《4.1 Executor 端长时容错详解.md》讨论区

[lw-lin]

这里是 《4.1 Executor 端长时容错详解.md》 讨论区。

如需要贴代码，请复制以下内容并修改：

public static final thisIsJavaCode;

val thisIsScalaCode

谢谢！

[luphappy]

您好，我想问一下，如果要是想catch spark Exception，应该怎么catch JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, batchInterval);

    JavaPairInputDStream<String, String> logs = KafkaUtils
    .createDirectStream(jssc, String.class, String.class,
            StringDecoder.class, StringDecoder.class, kafkaParams,
            topicsSet);

     dotask()

这个应该怎么catch才能让application不退出呢

[bhlx3lyx7]

您好，我想问下，文章中关于 细粒度忽略 的修改原生Spark Streaming的代码在哪里呀

[Adamyuanyuan]

您好，感谢编辑这么优秀的解析系列，今天我也看到Structured Streaming 更新了，本节的“WriteAheadLog 框架“部分的第一张图：“Spark Streaming 里的 WAL 框架图”应该不对，请问是笔误吗？我在网上找了一张类似的，是否可用？

[lw-lin]

@Adamyuanyuan

确实第一张图不对。你找的这张图（Spark 1.2 版本）结构上是正确的，除了 Spark 1.2 以来有些类的名字对不上了需要修改下 —— 稍后我把图给更新一下。Thanks for bringing this up!

[bjkonglu]

@lw-lin 你在你的文章中提到细粒度的忽略处理，你们是怎么修改的，很好奇，想借阅一下你们修改的部分。谢谢！

[lw-lin]

@bjkonglu

主要是修改 BlockRDD.scala，修改后整个文件源码如下：

/*
 * 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._
import org.apache.spark.storage.{BlockId, BlockManager, StreamBlockId}
import scala.Some

private[spark] class BlockRDDPartition(val blockId: BlockId, idx: Int) extends Partition {
  val index = idx
}

private[spark]
class BlockRDD[T: ClassTag](sc: SparkContext, @transient val blockIds: Array[BlockId])
  extends RDD[T](sc, Nil) {

  @transient lazy val _locations = BlockManager.blockIdsToHosts(blockIds, SparkEnv.get)
  @volatile private var _isValid = true

  override def getPartitions: Array[Partition] = {
    assertValid()
    (0 until blockIds.length).map { i =>
      new BlockRDDPartition(blockIds(i), i).asInstanceOf[Partition]
    }.toArray
  }

  override def compute(split: Partition, context: TaskContext): Iterator[T] = {
    assertValid()
    val blockManager = SparkEnv.get.blockManager
    val blockId = split.asInstanceOf[BlockRDDPartition].blockId

    //=========================modification begin ======================
    val isStreamBlock = blockId.isInstanceOf[StreamBlockId]
    val enableBlockRddMissingIgnoreFeature =
      SparkEnv.get.conf.getBoolean("spark._.enableBlockRddMissingIgnoreFeature" , false)
    val defaultTaskRetryNum =
      SparkEnv.get.conf.getInt("spark.task.maxFailures" , 4)
    val maxRetryNum =
      SparkEnv.get.conf.getInt("spark._.blockRddMissingMaxRetryNum" , 4).min(defaultTaskRetryNum)
    val attemptTime = context.attemptNumber()
    val shouldIgnore = attemptTime >= maxRetryNum - 1
    //=========================modification  end ======================
    blockManager.get(blockId) match {
      case Some(block) => block.data.asInstanceOf[Iterator[T]]
      case None =>
        //=========================modification begin ======================
        if (enableBlockRddMissingIgnoreFeature && isStreamBlock && shouldIgnore) {
          logError("block missing : " + blockId)
          Seq().iterator
        } else {
          throw new Exception(s"Could not compute split, block $blockId of RDD $id not found")
        }
      //=========================modification  end ======================
    }
  }

  override def getPreferredLocations(split: Partition): Seq[String] = {
    assertValid()
    _locations(split.asInstanceOf[BlockRDDPartition].blockId)
  }

  /**
   * Remove the data blocks that this BlockRDD is made from. NOTE: This is an
   * irreversible operation, as the data in the blocks cannot be recovered back
   * once removed. Use it with caution.
   */
  private[spark] def removeBlocks() {
    blockIds.foreach { blockId =>
      sparkContext.env.blockManager.master.removeBlock(blockId)
    }
    _isValid = false
  }

  /**
   * Whether this BlockRDD is actually usable. This will be false if the data blocks have been
   * removed using `this.removeBlocks`.
   */
  private[spark] def isValid: Boolean = {
    _isValid
  }

  /** Check if this BlockRDD is valid. If not valid, exception is thrown. */
  private[spark] def assertValid() {
    if (!isValid) {
      throw new SparkException(
        "Attempted to use %s after its blocks have been removed!".format(toString))
    }
  }

  protected def getBlockIdLocations(): Map[BlockId, Seq[String]] = {
    _locations
  }
}