Hadoop-2.7.3集群搭建中遇到的问题总结

xiaoxiao2021-12-14 21

1 搭建过程中常用Hadoop指令：

1）启动Hadoop指令：

start-all.sh mr-jobhistory-daemon.sh start historyserver 12

启动成功过程log输出：

2）查看DataNode是否正常启动命令：

hdfs dfsadmin -report 1

shell输出：注：也可以通过web页面(http://master:50070)查看具体状态

3）创建HDFS上的用户输入输出目录：

hdfs dfs -mkdir /user/hadoop/input hdfs dfs -mkdir /user/hadoop/output 12

4）将文件作为输入文件复制到分布式文件系统中：

以hadoop中配置文件作为输入数据源

hdfs dfs -put $HADOOP_HOME/etc/hadoop/*.xml /user/hadoop/input 1

注：复制后可通过web页面查看DataNode的Block pool used是否有变化

5）MapReduce作业：

以wordCount为例

hadoop jar $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-example-2.7.3.jar wordcount /user/hadoop/input /user/hadoop/output 1

注意：在此之前，必须手动新建output目录，目录不能重复！

hdfs dfs -mkdir /user/hadoop/output 1

6）停止Hadoop指令：

mr-jobhistory-daemon.sh stop historyserver stop-all.sh 12

2 配置集群以及执行mapreduce中遇到的问题及解决方案分享：

1）SSH无密码登陆子节点失败：

原因：主节点和子节点编码格式不一致解决：统一编码格式，具体如何实现SSH无密码登陆，参照上一篇博文

2）Hadoop安装包共享问题：

解决：Windows和Linux之间文件传输可使用secureCRT软件，在linux上安装”lrzsz”即可，使用命令rz，即可实现windows上传到linux；sz则反之，具体网上有资料~

3）是否需要手动配置三次？

答案：不需要！怎么做：先配置安装主节点的hadoop，然后压缩打包成xxx.tar.gz文件，通过指令copy到从节点上：

scp xxx.tar.gz 用户名@Slave1:~/ 1

这份文件即会出现在从节点的用户根目录下，解压配置hadoop环境变量即可

4）最蛋疼的问题！！！：执行mapreduce时，hadoop卡在Running job上，即hadoop stuck at running job

题外话：这个问题卡了我快两天的时间！各种google，参考了国内外各大神的帖子博文，最后还是得看hadoop的输出日志才对症下药，finish掉这个问题

问题现象：

网上盗的图，自己那个没记录下来。。。现象是一致的！

hadoop@ubuntu:~$ $HADOOP_HOME/bin/hadoop jar $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.3.0.jar wordcount /myprg outputfile1 14/04/30 13:20:40 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032 14/04/30 13:20:51 INFO input.FileInputFormat: Total input paths to process : 1 14/04/30 13:20:53 INFO mapreduce.JobSubmitter: number of splits:1 14/04/30 13:21:02 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1398885280814_0004 14/04/30 13:21:07 INFO impl.YarnClientImpl: Submitted application application_1398885280814_0004 14/04/30 13:21:09 INFO mapreduce.Job: The url to track the job: http://ubuntu:8088/proxy/application_1398885280814_0004/ 14/04/30 13:21:09 INFO mapreduce.Job: Running job: job_1398885280814_0004 12345678

然后打开(http://master:8088)查看Applications，发现刚提交的job一直卡在Accepted状态，并没有Running，等待许久也如此！

我碰到的原因有二：一、内存不足引起需要更改yarn-site.xml和mapred-site.xml的内存配置，我参考了国外的博文(http://zh.hortonworks.com/blog/how-to-plan-and-configure-yarn-in-hdp-2-0/) 具体修改如下： 1）yarn-site.xml

<configuration> <property> <name>yarn.resourcemanager.hostname</name> <value>Master</value> </property> <property> <name>yarn.nodemanager.aux-services</name> <value>mapreduce_shuffle</value> </property> <property> <name>yarn.nodemanager.resource.memory-mb</name> <value>40960</value> </property> <property> <name>yarn.scheduler.minimum-allocation-mb</name> <value>2048</value> </property> <property> <name>yarn.nodemanager.vmem-pmem-ratio</name> <value>2.1</value> </property> </configuration> 12345678910111213141516171819202122

