在Hadoop 2.3上运行C++程序各种疑难杂症（Hadoop Pipes选择、错误集锦、Hadoop2.3编译等）

时间：2022-02-07 04:03:25

首记

感觉Hadoop是一个坑，打着大数据最佳解决方案的旗帜到处坑害良民。记得以前看过一篇文章，说1TB以下的数据就不要用Hadoop了，体现不 出太大的优势，有时候反而会成为累赘。因此Hadoop的使用场所一般有两：一是有一定规模的公司，数据流一般是TB级别的，这样的公司其实不多；二是各 大高校的实验室，作为研究使用。不幸的我也走上了这条路，仅为研究之用。而且我的使用需求还不是一般的在Hadoop下开发应用程序，而是开发好的C++ 程序要放到Hadoop平台下进行测试。Hadoop是基于Java的数据计算平台，当然对Java支持的最好，如果要运行C++程序，有三种解决方案：

  • 使用JNI/JNA/JNR技术。这三种Java外部函数接口技术都是解决在Java程序中运行C++功能函数的需求，从而使得在Hadoop平 台下开发Java程序且能调用C++函数完成在Hadoop Java版应用中运行C++程序的目的。早期（大概11年）阿里就是使用该技术将C语言实现 的分词软件成功部署到Hadoop平台下运行，详情请看参考资料1。用过JNI的都知道JNI实在不好用，所以后来有人开发了另外两种Java外部函数接口，即JNA和JNR，具体介绍和使用实例请看参考资料2和参考资料3。
  • 使 用Hadoop Streaming技术。这项技术可以使得除了Java之外的多种其它语言如C/C++/Python/C#甚至shell脚本等运行在 Hadoop平台下，程序只需要按照一定的格式从标准输入读取数据、向标准输出写数据就可以在Hadoop平台上使用，原有的单机程序稍加改动就可以在 Hadoop平台进行分布式处理。
  • 使用Hadoop Pipes技术。该技术只专注于在Hadoop平台下运行C++程序，只允许用户 使用C++语言进行MapReduce程序设计。它采用的主要方法是将应用逻辑相关的C++代码放在单独的进程中，然后通过Socket让Java代码与 C++代码通信。从很大程度上说，这种方法类似于Hadoop Streaming，不同之处是通信方式不同：一个是标准输入输出，另一个是socket。

对于这三种技术我都做了相关调研和比较分析，首先排除了方法1，因为我不想写Java程序来调用C++功能，有些累赘，调试非常不方便，那么是用Hadoop Streaming技术还是Hadoop Pipes技术呢？两种方式各有优缺点，具体可看参考资料4（说的不太准确，仅供参考），为了准确选择，我需要将两种方法都试验一下，看哪个适合自己的需求再做最终决定。

首先选择了仅专注C++的Hadoop Pipes技术，于是就开启了下面一系列的过程……

Hadoop 2.3环境配置安装

我之前配置过Hadoop环境（看参考资料5），但那时用的版本是1.1.2，比较老的版本，一堆bug，为了避免遗留bug的困扰我选择了最新版2.3，因此需要重新配置（主要借鉴参考资料6），由于2.3版本使用的是新MapReduce框架yarn，因此配置与之前有所差异。

配置成功后，用自带的经典案例wordcount测试下运行是否正常（已上传数据）：

hadoop jar ./hadoop-2.3./share/hadoop/mapreduce/hadoop-mapreduce-examples-2.3..jar wordcount wc_input.txt out

