Apache纱线模式启动时间过长(10+ secs)

Question

我运行的火花应用程序的纱线-客户端或纱线-集群模式。

但似乎要花很长时间才能启动。

初始化星火上下文需要10+秒。

这是正常的吗？或者它能被优化？

环境情况如下：

• Hadoop: Hortonworks HDP 2.2 (Hadoop2.6)(带有3个数据节点的小型测试集群)
• 火花: 1.3.1
• Client: Windows 7，但在CentOS 6.6上类似的结果

下面是应用程序日志的启动部分。(编辑了一些私人资料)

第一行的‘MainProcessor:初始化上下文’和最后一行的‘MainProcessor:删除以前的输出文件’是应用程序的日志。其他在两者之间的是火花本身。在显示此日志之后执行应用程序逻辑。

15/05/07 09:18:31 INFO Main: Initializing context
15/05/07 09:18:31 INFO SparkContext: Running Spark version 1.3.1
15/05/07 09:18:31 INFO SecurityManager: Changing view acls to: myuser,myapp
15/05/07 09:18:31 INFO SecurityManager: Changing modify acls to: myuser,myapp
15/05/07 09:18:31 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(myuser, myapp); users with modify permissions: Set(myuser, myapp)
15/05/07 09:18:31 INFO Slf4jLogger: Slf4jLogger started
15/05/07 09:18:31 INFO Remoting: Starting remoting
15/05/07 09:18:31 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkDriver@mymachine:54449]
15/05/07 09:18:31 INFO Utils: Successfully started service 'sparkDriver' on port 54449.
15/05/07 09:18:31 INFO SparkEnv: Registering MapOutputTracker
15/05/07 09:18:32 INFO SparkEnv: Registering BlockManagerMaster
15/05/07 09:18:32 INFO DiskBlockManager: Created local directory at C:\Users\myuser\AppData\Local\Temp\spark-2d3db9d6-ea78-438e-956f-be9c1dcf3a9d\blockmgr-e9ade223-a4b8-4d9f-b038-efd66adf9772
15/05/07 09:18:32 INFO MemoryStore: MemoryStore started with capacity 1956.7 MB
15/05/07 09:18:32 INFO HttpFileServer: HTTP File server directory is C:\Users\myuser\AppData\Local\Temp\spark-ff40d73b-e8ab-433e-88c4-35da27fb6278\httpd-def9220f-ac3a-4dd2-9ac1-2c593b94b2d9
15/05/07 09:18:32 INFO HttpServer: Starting HTTP Server
15/05/07 09:18:32 INFO Server: jetty-8.y.z-SNAPSHOT
15/05/07 09:18:32 INFO AbstractConnector: Started SocketConnector@0.0.0.0:54450
15/05/07 09:18:32 INFO Utils: Successfully started service 'HTTP file server' on port 54450.
15/05/07 09:18:32 INFO SparkEnv: Registering OutputCommitCoordinator
15/05/07 09:18:32 INFO Server: jetty-8.y.z-SNAPSHOT
15/05/07 09:18:32 INFO AbstractConnector: Started SelectChannelConnector@0.0.0.0:4040
15/05/07 09:18:32 INFO Utils: Successfully started service 'SparkUI' on port 4040.
15/05/07 09:18:32 INFO SparkUI: Started SparkUI at http://mymachine:4040
15/05/07 09:18:32 INFO SparkContext: Added JAR file:/D:/Projects/MyApp/MyApp.jar at http://10.111.111.199:54450/jars/MyApp.jar with timestamp 1430957912240
15/05/07 09:18:32 INFO RMProxy: Connecting to ResourceManager at cluster01/10.111.111.11:8050
15/05/07 09:18:32 INFO Client: Requesting a new application from cluster with 3 NodeManagers
15/05/07 09:18:32 INFO Client: Verifying our application has not requested more than the maximum memory capability of the cluster (23040 MB per container)
15/05/07 09:18:32 INFO Client: Will allocate AM container, with 896 MB memory including 384 MB overhead
15/05/07 09:18:32 INFO Client: Setting up container launch context for our AM
15/05/07 09:18:32 INFO Client: Preparing resources for our AM container
15/05/07 09:18:32 INFO Client: Source and destination file systems are the same. Not copying hdfs://cluster01/apps/spark/spark-assembly-1.3.1-hadoop2.6.0.jar
15/05/07 09:18:32 INFO Client: Setting up the launch environment for our AM container
15/05/07 09:18:33 INFO SecurityManager: Changing view acls to: myuser,myapp
15/05/07 09:18:33 INFO SecurityManager: Changing modify acls to: myuser,myapp
15/05/07 09:18:33 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(myuser, myapp); users with modify permissions: Set(myuser, myapp)
15/05/07 09:18:33 INFO Client: Submitting application 2 to ResourceManager
15/05/07 09:18:33 INFO YarnClientImpl: Submitted application application_1430956687773_0002
15/05/07 09:18:34 INFO Client: Application report for application_1430956687773_0002 (state: ACCEPTED)
15/05/07 09:18:34 INFO Client: 
     client token: N/A
     diagnostics: N/A
     ApplicationMaster host: N/A
     ApplicationMaster RPC port: -1
     queue: default
     start time: 1430957906540
     final status: UNDEFINED
     tracking URL: http://cluster01:8088/proxy/application_1430956687773_0002/
     user: myapp
15/05/07 09:18:35 INFO Client: Application report for application_1430956687773_0002 (state: ACCEPTED)
15/05/07 09:18:36 INFO Client: Application report for application_1430956687773_0002 (state: ACCEPTED)
15/05/07 09:18:37 INFO Client: Application report for application_1430956687773_0002 (state: ACCEPTED)
15/05/07 09:18:37 INFO YarnClientSchedulerBackend: ApplicationMaster registered as Actor[akka.tcp://sparkYarnAM@cluster02:39698/user/YarnAM#-1579648782]
15/05/07 09:18:37 INFO YarnClientSchedulerBackend: Add WebUI Filter. org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter, Map(PROXY_HOSTS -> cluster01, PROXY_URI_BASES -> http://cluster01:8088/proxy/application_1430956687773_0002), /proxy/application_1430956687773_0002
15/05/07 09:18:37 INFO JettyUtils: Adding filter: org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter
15/05/07 09:18:38 INFO Client: Application report for application_1430956687773_0002 (state: RUNNING)
15/05/07 09:18:38 INFO Client: 
     client token: N/A
     diagnostics: N/A
     ApplicationMaster host: cluster02
     ApplicationMaster RPC port: 0
     queue: default
     start time: 1430957906540
     final status: UNDEFINED
     tracking URL: http://cluster01:8088/proxy/application_1430956687773_0002/
     user: myapp
15/05/07 09:18:38 INFO YarnClientSchedulerBackend: Application application_1430956687773_0002 has started running.
15/05/07 09:18:38 INFO NettyBlockTransferService: Server created on 54491
15/05/07 09:18:38 INFO BlockManagerMaster: Trying to register BlockManager
15/05/07 09:18:38 INFO BlockManagerMasterActor: Registering block manager mymachine:54491 with 1956.7 MB RAM, BlockManagerId(<driver>, mymachine, 54491)
15/05/07 09:18:38 INFO BlockManagerMaster: Registered BlockManager
15/05/07 09:18:43 INFO YarnClientSchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@cluster02:44996/user/Executor#-786778979] with ID 1
15/05/07 09:18:43 INFO YarnClientSchedulerBackend: SchedulerBackend is ready for scheduling beginning after reached minRegisteredResourcesRatio: 0.8
15/05/07 09:18:43 INFO MainProcessor: Deleting previous output files