2）mapred-site.xml

<configuration> <property> <name>mapreduce.framework.name</name> <value>yarn</value> </property> <property> <name>mapreduce.jobhistory.address</name> <value>Master:10020</value> </property> <property> <name>mapreduce.jobhistory.webapp.address</name> <value>Master:19888</value> </property> <property> <name>mapreduce.map.memory.mb</name> <value>4096</value> </property> <property> <name>mapreduce.reduce.memory.mb</name> <value>8192</value> </property> <property> <name>mapreduce.map.java.opts</name> <value>-Xmx3072m</value> </property> <property> <name>mapreduce.reduce.java.opts</name> <value>-Xmx6144m</value> </property> </configuration> 123456789101112131415161718192021222324252627282930

然而，我是这么做了，可是问题依旧！二、节点/etc/hostname配置摆乌龙于是乎，我查看了hadoop的输出日志，我查看了“yarn-用户名-resourcemanager-主机名.log”，里面的内容相当多……………… 然后，我发现了问题！

2016-09-20 15:31:59,339 ERROR org.apache.hadoop.yarn.server.resourcemanager.scheduler.SchedulerApplicationAttempt: Error trying to assign container token and NM token to an allocated container container_1474355964293_0002_01_000001 java.lang.IllegalArgumentException: java.net.UnknownHostException: 子节点hostname at org.apache.hadoop.security.SecurityUtil.buildTokenService(SecurityUtil.java:377) at org.apache.hadoop.yarn.server.utils.BuilderUtils.newContainerToken(BuilderUtils.java:258) at org.apache.hadoop.yarn.server.resourcemanager.security.RMContainerTokenSecretManager.createContainerToken(RMContainerTokenSecretManager.java:220) at org.apache.hadoop.yarn.server.resourcemanager.scheduler.SchedulerApplicationAttempt.pullNewlyAllocatedContainersAndNMTokens(SchedulerApplicationAttempt.java:455) at org.apache.hadoop.yarn.server.resourcemanager.scheduler.common.fica.FiCaSchedulerApp.getAllocation(FiCaSchedulerApp.java:269) at org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacityScheduler.allocate(CapacityScheduler.java:988) at org.apache.hadoop.yarn.server.resourcemanager.rmapp.attempt.RMAppAttemptImpl$AMContainerAllocatedTransition.transition(RMAppAttemptImpl.java:988) at org.apache.hadoop.yarn.server.resourcemanager.rmapp.attempt.RMAppAttemptImpl$AMContainerAllocatedTransition.transition(RMAppAttemptImpl.java:981) at org.apache.hadoop.yarn.state.StateMachineFactory$MultipleInternalArc.doTransition(StateMachineFactory.java:385) at org.apache.hadoop.yarn.state.StateMachineFactory.doTransition(StateMachineFactory.java:302) at org.apache.hadoop.yarn.state.StateMachineFactory.access$300(StateMachineFactory.java:46) at org.apache.hadoop.yarn.state.StateMachineFactory$InternalStateMachine.doTransition(StateMachineFactory.java:448) at org.apache.hadoop.yarn.server.resourcemanager.rmapp.attempt.RMAppAttemptImpl.handle(RMAppAttemptImpl.java:806) at org.apache.hadoop.yarn.server.resourcemanager.rmapp.attempt.RMAppAttemptImpl.handle(RMAppAttemptImpl.java:107) at org.apache.hadoop.yarn.server.resourcemanager.ResourceManager$ApplicationAttemptEventDispatcher.handle(ResourceManager.java:803) at org.apache.hadoop.yarn.server.resourcemanager.ResourceManager$ApplicationAttemptEventDispatcher.handle(ResourceManager.java:784) at org.apache.hadoop.yarn.event.AsyncDispatcher.dispatch(AsyncDispatcher.java:184) at org.apache.hadoop.yarn.event.AsyncDispatcher$1.run(AsyncDispatcher.java:110) at java.lang.Thread.run(Thread.java:745) Caused by: java.net.UnknownHostException: 子节点hostname 12345678910111213141516171819202122