结果出现了下面错误：

-- ::, FATAL org.apache.hadoop.yarn.server.nodemanager.NodeManager: Error starting NodeManager

org.apache.hadoop.yarn.exceptions.YarnRuntimeException: java.net.ConnectException: Call From Slave1/192.168.1.152 to 0.0.0.0:8031 failed on connection exception: java.net.ConnectException: Connection refused; For more details see: http://wiki.apache.org/hadoop/ConnectionRefused

 at org.apache.hadoop.yarn.server.nodemanager.NodeStatusUpdaterImpl.serviceStart(NodeStatusUpdaterImpl.java:)

 at org.apache.hadoop.service.AbstractService.start(AbstractService.java:)

 at org.apache.hadoop.service.CompositeService.serviceStart(CompositeService.java:)

 at org.apache.hadoop.yarn.server.nodemanager.NodeManager.serviceStart(NodeManager.java:)

 at org.apache.hadoop.service.AbstractService.start(AbstractService.java:)

 at org.apache.hadoop.yarn.server.nodemanager.NodeManager.initAndStartNodeManager(NodeManager.java:)

 at org.apache.hadoop.yarn.server.nodemanager.NodeManager.main(NodeManager.java:)

Caused by: java.net.ConnectException: Call From Slave1/192.168.1.152 to 0.0.0.0: failed on connection exception: java.net.ConnectException: Connection refused; For more details see: http://wiki.apache.org/hadoop/ConnectionRefused

 at sun.reflect.GeneratedConstructorAccessor8.newInstance(Unknown Source)

 at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:)

 at java.lang.reflect.Constructor.newInstance(Constructor.java:)

 at org.apache.hadoop.net.NetUtils.wrapWithMessage(NetUtils.java:)

 at org.apache.hadoop.net.NetUtils.wrapException(NetUtils.java:)

 at org.apache.hadoop.ipc.Client.call(Client.java:)

 at org.apache.hadoop.ipc.Client.call(Client.java:)

 at org.apache.hadoop.ipc.ProtobufRpcEngine$Invoker.invoke(ProtobufRpcEngine.java:)

 at com.sun.proxy.$Proxy23.registerNodeManager(Unknown Source)

 at org.apache.hadoop.yarn.server.api.impl.pb.client.ResourceTrackerPBClientImpl.registerNodeManager(ResourceTrackerPBClientImpl.java:)

 at sun.reflect.GeneratedMethodAccessor2.invoke(Unknown Source)

 at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:)

 at java.lang.reflect.Method.invoke(Method.java:)

 at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:)

 at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:)

 at com.sun.proxy.$Proxy24.registerNodeManager(Unknown Source)

 at org.apache.hadoop.yarn.server.nodemanager.NodeStatusUpdaterImpl.registerWithRM(NodeStatusUpdaterImpl.java:)

 at org.apache.hadoop.yarn.server.nodemanager.NodeStatusUpdaterImpl.serviceStart(NodeStatusUpdaterImpl.java:)

 ...  more

Caused by: java.net.ConnectException: Connection refused

 at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method)

 at sun.nio.ch.SocketChannelImpl.finishConnect(SocketChannelImpl.java:)

 at org.apache.hadoop.net.SocketIOWithTimeout.connect(SocketIOWithTimeout.java:)

 at org.apache.hadoop.net.NetUtils.connect(NetUtils.java:)

 at org.apache.hadoop.net.NetUtils.connect(NetUtils.java:)

 at org.apache.hadoop.ipc.Client$Connection.setupConnection(Client.java:)

 at org.apache.hadoop.ipc.Client$Connection.setupIOstreams(Client.java:)

 at org.apache.hadoop.ipc.Client$Connection.access$(Client.java:)

 at org.apache.hadoop.ipc.Client.getConnection(Client.java:)

 at org.apache.hadoop.ipc.Client.call(Client.java:)

 ...  more

是连接拒绝问题，连接了“0.0.0.0:8031”这个地址，这显然是某个选项没配置连接地址，导致使用了默认的错误地址。经过查资料，是yarn-site.xml文件没配置好，其中的yarn.nodemanager.address没有配置，默认是0.0.0.0。正确的配置如下（可能有些项不需要配置，但为了保险还是大部分都配置了）

