我编写了一些复杂的sparkR脚本，并使用spark-submit运行它。脚本的基本作用是逐行读取基于 hive /黑斑羚的大型拼花地板的表格，并生成具有相同行数的新拼花地板文件。
但是似乎工作在大约100分钟后停止，这似乎有些超时。
我检查了所有已知的值，并测试了100分钟范围。但是找不到任何解决方案。
[user@localhost R]$ time spark-submit sparkr-pre.R
Loading required package: methods
Attaching package: ‘SparkR’
The following objects are masked from ‘package:stats’:
filter, na.omit
The following objects are masked from ‘package:base’:
intersect, rbind, sample, subset, summary, table, transform
15/12/30 18:04:27 WARN MetricsSystem: Using default name DAGScheduler for source because spark.app.id is not set.
[Stage 1:========================================> (7 + 3) / 10]Error in if (returnStatus != 0) { : argument is of length zero
Calls: write.df -> write.df -> .local -> callJMethod -> invokeJava
Execution halted
15/12/30 19:44:52 ERROR InsertIntoHadoopFsRelation: Aborting job.
org.apache.spark.SparkException: Job cancelled because SparkContext was shut down
at org.apache.spark.scheduler.DAGScheduler$$anonfun$cleanUpAfterSchedulerStop$1.apply(DAGScheduler.scala:703)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$cleanUpAfterSchedulerStop$1.apply(DAGScheduler.scala:702)
at scala.collection.mutable.HashSet.foreach(HashSet.scala:79)
at org.apache.spark.scheduler.DAGScheduler.cleanUpAfterSchedulerStop(DAGScheduler.scala:702)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onStop(DAGScheduler.scala:1514)
at org.apache.spark.util.EventLoop.stop(EventLoop.scala:84)
at org.apache.spark.scheduler.DAGScheduler.stop(DAGScheduler.scala:1438)
at org.apache.spark.SparkContext$$anonfun$stop$7.apply$mcV$sp(SparkContext.scala:1724)
at org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1185)
at org.apache.spark.SparkContext.stop(SparkContext.scala:1723)
at org.apache.spark.SparkContext$$anonfun$3.apply$mcV$sp(SparkContext.scala:587)
at org.apache.spark.util.SparkShutdownHook.run(ShutdownHookManager.scala:264)
at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ShutdownHookManager.scala:234)
at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply(ShutdownHookManager.scala:234)
at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply(ShutdownHookManager.scala:234)
at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1699)
at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply$mcV$sp(ShutdownHookManager.scala:234)
at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply(ShutdownHookManager.scala:234)
at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply(ShutdownHookManager.scala:234)
at scala.util.Try$.apply(Try.scala:161)
at org.apache.spark.util.SparkShutdownHookManager.runAll(ShutdownHookManager.scala:234)
at org.apache.spark.util.SparkShutdownHookManager$$anon$2.run(ShutdownHookManager.scala:216)
at org.apache.hadoop.util.ShutdownHookManager$1.run(ShutdownHookManager.java:54)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:567)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1824)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1837)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1914)
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1.apply$mcV$sp(InsertIntoHadoopFsRelation.scala:150)
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1.apply(InsertIntoHadoopFsRelation.scala:108)
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1.apply(InsertIntoHadoopFsRelation.scala:108)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:56)
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation.run(InsertIntoHadoopFsRelation.scala:108)
at org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult$lzycompute(commands.scala:57)
at org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult(commands.scala:57)
at org.apache.spark.sql.execution.ExecutedCommand.doExecute(commands.scala:69)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:140)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:138)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:147)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:138)
at org.apache.spark.sql.SQLContext$QueryExecution.toRdd$lzycompute(SQLContext.scala:933)
at org.apache.spark.sql.SQLContext$QueryExecution.toRdd(SQLContext.scala:933)
at org.apache.spark.sql.execution.datasources.ResolvedDataSource$.apply(ResolvedDataSource.scala:197)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:146)
at org.apache.spark.sql.DataFrame.save(DataFrame.scala:1855)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:497)
at org.apache.spark.api.r.RBackendHandler.handleMethodCall(RBackendHandler.scala:132)
at org.apache.spark.api.r.RBackendHandler.channelRead0(RBackendHandler.scala:79)
at org.apache.spark.api.r.RBackendHandler.channelRead0(RBackendHandler.scala:38)
at io.netty.channel.SimpleChannelInboundHandler.channelRead(SimpleChannelInboundHandler.java:105)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:308)
at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:294)
at io.netty.handler.codec.MessageToMessageDecoder.channelRead(MessageToMessageDecoder.java:103)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:308)
at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:294)
at io.netty.handler.codec.ByteToMessageDecoder.channelRead(ByteToMessageDecoder.java:244)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:308)
at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:294)
at io.netty.channel.DefaultChannelPipeline.fireChannelRead(DefaultChannelPipeline.java:846)
at io.netty.channel.nio.AbstractNioByteChannel$NioByteUnsafe.read(AbstractNioByteChannel.java:131)
at io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:511)
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$2.run(SingleThreadEventExecutor.java:111)
at io.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:137)
at java.lang.Thread.run(Thread.java:745)
15/12/30 19:44:52 ERROR DefaultWriterContainer: Job job_201512301804_0000 aborted.