这下问题就清楚了，如何解决？很简单，配置每个节点的/etc/hostname（在ubuntu中），将Master、Slave1、Slave2分别配置在各自对应主机的/etc/hostname文件第一行，如：

Slave1 NETWORKING=yes HOSTNAME=Slave1 1234

然后三个主机全部重启！激动人心的时候终于要到来啦！再次执行

hadoop jar $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-example-2.7.3.jar wordcount /user/hadoop/input /user/hadoop/output 1

执行过程及结果：

xxx@Master:~$ hadoop jar /opt/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.3.jar wordcount /user/hadoop/input /user/hadoop/output 16/09/21 12:54:57 INFO client.RMProxy: Connecting to ResourceManager at Master/10.100.3.88:8032 16/09/21 12:54:58 INFO input.FileInputFormat: Total input paths to process : 9 16/09/21 12:54:58 INFO mapreduce.JobSubmitter: number of splits:9 16/09/21 12:54:59 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1474433568841_0001 16/09/21 12:54:59 INFO impl.YarnClientImpl: Submitted application application_1474433568841_0001 16/09/21 12:54:59 INFO mapreduce.Job: The url to track the job: http://Master:8088/proxy/application_1474433568841_0001/ 16/09/21 12:54:59 INFO mapreduce.Job: Running job: job_1474433568841_0001 16/09/21 12:55:11 INFO mapreduce.Job: Job job_1474433568841_0001 running in uber mode : false 16/09/21 12:55:11 INFO mapreduce.Job: map 0% reduce 0% 16/09/21 12:55:56 INFO mapreduce.Job: map 33% reduce 0% 16/09/21 12:56:02 INFO mapreduce.Job: map 89% reduce 0% 16/09/21 12:56:03 INFO mapreduce.Job: map 100% reduce 0% 16/09/21 12:56:12 INFO mapreduce.Job: map 100% reduce 100% 16/09/21 12:56:13 INFO mapreduce.Job: Job job_1474433568841_0001 completed successfully 16/09/21 12:56:14 INFO mapreduce.Job: Counters: 49 File System Counters FILE: Number of bytes read=22561 FILE: Number of bytes written=1233835 FILE: Number of read operations=0 FILE: Number of large read operations=0 FILE: Number of write operations=0 HDFS: Number of bytes read=29774 HDFS: Number of bytes written=11201 HDFS: Number of read operations=30 HDFS: Number of large read operations=0 HDFS: Number of write operations=2 Job Counters Launched map tasks=9 Launched reduce tasks=1 Data-local map tasks=9 Total time spent by all maps in occupied slots (ms)=885942 Total time spent by all reduces in occupied slots (ms)=24432 Total time spent by all map tasks (ms)=442971 Total time spent by all reduce tasks (ms)=6108 Total vcore-milliseconds taken by all map tasks=442971 Total vcore-milliseconds taken by all reduce tasks=6108 Total megabyte-milliseconds taken by all map tasks=1814409216 Total megabyte-milliseconds taken by all reduce tasks=50036736 Map-Reduce Framework Map input records=825 Map output records=2920 Map output bytes=37672 Map output materialized bytes=22609 Input split bytes=987 Combine input records=2920 Combine output records=1281 Reduce input groups=622 Reduce shuffle bytes=22609 Reduce input records=1281 Reduce output records=622 Spilled Records=2562 Shuffled Maps =9 Failed Shuffles=0 Merged Map outputs=9 GC time elapsed (ms)=6441 CPU time spent (ms)=14050 Physical memory (bytes) snapshot=1877909504 Virtual memory (bytes) snapshot=53782507520 Total committed heap usage (bytes)=2054160384 Shuffle Errors BAD_ID=0 CONNECTION=0 IO_ERROR=0 WRONG_LENGTH=0 WRONG_MAP=0 WRONG_REDUCE=0 File Input Format Counters Bytes Read=28787 File Output Format Counters Bytes Written=11201 1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071

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