<configuration>

<property>

　　<name>yarn.resourcemanager.address</name>

　　<value>192.168.1.137:</value>

</property>

<property>

　　<name>yarn.resourcemanager.scheduler.address</name>

　　<value>192.168.1.137:</value>

</property>

<property>

　　<name>yarn.resourcemanager.resource-tracker.address</name>

　　<value>192.168.1.137:8031</value>

</property>

<property>

　　<name>yarn.resourcemanager.admin.address</name>

　　<value>192.168.1.137:</value>

</property>

<property>

       <name>yarn.resourcemanager.webapp.address</name>

　　<value>192.168.1.137:</value>

</property>

<property>

    <name>yarn.nodemanager.aux-services</name>

    <value>mapreduce_shuffle</value>

</property>

<property>

    <name>yarn.nodemanager.aux-services.mapreduce.shuffle.class</name>

    <value>org.apache.hadoop.mapred.ShuffleHandler</value>

</property>

</configuration>

然后就运行成功了。开始以为环境就这样配好了，直到我测试Hadoop pipes程序……

Hadoop pipes运行错误集锦

Hadoop 2.3发布版本没有自带Hadoop pipes的wordcount例子，于是我就在老的1.1.2版本中找来了wordcount-simple.cc例子，修改了头文件路径（新版本与旧版本路径完全不一样），并参考资料7使用下面的makefile编译（其中的-lssl也许非必需）：

HADOOP_INSTALL=/opt/hadoop

CC = g++

CCFLAGS = -I$(HADOOP_INSTALL)/include

wordcount :wordcount-simple.cc

        $(CC) $(CCFLAGS) $< -Wall -L$(HADOOP_INSTALL)/lib/native -lhadooppipes -lhadooputils -lpthread -lcrypto -lssl -g -O2 -o $@

编译成功后，上传到HDFS上，用下面的命令测试运行：

hadoop pipes -D hadoop.pipes.java.recordreader=true -D hadoop.pipes.java.recordwriter=true  \

                       -D mapred.job.name=wordcount -input /data/wc_in -output /data/wc_out2  -program /bin/wordcount

开始一直卡在”map 0% reduce 0%“，不知道过了多久，报出了下面一系列错误：

DEPRECATED: Use of this script to execute mapred command is deprecated.

Instead use the mapred command for it.

// :: INFO client.RMProxy: Connecting to ResourceManager at /192.168.1.137:

// :: INFO client.RMProxy: Connecting to ResourceManager at /192.168.1.137:

// :: WARN mapreduce.JobSubmitter: No job jar file set. User classes may not be found. See Job or Job#setJar(String).

// :: INFO mapred.FileInputFormat: Total input paths to process :

// :: INFO mapreduce.JobSubmitter: number of splits:

// :: INFO Configuration.deprecation: hadoop.pipes.java.recordreader is deprecated. Instead, use mapreduce.pipes.isjavarecordreader

// :: INFO Configuration.deprecation: mapred.job.name is deprecated. Instead, use mapreduce.job.name

// :: INFO Configuration.deprecation: hadoop.pipes.java.recordwriter is deprecated. Instead, use mapreduce.pipes.isjavarecordwriter

// :: INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1396578697573_0004

// :: INFO mapred.YARNRunner: Job jar is not present. Not adding any jar to the list of resources.

// :: INFO impl.YarnClientImpl: Submitted application application_1396578697573_0004

// :: INFO mapreduce.Job: The url to track the job: http://Master:8088/proxy/application_1396578697573_0004/

// :: INFO mapreduce.Job: Running job: job_1396578697573_0004

// :: INFO mapreduce.Job: Job job_1396578697573_0004 running in uber mode : false

// :: INFO mapreduce.Job: map 0% reduce 0%

14/04/04 00:10:53 INFO mapreduce.Job: map 100% reduce 0%

14/04/04 00:10:53 INFO mapreduce.Job: Task Id : attempt_1396578697573_0004_m_000001_0, Status : FAILED

AttemptID:attempt_1396578697573_0004_m_000001_0 Timed out after 600 secs

// :: INFO mapreduce.Job: Task Id : attempt_1396578697573_0004_m_000000_0, Status : FAILED

