spark LocalRDDCheckpointData 源码

spark LocalRDDCheckpointData 代码

文件路径：/core/src/main/scala/org/apache/spark/rdd/LocalRDDCheckpointData.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.rdd

import scala.reflect.ClassTag

import org.apache.spark.{SparkEnv, TaskContext}
import org.apache.spark.internal.Logging
import org.apache.spark.storage.{RDDBlockId, StorageLevel}
import org.apache.spark.util.Utils

/**
 * An implementation of checkpointing implemented on top of Spark's caching layer.
 *
 * Local checkpointing trades off fault tolerance for performance by skipping the expensive
 * step of saving the RDD data to a reliable and fault-tolerant storage. Instead, the data
 * is written to the local, ephemeral block storage that lives in each executor. This is useful
 * for use cases where RDDs build up long lineages that need to be truncated often (e.g. GraphX).
 */
private[spark] class LocalRDDCheckpointData[T: ClassTag](@transient private val rdd: RDD[T])
  extends RDDCheckpointData[T](rdd) with Logging {

  /**
   * Ensure the RDD is fully cached so the partitions can be recovered later.
   */
  protected override def doCheckpoint(): CheckpointRDD[T] = {
    val level = rdd.getStorageLevel

    // Assume storage level uses disk; otherwise memory eviction may cause data loss
    assume(level.useDisk, s"Storage level $level is not appropriate for local checkpointing")

    // Not all actions compute all partitions of the RDD (e.g. take). For correctness, we
    // must cache any missing partitions. TODO: avoid running another job here (SPARK-8582).
    val action = (tc: TaskContext, iterator: Iterator[T]) => Utils.getIteratorSize(iterator)
    val missingPartitionIndices = rdd.partitions.map(_.index).filter { i =>
      !SparkEnv.get.blockManager.master.contains(RDDBlockId(rdd.id, i))
    }
    if (missingPartitionIndices.nonEmpty) {
      rdd.sparkContext.runJob(rdd, action, missingPartitionIndices)
    }

    new LocalCheckpointRDD[T](rdd)
  }

}

private[spark] object LocalRDDCheckpointData {

  val DEFAULT_STORAGE_LEVEL = StorageLevel.MEMORY_AND_DISK

  /**
   * Transform the specified storage level to one that uses disk.
   *
   * This guarantees that the RDD can be recomputed multiple times correctly as long as
   * executors do not fail. Otherwise, if the RDD is cached in memory only, for instance,
   * the checkpoint data will be lost if the relevant block is evicted from memory.
   *
   * This method is idempotent.
   */
  def transformStorageLevel(level: StorageLevel): StorageLevel = {
    StorageLevel(useDisk = true, level.useMemory, level.deserialized, level.replication)
  }
}

相关信息

spark 源码目录

相关文章

spark AsyncRDDActions 源码

spark BinaryFileRDD 源码

spark BlockRDD 源码

spark CartesianRDD 源码

spark CheckpointRDD 源码

spark CoGroupedRDD 源码

spark CoalescedRDD 源码

spark DoubleRDDFunctions 源码

spark EmptyRDD 源码

spark HadoopRDD 源码