15/12/30 19:44:52 ERROR RBackendHandler: save on 25 failed
real 100m30.944s
user 1m26.326s
sys 0m19.459s
环境
运行时信息
Name Value
Java Home /usr/java/jdk1.8.0_40/jre
Java Version 1.8.0_40 (Oracle Corporation)
Scala Version version 2.10.4
Spark Properties
Name Value
spark.akka.frameSize 1024
spark.app.id application_1451466100034_0019
spark.app.name SparkR
spark.driver.appUIAddress http://x.x.x.x:4040
spark.driver.host x.x.x.x
spark.driver.maxResultSize 8G
spark.driver.memory 100G
spark.driver.port 60471
spark.executor.id driver
spark.executor.memory 14G
spark.executorEnv.LD_LIBRARY_PATH $LD_LIBRARY_PATH:/usr/lib64/R/lib:/usr/local/lib64:/usr/lib/jvm/jre/lib/amd64/server:/usr/lib/jvm/jre/lib/amd64:/usr/lib/jvm/java/lib/amd64:/usr/java/packages/lib/amd64:/lib:/usr/lib::/usr/lib/hadoop/lib/native
spark.externalBlockStore.folderName spark-b60f685e-c46c-435d-ab1b-c9d1279f630f
spark.fileserver.uri http://x.x.x.x:50281
spark.home /datas/spark-1.5.2-bin-hadoop2.6
spark.kryoserializer.buffer.max 2000M
spark.master yarn-client
spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.param.PROXY_HOSTS CDHPR1.dc.dialog.lk
spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.param.PROXY_URI_BASES http://CDHPR1.dc.dialog.lk:8088/proxy/application_1451466100034_0019
spark.scheduler.mode FIFO
spark.serializer org.apache.spark.serializer.KryoSerializer
spark.sql.parquet.binaryAsString true
spark.submit.deployMode client
spark.ui.filters org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter
spark.yarn.dist.archives file:/datas/spark-1.5.2-bin-hadoop2.6/R/lib/sparkr.zip#sparkr
spark.yarn.dist.files file:/home/inuser/R/sparkr-pre.R
System Properties
Name Value
SPARK_SUBMIT true
SPARK_YARN_MODE true
awt.toolkit sun.awt.X11.XToolkit
file.encoding UTF-8
file.encoding.pkg sun.io
file.separator /
java.awt.graphicsenv sun.awt.X11GraphicsEnvironment
java.awt.printerjob sun.print.PSPrinterJob
java.class.version 52.0
java.endorsed.dirs /usr/java/jdk1.8.0_40/jre/lib/endorsed
java.ext.dirs /usr/java/jdk1.8.0_40/jre/lib/ext:/usr/java/packages/lib/ext
java.home /usr/java/jdk1.8.0_40/jre
java.io.tmpdir /tmp
java.library.path :/usr/lib/hadoop/lib/native:/usr/java/packages/lib/amd64:/usr/lib64:/lib64:/lib:/usr/lib
java.runtime.name Java(TM) SE Runtime Environment
java.runtime.version 1.8.0_40-b26
java.specification.name Java Platform API Specification
java.specification.vendor Oracle Corporation
java.specification.version 1.8
java.vendor Oracle Corporation
java.vendor.url http://java.oracle.com/
java.vendor.url.bug http://bugreport.sun.com/bugreport/
java.version 1.8.0_40
java.vm.info mixed mode
java.vm.name Java HotSpot(TM) 64-Bit Server VM
java.vm.specification.name Java Virtual Machine Specification
java.vm.specification.vendor Oracle Corporation
java.vm.specification.version 1.8
java.vm.vendor Oracle Corporation
java.vm.version 25.40-b25
line.separator
os.arch amd64
os.name Linux
os.version 2.6.32-431.el6.x86_64