AttemptID:attempt_1396578697573_0004_m_000000_0 Timed out after  secs

// :: INFO mapreduce.Job: map % reduce %

// :: INFO mapreduce.Job: map % reduce %

// :: INFO mapreduce.Job: Task Id : attempt_1396578697573_0004_m_000000_1, Status : FAILED

AttemptID:attempt_1396578697573_0004_m_000000_1 Timed out after  secs

// :: INFO mapreduce.Job: Task Id : attempt_1396578697573_0004_m_000001_1, Status : FAILED

AttemptID:attempt_1396578697573_0004_m_000001_1 Timed out after  secs

// :: INFO mapreduce.Job: map % reduce %

// :: INFO mapreduce.Job: Task Id : attempt_1396578697573_0004_m_000000_2, Status : FAILED

AttemptID:attempt_1396578697573_0004_m_000000_2 Timed out after  secs

// :: INFO mapreduce.Job: Task Id : attempt_1396578697573_0004_m_000001_2, Status : FAILED

AttemptID:attempt_1396578697573_0004_m_000001_2 Timed out after  secs

// :: INFO mapreduce.Job: map % reduce %

// :: INFO mapreduce.Job: map % reduce %

// :: INFO mapreduce.Job: Job job_1396578697573_0004 failed with state FAILED due to: Task failed task_1396578697573_0004_m_000000

Job failed as tasks failed. failedMaps: failedReduces:

// :: INFO mapreduce.Job: Counters:

 Job Counters

  Failed map tasks=

  Launched map tasks=

  Other local map tasks=

  Data-local map tasks=

  Total time spent by all maps in occupied slots (ms)=

  Total time spent by all reduces in occupied slots (ms)=

  Total time spent by all map tasks (ms)=

  Total vcore-seconds taken by all map tasks=

  Total megabyte-seconds taken by all map tasks=

Exception in thread "main" java.io.IOException: Job failed!

 at org.apache.hadoop.mapred.JobClient.runJob(JobClient.java:)

 at org.apache.hadoop.mapred.pipes.Submitter.runJob(Submitter.java:)

 at org.apache.hadoop.mapred.pipes.Submitter.run(Submitter.java:)

 at org.apache.hadoop.mapred.pipes.Submitter.main(Submitter.java:)

通过网上各种资料（之所以在网上找解决方案是因为在namenode、datanode及yarn的日志里都没能找到错误信息及有效信息，所以很不解到底 是什么原因导致的错误），解决方案都不靠谱，都没能解决该问题。最后自己一阵捣鼓，最终在一个很隐蔽的日志里看到了错误提示，该日志在datanode的Hadoop安装路径下logs目录下的userlogs文 件夹里，开始以为这是个无用的文件夹，后来发现这个文件夹解决了我所有的问题，该文件夹下面包含了很多application子文件夹，而子文件夹下面又 包含了很多container子文件夹，container下面就是标准的三个输出日志”stderr“、”stdout“和”syslog“，也就是这 样的目录结构（datanode的container logs）：

|-- Hadoop安装路径

|-- logs

|- -userlogs

|-- application_XXXXXXXX

|-- container_XXXXXXXX

   |-- stderr

|-- stdout

|-- syslog

其中stderr是最重要的文件，具体的错误原因就保存在这个文件里面。比如上面的错误信息在该文件里找到了这样的错误提示（...表示省略无关信息）：

.../application_1396607014314_0001/container_1396607014314_0001_01_000002/wordcount:

error while loading shared libraries: libcrypto.so.10: cannot open shared object file: No such file or directory

在datanode上用”locate libcrypto.so.10“搜了下的确没找到，但在namenode上找到了这个共享库文件，于是就将 namenode下的该文件拷贝到了datanode的/usr/lib下面，重新运行程序，还是同样的问题，卡住了最后报出了那样的错误，于是到 datanode下面的container logs里找原因，发现了新的问题：

/usr/lib/libstdc++.so.6: version `GLIBCXX_3.4.11' not found (required by .../wordcount)

同样的还是namenode里有而datanode里没有，因此还是将namenode的libstdc++.so.6拷过去了，再次运行，错误依旧，这时原因是：

error while loading shared libraries: /usr/lib/libstdc++.so.6: ELF file OS ABI invalid

