logWarning(s"""
               |Failed to pickle Java object as key: ${t._1.getClass.getSimpleName}, falling back
               |to 'toString'. Error: ${kf.getMessage}""".stripMargin)
        logWarning(s"""
               |Failed to pickle Java object as value: ${t._2.getClass.getSimpleName}, falling back
               |to 'toString'. Error: ${vf.getMessage}""".stripMargin)
        (true, true)
      case (Failure(kf), _) =>
        logWarning(s"""
               |Failed to pickle Java object as key: ${t._1.getClass.getSimpleName}, falling back
               |to 'toString'. Error: ${kf.getMessage}""".stripMargin)
        (true, false)
      case (_, Failure(vf)) =>
        logWarning(s"""
               |Failed to pickle Java object as value: ${t._2.getClass.getSimpleName}, falling back
               |to 'toString'. Error: ${vf.getMessage}""".stripMargin)
        (false, true)
      case _ =>
        (false, false)
    }
  }

  /**
   * Convert an RDD of key-value pairs to an RDD of serialized Python objects, that is usable
   * by PySpark. By default, if serialization fails, toString is called and the string
   * representation is serialized
   */
  def pairRDDToPython(rdd: RDD[(Any, Any)], batchSize: Int): RDD[Array[Byte]] = {
    val (keyFailed, valueFailed) = rdd.take(1) match {
      case Array() => (false, false)
      case Array(first) => checkPickle(first)
    }

    rdd.mapPartitions { iter =>
      val cleaned = iter.map { case (k, v) =>
        val key = if (keyFailed) k.toString else k
        val value = if (valueFailed) v.toString else v
        Array[Any](key, value)
      }
      if (batchSize == 0) {
        new AutoBatchedPickler(cleaned)
      } else {
        val pickle = new Pickler(/* useMemo = */ true,
          /* valueCompare = */ false)
        cleaned.grouped(batchSize).map(batched => pickle.dumps(batched.asJava))
      }
    }
  }

  /**
   * Convert an RDD of serialized Python tuple (K, V) to RDD[(K, V)].
   */
  def pythonToPairRDD[K, V](pyRDD: RDD[Array[Byte]], batched: Boolean): RDD[(K, V)] = {
    def isPair(obj: Any): Boolean = {
      Option(obj.getClass.getComponentType).exists(!_.isPrimitive) &&
        obj.asInstanceOf[Array[_]].length == 2
    }

    val rdd = pythonToJava(pyRDD, batched).rdd
    rdd.take(1) match {
      case Array(obj) if isPair(obj) =>
        // we only accept (K, V)
      case Array() =>
        // we also accept empty collections
      case Array(other) => throw new SparkException(
        s"RDD element of type ${other.getClass.getName} cannot be used")
    }
    rdd.map { obj =>
      val arr = obj.asInstanceOf[Array[_]]
      (arr.head.asInstanceOf[K], arr.last.asInstanceOf[V])
    }
  }
}

相关信息

spark 源码目录

相关文章

spark Py4JServer 源码

spark PythonGatewayServer 源码

spark PythonHadoopUtil 源码

spark PythonPartitioner 源码

spark PythonRDD 源码

spark PythonRunner 源码

spark PythonUtils 源码

spark PythonWorkerFactory 源码

spark WriteInputFormatTestDataGenerator 源码