org.apache.spark.shuffle.ShuffleWriteProcessor.scala Maven / Gradle / Ivy
/*
* 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.shuffle
import org.apache.spark.{Partition, ShuffleDependency, SparkEnv, TaskContext}
import org.apache.spark.internal.Logging
import org.apache.spark.rdd.RDD
import org.apache.spark.scheduler.MapStatus
/**
* The interface for customizing shuffle write process. The driver create a ShuffleWriteProcessor
* and put it into [[ShuffleDependency]], and executors use it in each ShuffleMapTask.
*/
private[spark] class ShuffleWriteProcessor extends Serializable with Logging {
/**
* Create a [[ShuffleWriteMetricsReporter]] from the task context. As the reporter is a
* per-row operator, here need a careful consideration on performance.
*/
protected def createMetricsReporter(context: TaskContext): ShuffleWriteMetricsReporter = {
context.taskMetrics().shuffleWriteMetrics
}
/**
* The write process for particular partition, it controls the life circle of [[ShuffleWriter]]
* get from [[ShuffleManager]] and triggers rdd compute, finally return the [[MapStatus]] for
* this task.
*/
def write(
rdd: RDD[_],
dep: ShuffleDependency[_, _, _],
mapId: Long,
context: TaskContext,
partition: Partition): MapStatus = {
var writer: ShuffleWriter[Any, Any] = null
try {
val manager = SparkEnv.get.shuffleManager
writer = manager.getWriter[Any, Any](
dep.shuffleHandle,
mapId,
context,
createMetricsReporter(context))
writer.write(
rdd.iterator(partition, context).asInstanceOf[Iterator[_ <: Product2[Any, Any]]])
writer.stop(success = true).get
} catch {
case e: Exception =>
try {
if (writer != null) {
writer.stop(success = false)
}
} catch {
case e: Exception =>
log.debug("Could not stop writer", e)
}
throw e
}
}
}