这时候我没有去搜相应的解决方案，我似乎知道了问题的症结所在，每次问题都是datanode没有相关的库文件而namenode都有，将namenode上的拷过去还可能造成与系统不匹配无效的后果，那么问题就应该出在：namenode(master)和datanode(slave)云节点系统内核的不匹配。 我的master节点是Ubuntu 13.04而slave节点是特别旧的Ubuntu 9.11，虽然都是Ubuntu但是内核已经不一样，各个库文件已经得到升级和改动，而Hadoop在运行程序时需要master和slave之间的通信 互动，采用的请求机制是一样的，比如master在请求一个动作时用到了libcrypto.so.10这个版本的库文件，那么slave答复或请求 master时也会去自己的系统上找这样的库文件，结果自己的系统上没有，根据Hadoop运行机制就有个timeout和继续重新请求的过程，但是一直 请求不到，所以就一直卡在那个地方，直到最终在限定的时间里没有完成任务导致的”Job failed“。可想而知，如果你master用的是 Ubuntu而slave用的是Fedora，那么这样的错误也非常可能发生。那么为什么之前运行普通的Java版wordcount程序没有发生错误 呢？这我就不清楚了，毕竟不知道其具体的内部运行机理。我猜测可能是Hadoop pipes采用的是socket通信方式，所以需要用到那些库文件，而 普通的Java版程序不需要这样的过程，因此没有发生错误，当然也不排除运行稍复杂的涉及通信的Java程序也发生这样的错误。

因此，我也得出这样一个结论：

Hadoop环境的master节点和所有slave节点的系统环境最好一模一样（同一个系统同一个版本号），对于Hadoop pipes而言就是必须一模一样。

由于我用的都是虚拟机系统，因此需要环境一样的最简单方式就是——克隆（clone）虚拟机，使用链接link方式即可。然后重新配置Hadoop 2.3环境，重新运行，以为问题就此解决，可是新的问题又来了：

// :: INFO mapreduce.Job: Running job: job_1396756477966_0002

// :: INFO mapreduce.Job: Job job_1396756477966_0002 running in uber mode : false

// :: INFO mapreduce.Job: map 0% reduce 0%

14/04/04 09:59:02 INFO mapreduce.Job: Task Id : attempt_1396618478715_0002_m_000000_0, Status : FAILED

Error: java.io.IOException

 at org.apache.hadoop.mapred.pipes.OutputHandler.waitForAuthentication(OutputHandler.java:186)

 at org.apache.hadoop.mapred.pipes.Application.waitForAuthentication(Application.java:195)

 at org.apache.hadoop.mapred.pipes.Application.<init>(Application.java:)

 at org.apache.hadoop.mapred.pipes.PipesMapRunner.run(PipesMapRunner.java:)

 at org.apache.hadoop.mapred.MapTask.runOldMapper(MapTask.java:)

 at org.apache.hadoop.mapred.MapTask.run(MapTask.java:)

 at org.apache.hadoop.mapred.YarnChild$.run(YarnChild.java:)

 at java.security.AccessController.doPrivileged(Native Method)

 at javax.security.auth.Subject.doAs(Subject.java:)

 at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:)

 at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:)

// :: INFO mapreduce.Job: Task Id : attempt_1396618478715_0002_m_000001_0, Status : FAILED

Error: java.io.IOException

 at org.apache.hadoop.mapred.pipes.OutputHandler.waitForAuthentication(OutputHandler.java:)

 at org.apache.hadoop.mapred.pipes.Application.waitForAuthentication(Application.java:)

 at org.apache.hadoop.mapred.pipes.Application.<init>(Application.java:)

 at org.apache.hadoop.mapred.pipes.PipesMapRunner.run(PipesMapRunner.java:)

 at org.apache.hadoop.mapred.MapTask.runOldMapper(MapTask.java:)

 at org.apache.hadoop.mapred.MapTask.run(MapTask.java:)

 at org.apache.hadoop.mapred.YarnChild$.run(YarnChild.java:)

 at java.security.AccessController.doPrivileged(Native Method)

 at javax.security.auth.Subject.doAs(Subject.java:)

 at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:)

 at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:)

