apache-spark - 在本地主机上运行的 Spark BlockManager

Question

我有一个简单的脚本文件，我试图在模仿教程的 spark-shell 中执行 here

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext

sc.stop();

val conf = new SparkConf().setAppName("MyApp").setMaster("mesos://zk://172.24.51.171:2181/mesos").set("spark.executor.uri", "hdfs://172.24.51.171:8020/spark-1.3.0-bin-hadoop2.4.tgz").set("spark.driver.host", "172.24.51.142")

val sc2 = new SparkContext(conf)

val file = sc2.textFile("hdfs://172.24.51.171:8020/input/pg4300.txt")

val errors = file.filter(line => line.contains("ERROR"))

errors.count()

我的namenode和mesos master在172.24.51.171，我的ip地址是172.24.51.142。我将这些行保存到一个文件中，然后我使用以下命令启动:

/opt/spark-1.3.0-bin-hadoop2.4/bin/spark-shell -i WordCount.scala

我的远程执行程序都因类似于以下的错误而死:

15/04/08 14:30:39 ERROR RetryingBlockFetcher: Exception while beginning fetch of 1 outstanding blocks 
java.io.IOException: Failed to connect to localhost/127.0.0.1:48554
    at org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:191)
    at org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:156)
    at org.apache.spark.network.netty.NettyBlockTransferService$$anon$1.createAndStart(NettyBlockTransferService.scala:78)
    at org.apache.spark.network.shuffle.RetryingBlockFetcher.fetchAllOutstanding(RetryingBlockFetcher.java:140)
    at org.apache.spark.network.shuffle.RetryingBlockFetcher.start(RetryingBlockFetcher.java:120)
    at org.apache.spark.network.netty.NettyBlockTransferService.fetchBlocks(NettyBlockTransferService.scala:87)
    at org.apache.spark.network.BlockTransferService.fetchBlockSync(BlockTransferService.scala:89)
    at org.apache.spark.storage.BlockManager$$anonfun$doGetRemote$2.apply(BlockManager.scala:594)
    at org.apache.spark.storage.BlockManager$$anonfun$doGetRemote$2.apply(BlockManager.scala:592)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
    at org.apache.spark.storage.BlockManager.doGetRemote(BlockManager.scala:592)
    at org.apache.spark.storage.BlockManager.getRemoteBytes(BlockManager.scala:586)
    at org.apache.spark.broadcast.TorrentBroadcast$$anonfun$org$apache$spark$broadcast$TorrentBroadcast$$readBlocks$1.org$apache$spark$broadcast$TorrentBroadcast$$anonfun$$getRemote$1(TorrentBroadcast.scala:126)
    at org.apache.spark.broadcast.TorrentBroadcast$$anonfun$org$apache$spark$broadcast$TorrentBroadcast$$readBlocks$1$$anonfun$1.apply(TorrentBroadcast.scala:136)
    at org.apache.spark.broadcast.TorrentBroadcast$$anonfun$org$apache$spark$broadcast$TorrentBroadcast$$readBlocks$1$$anonfun$1.apply(TorrentBroadcast.scala:136)
    at scala.Option.orElse(Option.scala:257)
    at org.apache.spark.broadcast.TorrentBroadcast$$anonfun$org$apache$spark$broadcast$TorrentBroadcast$$readBlocks$1.apply$mcVI$sp(TorrentBroadcast.scala:136)
    at org.apache.spark.broadcast.TorrentBroadcast$$anonfun$org$apache$spark$broadcast$TorrentBroadcast$$readBlocks$1.apply(TorrentBroadcast.scala:119)
    at org.apache.spark.broadcast.TorrentBroadcast$$anonfun$org$apache$spark$broadcast$TorrentBroadcast$$readBlocks$1.apply(TorrentBroadcast.scala:119)
    at scala.collection.immutable.List.foreach(List.scala:318)
    at org.apache.spark.broadcast.TorrentBroadcast.org$apache$spark$broadcast$TorrentBroadcast$$readBlocks(TorrentBroadcast.scala:119)
    at org.apache.spark.broadcast.TorrentBroadcast$$anonfun$readBroadcastBlock$1.apply(TorrentBroadcast.scala:174)
    at org.apache.spark.util.Utils$.tryOrIOException(Utils.scala:1152)
    at org.apache.spark.broadcast.TorrentBroadcast.readBroadcastBlock(TorrentBroadcast.scala:164)
    at org.apache.spark.broadcast.TorrentBroadcast._value$lzycompute(TorrentBroadcast.scala:64)
    at org.apache.spark.broadcast.TorrentBroadcast._value(TorrentBroadcast.scala:64)
    at org.apache.spark.broadcast.TorrentBroadcast.getValue(TorrentBroadcast.scala:87)
    at org.apache.spark.broadcast.Broadcast.value(Broadcast.scala:70)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:58)
    at org.apache.spark.scheduler.Task.run(Task.scala:64)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:203)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
    at java.lang.Thread.run(Thread.java:745)
Caused by: java.net.ConnectException: Connection refused: localhost/127.0.0.1:48554
    at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method)
    at sun.nio.ch.SocketChannelImpl.finishConnect(SocketChannelImpl.java:739)
    at io.netty.channel.socket.nio.NioSocketChannel.doFinishConnect(NioSocketChannel.java:208)
    at io.netty.channel.nio.AbstractNioChannel$AbstractNioUnsafe.finishConnect(AbstractNioChannel.java:287)
    at io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:528)
    at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:468)
    at io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:382)
    at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:354)
    at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:116)
    ... 1 more 

在我运行 errors.count() 命令后会发生此故障。在我的 shell 中，在创建新的 SparkContext 之后，我看到了以下几行:

15/04/08 14:31:18 INFO NettyBlockTransferService: Server created on 48554
15/04/08 14:31:18 INFO BlockManagerMaster: Trying to register BlockManager
15/04/08 14:31:18 INFO BlockManagerMasterActor: Registering block manager localhost:48554 with 265.4 MB RAM, BlockManagerId(<driver>, localhost, 48554)
15/04/08 14:31:18 INFO BlockManagerMaster: Registered BlockManager

我想发生的事情是 Spark 将 BlockManager 的地址记录为 localhost:48554，然后将其发送给所有尝试与其 localhosts:48554 通信的执行程序，而不是端口 48554 上的驱动程序 IP 地址。 为什么 spark 使用 localhost 作为 BlockManager 的地址而不是 spark.driver.host？

附加信息

在 Spark Config有 spark.blockManager.port 但没有 spark.blockManager.host？只有一个 spark.driver.host，你可以看到我在我的 SparkConf 中设置的。

可能与此有关 JIRA Ticket虽然这似乎是一个网络问题。我的网络配置了 DNS 就好了。

Answer 1

您可以尝试在调用 spark-shell 时使用 --master 参数提供 Spark Master 地址(或在 spark-defaults.conf 中添加)。我有一个类似的问题(请参阅我的帖子 Spark Shell Listens on localhost instead of configured IP address )，当在 shell 中动态创建上下文时，BlockManager 似乎在 localhost 上监听。

日志:

使用原始上下文时(监听主机名)
BlockManagerInfo:在 ubuntu64server2 的内存中添加了 broadcast_1_piece0:33301

创建新上下文时(在本地主机上监听)
BlockManagerInfo:在 localhost:40235

的内存中添加了 broadcast_1_piece0

我必须连接到 Cassandra 集群，并且能够通过在 spark-defaults.conf 中提供 spark.cassandra.connection.host 并在 spark shell 中导入包 com.datastax.spark.connector._ 来查询它。