谢谢。

更新

我想我已经找到了(可能是部分的，但主要的)原因。

它介于以下几个字之间：

15/05/08 11:36:32 INFO BlockManagerMaster: Registered BlockManager
15/05/08 11:36:38 INFO YarnClientSchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@cluster04:55237/user/Executor#-149550753] with ID 1

当我在集群端读取日志时，发现了以下几行：(上面一行的确切时间不同，但这是机器之间的区别)

15/05/08 11:36:23 INFO yarn.ApplicationMaster: Started progress reporter thread - sleep time : 5000
15/05/08 11:36:28 INFO impl.AMRMClientImpl: Received new token for : cluster04:45454

似乎火花故意睡了5秒。

我已经阅读了星火的源代码，在org.apache.spark.deploy.yarn.ApplicationMaster.scala，中，launchReporterThread()有相应的代码。它循环调用allocator.allocateResources()和Thread.sleep()。对于睡眠，它读取配置变量scapk.yarn.Scheder.Hearbeat.Interval-ms(默认值为5000，即5秒)。根据评论，“我们希望在不引起太多请求的情况下对RM做出合理的响应”。因此，除非纱线立即满足分配要求，否则将浪费5秒。

当我将配置变量修改为1000时，它只等待了1秒。

以下是更改后的日志行：

15/05/08 11:47:21 INFO yarn.ApplicationMaster: Started progress reporter thread - sleep time : 1000
15/05/08 11:47:22 INFO impl.AMRMClientImpl: Received new token for : cluster04:45454

节省了4秒。

因此，当不想等待5秒时，可以更改spark.yarn.scheduler.heartbeat.interval-ms.

我希望它所带来的额外费用将是微不足道的。

更新

联合爱尔兰共和军的一个相关问题已经开始并得到解决。请参阅https://issues.apache.org/jira/browse/SPARK-7533

Answer 1

这是很典型的。我的系统运行火花提交大约20秒，直到得到一个SparkContext。

正如它在文档中提到的那样，解决方案是将您的驱动程序转换为RPC服务器。这样您就可以初始化一次，然后其他应用程序就可以使用驱动程序的上下文作为服务。

我正在用我的申请做这件事。我正在使用http4s并将我的驱动程序转换成一个web服务器。

Answer 2

为了快速创建火花上下文

在电子病历上测试：

1. cd /usr/lib/spark/jars/; zip /tmp/yarn-archive.zip *.jar
2. cd path/to/folder/of/someOtherDependancy/jarFolder/; zip /tmp/yarn-archive.zip jar-file.jar
3. 用于测试完整性和详细调试的zip -Tv /tmp/yarn-archive.zip
4. 如果yarn-archive.zip已经存在于hdfs上，那么hdfs dfs -rm -r -f -skipTrash /user/hadoop/yarn-archive.zip hdfs dfs -put /tmp/yarn-archive.zip /user/hadoop/ hdfs dfs -put /tmp/yarn-archive.zip /user/hadoop/
5. --conf spark.yarn.archive="hdfs:///user/hadoop/yarn-archive.zip"在spark-submit中使用此参数

这样做的原因是，主人不必把所有的罐子都分发给奴隶。它可以从一些常见的hdfs路径中获得，这里是hdfs:///user/hadoop/yarn-archive.zip。

我意识到它可以节省您的时间3-5秒，这一次也取决于集群中节点的数量。节点越多，节省的时间就越多。

Answer 3

您可以检查Apache，它是Spark前面的REST。

• http://livy.io/
• https://github.com/cloudera/livy

您可以使用一个会话和多个请求来处理这个Spark/Livy会话。