// :: INFO mapreduce.Job: Task Id : attempt_1396618478715_0002_m_000000_1, Status : FAILED

Error: java.io.IOException

 at org.apache.hadoop.mapred.pipes.OutputHandler.waitForAuthentication(OutputHandler.java:)

 at org.apache.hadoop.mapred.pipes.Application.waitForAuthentication(Application.java:)

 at org.apache.hadoop.mapred.pipes.Application.<init>(Application.java:)

 at org.apache.hadoop.mapred.pipes.PipesMapRunner.run(PipesMapRunner.java:)

 at org.apache.hadoop.mapred.MapTask.runOldMapper(MapTask.java:)

 at org.apache.hadoop.mapred.MapTask.run(MapTask.java:)

 at org.apache.hadoop.mapred.YarnChild$.run(YarnChild.java:)

 at java.security.AccessController.doPrivileged(Native Method)

 at javax.security.auth.Subject.doAs(Subject.java:)

 at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:)

 at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:)

// :: INFO mapreduce.Job: Task Id : attempt_1396618478715_0002_m_000001_1, Status : FAILED

Error: java.io.IOException

 at org.apache.hadoop.mapred.pipes.OutputHandler.waitForAuthentication(OutputHandler.java:)

 at org.apache.hadoop.mapred.pipes.Application.waitForAuthentication(Application.java:)

 at org.apache.hadoop.mapred.pipes.Application.<init>(Application.java:)

 at org.apache.hadoop.mapred.pipes.PipesMapRunner.run(PipesMapRunner.java:)

 at org.apache.hadoop.mapred.MapTask.runOldMapper(MapTask.java:)

 at org.apache.hadoop.mapred.MapTask.run(MapTask.java:)

 at org.apache.hadoop.mapred.YarnChild$.run(YarnChild.java:)

 at java.security.AccessController.doPrivileged(Native Method)

 at javax.security.auth.Subject.doAs(Subject.java:)

 at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:)

 at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:)

// :: INFO mapreduce.Job: Task Id : attempt_1396618478715_0002_m_000000_2, Status : FAILED

Error: java.io.IOException

 at org.apache.hadoop.mapred.pipes.OutputHandler.waitForAuthentication(OutputHandler.java:)

 at org.apache.hadoop.mapred.pipes.Application.waitForAuthentication(Application.java:)

 at org.apache.hadoop.mapred.pipes.Application.<init>(Application.java:)

 at org.apache.hadoop.mapred.pipes.PipesMapRunner.run(PipesMapRunner.java:)

 at org.apache.hadoop.mapred.MapTask.runOldMapper(MapTask.java:)

 at org.apache.hadoop.mapred.MapTask.run(MapTask.java:)

 at org.apache.hadoop.mapred.YarnChild$.run(YarnChild.java:)

 at java.security.AccessController.doPrivileged(Native Method)

 at javax.security.auth.Subject.doAs(Subject.java:)

 at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:)

 at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:)

// :: INFO mapreduce.Job: Task Id : attempt_1396618478715_0002_m_000001_2, Status : FAILED

Error: java.io.IOException

 at org.apache.hadoop.mapred.pipes.OutputHandler.waitForAuthentication(OutputHandler.java:)

 at org.apache.hadoop.mapred.pipes.Application.waitForAuthentication(Application.java:)

 at org.apache.hadoop.mapred.pipes.Application.<init>(Application.java:)

 at org.apache.hadoop.mapred.pipes.PipesMapRunner.run(PipesMapRunner.java:)

 at org.apache.hadoop.mapred.MapTask.runOldMapper(MapTask.java:)

 at org.apache.hadoop.mapred.MapTask.run(MapTask.java:)

 at org.apache.hadoop.mapred.YarnChild$.run(YarnChild.java:)

 at java.security.AccessController.doPrivileged(Native Method)

 at javax.security.auth.Subject.doAs(Subject.java:)

 at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:)

 at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:)

