星火BlockManager在本地主机上运行

Question

我有一个简单的脚本文件，我试图在模拟教程这里的星火壳中执行。

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主机在172.24.51.171上，我的ip地址是172.24.51.142。我将这些行保存到一个文件中，然后使用命令启动该文件：

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

我的远程执行程序都会因为类似以下错误而死亡：

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 

这个失败发生在我运行errors.count()命令之后。在shell的前面，在创建新的SparkContext之后，我看到了行：

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

我猜发生了什么，星火正在将BlockManager的地址记录为localhost: 48554，然后发送给所有试图与本地主机对话的执行者:48554，而不是48554端口的驱动程序的ip地址。为什么使用本地主机作为BlockManager的地址而不是spark.driver.host?

更多信息

1. 在火花控制中有一个spark.blockManager.port，但是没有spark.blockManager.host？这里只有一个spark.driver.host，您可以看到我在SparkConf中设置了它。
2. 可能与此JIRA机票有关，尽管这似乎是一个网络问题。我的网络配置了很好的DNS。

Answer 1

在调用火花壳(或添加火花-defaults.conf)时，可以使用--主参数提供Spark地址吗？我也遇到了类似的问题(请参阅我的post Shell监听本地主机而不是配置的IP地址。)，当在shell中动态创建上下文时，BlockManager似乎会监听本地主机。

日志：

• 当使用原始上下文(侦听主机名)时，BlockManagerInfo:在ubuntu64server2:33301上的内存中添加broadcast_1_piece0
• 创建新上下文时(侦听本地主机)，BlockManagerInfo:在localhost上的内存中添加broadcast_1_piece0 :40235

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

Answer 2

尝试通过SPARK_LOCAL_IP对象设置spark.local.ip (在命令行上)或设置spark.local.ip。