path.separator :
sun.arch.data.model 64
sun.boot.class.path /usr/java/jdk1.8.0_40/jre/lib/resources.jar:/usr/java/jdk1.8.0_40/jre/lib/rt.jar:/usr/java/jdk1.8.0_40/jre/lib/sunrsasign.jar:/usr/java/jdk1.8.0_40/jre/lib/jsse.jar:/usr/java/jdk1.8.0_40/jre/lib/jce.jar:/usr/java/jdk1.8.0_40/jre/lib/charsets.jar:/usr/java/jdk1.8.0_40/jre/lib/jfr.jar:/usr/java/jdk1.8.0_40/jre/classes
sun.boot.library.path /usr/java/jdk1.8.0_40/jre/lib/amd64
sun.cpu.endian little
sun.cpu.isalist
sun.io.unicode.encoding UnicodeLittle
sun.java.command org.apache.spark.deploy.SparkSubmit sparkr-pre.R
sun.java.launcher SUN_STANDARD
sun.jnu.encoding UTF-8
sun.management.compiler HotSpot 64-Bit Tiered Compilers
sun.nio.ch.bugLevel
sun.os.patch.level unknown
user.country US
user.dir /home/user/R
user.home /home/user
user.language en
user.name inuser
user.timezone Asia/Colombo
Classpath Entries
Resource Source
/datas/spark-1.5.2-bin-hadoop2.6/conf/ System Classpath
/datas/spark-1.5.2-bin-hadoop2.6/conf/yarn-conf/ System Classpath
/datas/spark-1.5.2-bin-hadoop2.6/lib/datanucleus-api-jdo-3.2.6.jar System Classpath
/datas/spark-1.5.2-bin-hadoop2.6/lib/datanucleus-core-3.2.10.jar System Classpath
/datas/spark-1.5.2-bin-hadoop2.6/lib/datanucleus-rdbms-3.2.9.jar System Classpath
/datas/spark-1.5.2-bin-hadoop2.6/lib/spark-assembly-1.5.2-hadoop2.6.0.jar System Classpath
spark-default.conf
# Default system properties included when running spark-submit.
# This is useful for setting default environmental settings.
# Example:
# spark.master spark://master:7077
# spark.eventLog.enabled true
# spark.eventLog.dir hdfs://namenode:8021/directory
# spark.serializer org.apache.spark.serializer.KryoSerializer
# spark.driver.memory 5g
# spark.executor.extraJavaOptions -XX:+PrintGCDetails -Dkey=value -Dnumbers="one two three"
#
spark.master yarn-client
spark.serializer org.apache.spark.serializer.KryoSerializer
spark.driver.memory 100G
spark.executor.memory 14G
spark.sql.parquet.binaryAsString true
spark.kryoserializer.buffer.max 2000M
spark.driver.maxResultSize 8G
spark.akka.frameSize 1024
#spark.executor.instances 16
我无法在公共(public)场合共享sparkR脚本。真的很抱歉。但是，当代码需要不到100分钟的时间才能完成时，它可以完美地工作。
最佳答案
这是Spark 1.6.0中的一个已知错误，请参阅:https://issues.apache.org/jira/browse/SPARK-12609。快速回顾一下SparkR代码还表明，该错误实际上是从Spark 1.4.0开始存在的。
在他们发布补丁之前，快速而肮脏的解决方案是增加超时。如问题中所述，有问题的函数是connectBackend。该函数可以在运行时使用assignInNamespace进行修补。
以下代码检索原始函数，然后将其包装在第二个函数中，为此我们将超时值增加到48小时。然后，原始函数将被包装器替换。
connectBackend.orig <- getFromNamespace('connectBackend', pos='package:SparkR')
connectBackend.patched <- function(hostname, port, timeout = 3600*48) {
connectBackend.orig(hostname, port, timeout)
}
assignInNamespace("connectBackend", value=connectBackend.patched, pos='package:SparkR')
加载SparkR软件包后放入此代码。
另一种解决方案是修改SparkR代码中的超时并重新编译。有关编译说明，请参见:https://github.com/apache/spark/blob/branch-1.6/R/install-dev.sh
关于hadoop - SparkR作业100分钟超时，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/34584284/