// :: INFO mapreduce.Job: map % reduce %

// :: INFO mapreduce.Job: Job job_1396618478715_0002 failed with state FAILED due to: Task failed task_1396618478715_0002_m_000000

Job failed as tasks failed. failedMaps: failedReduces:

// :: INFO mapreduce.Job: Counters:

 Job Counters

  Failed map tasks=

  Killed map tasks=

  Launched map tasks=

  Other local map tasks=

  Data-local map tasks=

  Total time spent by all maps in occupied slots (ms)=

  Total time spent by all reduces in occupied slots (ms)=

  Total time spent by all map tasks (ms)=

  Total vcore-seconds taken by all map tasks=

  Total megabyte-seconds taken by all map tasks=

 Map-Reduce Framework

  CPU time spent (ms)=

  Physical memory (bytes) snapshot=

  Virtual memory (bytes) snapshot=

Exception in thread "main" java.io.IOException: Job failed!

 at org.apache.hadoop.mapred.JobClient.runJob(JobClient.java:)

 at org.apache.hadoop.mapred.pipes.Submitter.runJob(Submitter.java:)

 at org.apache.hadoop.mapred.pipes.Submitter.run(Submitter.java:)

 at org.apache.hadoop.mapred.pipes.Submitter.main(Submitter.java:)

注意这里并没有一直卡在”map 0% reduce 0%“而是很快返回了错误结果，说明之前的问题已经解决，这次肯定不是系统的问题，而是IO问题。同样检查container logs，发现了问题原因：

Server failed to authenticate. Exiting

然后搜索解决方案，最后发现原因竟然是Hadoop 2.3自身的原因，需 要我们自己编译Makefile里所需要的libhadooppipes.a和libhadooputils.a这两个静态库文件以适应自己系统的需求 （官网说预编译的是32位库，如果你是64位的才需要重新编译，可是我主机虽是64位但虚拟机系统是32位的，不知道为什么也不行需要重新编译）。这个要求真是很不注重用户体验的要求，于是就来到了重新编译Hadoop 2.3本地库的地狱世界……

编译Hadoop 2.3 Native Library

首先当然是先下载Hadoop 2.3的源码了，解压后根据里面的BUILDING.txt使用下面命令进行编译获得本地库：

mvn package -Pdist,native -Dskiptests -Dtar

期间出现了各种错误，基本上所有的错误都出现在参考资料9中，解决方案就是安装编译前所需要的各种依赖库和依赖程序，比如protobuf、cmake、zlib-devel和openssl-devel，都安装好了过后就”BUILD SUCCESS“了，真是不易，其实官网也给出了这些提示，说需要提前安装所依赖的东西，只是一开始懒得看，具体看参考资料10。

编译成功后，在HADOOP_PATH/hadoop-tools/hadoop-pipes/target/native/路径下可以看到我们所需要的libhadooppipes.a和libhadooputils.a了，将它们拷贝到master和所有slave机器上的 lib/native文件里（最好事先备份一下自带的库文件，避免该方案失败），然后重新make编译wordcount程序，然后 Hadoop pipes运行，最终看到这样的结果：

// :: WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable

// :: INFO client.RMProxy: Connecting to ResourceManager at /192.168.1.137:

// :: INFO client.RMProxy: Connecting to ResourceManager at /192.168.1.137:

// :: WARN mapreduce.JobSubmitter: No job jar file set.  User classes may not be found. See Job or Job#setJar(String).

// :: INFO mapred.FileInputFormat: Total input paths to process :

// :: INFO mapreduce.JobSubmitter: number of splits:

// :: INFO Configuration.deprecation: hadoop.pipes.java.recordreader is deprecated. Instead, use mapreduce.pipes.isjavarecordreader

// :: INFO Configuration.deprecation: mapred.job.name is deprecated. Instead, use mapreduce.job.name

// :: INFO Configuration.deprecation: hadoop.pipes.java.recordwriter is deprecated. Instead, use mapreduce.pipes.isjavarecordwriter

// :: INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1396756477966_0004

// :: INFO mapred.YARNRunner: Job jar is not present. Not adding any jar to the list of resources.

// :: INFO impl.YarnClientImpl: Submitted application application_1396756477966_0004

// :: INFO mapreduce.Job: The url to track the job: http://Master:8088/proxy/application_1396756477966_0004/

// :: INFO mapreduce.Job: Running job: job_1396756477966_0004

// :: INFO mapreduce.Job: Job job_1396756477966_0004 running in uber mode : false

// :: INFO mapreduce.Job:  map 0% reduce 0%

14/04/06 01:22:56 INFO mapreduce.Job:  map 67% reduce 0%

14/04/06 01:22:57 INFO mapreduce.Job:  map 100% reduce 0%

14/04/06 01:23:09 INFO mapreduce.Job:  map 100% reduce 100%

14/04/06 01:23:11 INFO mapreduce.Job: Job job_1396756477966_0004 completed successfully

// :: INFO mapreduce.Job: Counters:

    File System Counters

        FILE: Number of bytes read=

        FILE: Number of bytes written=

        FILE: Number of read operations=

        FILE: Number of large read operations=

        FILE: Number of write operations=

        HDFS: Number of bytes read=

        HDFS: Number of bytes written=

        HDFS: Number of read operations=

        HDFS: Number of large read operations=

        HDFS: Number of write operations=

    Job Counters

        Launched map tasks=

        Launched reduce tasks=

        Data-local map tasks=

        Total time spent by all maps in occupied slots (ms)=

        Total time spent by all reduces in occupied slots (ms)=

        Total time spent by all map tasks (ms)=

        Total time spent by all reduce tasks (ms)=

        Total vcore-seconds taken by all map tasks=

        Total vcore-seconds taken by all reduce tasks=

        Total megabyte-seconds taken by all map tasks=

        Total megabyte-seconds taken by all reduce tasks=

    Map-Reduce Framework

        Map input records=

        Map output records=

        Map output bytes=

        Map output materialized bytes=

        Input split bytes=

        Combine input records=

        Combine output records=

        Reduce input groups=

        Reduce shuffle bytes=

        Reduce input records=

        Reduce output records=

        Spilled Records=

        Shuffled Maps =

        Failed Shuffles=

        Merged Map outputs=

        GC time elapsed (ms)=

        CPU time spent (ms)=

        Physical memory (bytes) snapshot=

        Virtual memory (bytes) snapshot=

        Total committed heap usage (bytes)=

    Shuffle Errors

        BAD_ID=

        CONNECTION=

        IO_ERROR=

        WRONG_LENGTH=

        WRONG_MAP=

        WRONG_REDUCE=

    WORDCOUNT

        INPUT_WORDS=

        OUTPUT_WORDS=

    File Input Format Counters

        Bytes Read=

    File Output Format Counters

        Bytes Written=

// :: INFO util.ExitUtil: Exiting with status

心里真是五味杂陈，这个配置环境的过程真是太痛苦了，当然也锻炼了发现问题和解决问题的能力，也懂得了遇到问题后，LOG永远是最佳解决途径。

谨以此文来记录Hadoop pipes运行环境的配置经历，同样也给那些与我遇到类似问题的小伙伴们指明一条明路……

参考资料

1. 如何在Hadoop集群运行JNI程序

2. JNI的替代者—使用JNA访问Java外部函数接口

3.JNI的又一替代者—使用JNR访问Java外部函数接口(jnr-ffi)

4. Hadoop Streaming和Pipes理解

5.一步步教你Hadoop多节点集群安装配置

6. Hadoop 2.3.0 分布式集群搭建图文

7.Hadoop Tutorial 2.2 -- Running C++ Programs on Hadoop

8. Hadoop 新 MapReduce 框架 Yarn 详解

9.编译hadoop 2.3的native library

10. Native Libraries Guide

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