Hibench做Hadoop基准测试遇到的问题

Hadoop 2.7.4集群基准测试

DN：32C 128G * 8，1块HDD 4T

hibench.scale.profile bigdata
hibench.default.map.parallelism 64
hibench.default.shuffle.parallelism 64

 

 

遇到的问题：

问题1：

Container launch failed for container_1591910537318_0012_01_002523 : java.io.IOException: Failed on local exception: java.io.IOException: java.net.SocketTimeoutException: 60000 millis timeout while waiting for channel to be ready for read. ch : 
java.nio.channels.SocketChannel[connected local=/10.0.0.222:52069 remote=CnBRWfGV-Core5.jcloud.local/10.0.0.220:45454]; Host Details : local host is: "CnBRWfGV-Core8.jcloud.local/10.0.0.222"; destination host is: "CnBRWfGV-Core5.jcloud.local":45454; 
at org.apache.hadoop.net.NetUtils.wrapException(NetUtils.java:776) at org.apache.hadoop.ipc.Client.call(Client.java:1480) 
at org.apache.hadoop.ipc.Client.call(Client.java:1413) 
at org.apache.hadoop.ipc.ProtobufRpcEngine$Invoker.invoke(ProtobufRpcEngine.java:229) 
at com.sun.proxy.$Proxy81.startContainers(Unknown Source) 
at org.apache.hadoop.yarn.api.impl.pb.client.ContainerManagementProtocolPBClientImpl.startContainers(ContainerManagementProtocolPBClientImpl.java:96) 
at sun.reflect.GeneratedMethodAccessor13.invoke(Unknown Source) 
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) 
at java.lang.reflect.Method.invoke(Method.java:498) 
at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:191) 
at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:102) 
at com.sun.proxy.$Proxy82.startContainers(Unknown Source) 
at org.apache.hadoop.mapreduce.v2.app.launcher.ContainerLauncherImpl$Container.launch(ContainerLauncherImpl.java:152) 
at org.apache.hadoop.mapreduce.v2.app.launcher.ContainerLauncherImpl$EventProcessor.run(ContainerLauncherImpl.java:375) 
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) 
at java.lang.Thread.run(Thread.java:745)

Caused by: java.io.IOException: java.net.SocketTimeoutException: 60000 millis timeout while waiting for channel to be ready for read. ch : java.nio.channels.SocketChannel[connected local=/10.0.0.222:52069 remote=CnBRWfGV-Core5.jcloud.local/10.0.0.220:45454] 
at org.apache.hadoop.ipc.Client$Connection$1.run(Client.java:688) 
at java.security.AccessController.doPrivileged(Native Method) 
at javax.security.auth.Subject.doAs(Subject.java:422) 
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1746) 
at org.apache.hadoop.ipc.Client$Connection.handleSaslConnectionFailure(Client.java:651) 
at org.apache.hadoop.ipc.Client$Connection.setupIOstreams(Client.java:738) 
at org.apache.hadoop.ipc.Client$Connection.access$2900(Client.java:376) 
at org.apache.hadoop.ipc.Client.getConnection(Client.java:1529) 
at org.apache.hadoop.ipc.Client.call(Client.java:1452) ... 15 more

Caused by: java.net.SocketTimeoutException: 60000 millis timeout while waiting for channel to be ready for read. ch : java.nio.channels.SocketChannel[connected local=/10.0.0.222:52069 remote=CnBRWfGV-Core5.jcloud.local/10.0.0.220:45454] 
at org.apache.hadoop.net.SocketIOWithTimeout.doIO(SocketIOWithTimeout.java:164) 
at org.apache.hadoop.net.SocketInputStream.read(SocketInputStream.java:161) 
at org.apache.hadoop.net.SocketInputStream.read(SocketInputStream.java:131) 
at java.io.FilterInputStream.read(FilterInputStream.java:133) 
at java.io.BufferedInputStream.fill(BufferedInputStream.java:246) 
at java.io.BufferedInputStream.read(BufferedInputStream.java:265) 
at java.io.DataInputStream.readInt(DataInputStream.java:387) 
at org.apache.hadoop.security.SaslRpcClient.saslConnect(SaslRpcClient.java:367) 
at org.apache.hadoop.ipc.Client$Connection.setupSaslConnection(Client.java:561) 
at org.apache.hadoop.ipc.Client$Connection.access$1900(Client.java:376) 
at org.apache.hadoop.ipc.Client$Connection$2.run(Client.java:730) 
at org.apache.hadoop.ipc.Client$Connection$2.run(Client.java:726) 
at java.security.AccessController.doPrivileged(Native Method) 
at javax.security.auth.Subject.doAs(Subject.java:422) 
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1746) 
at org.apache.hadoop.ipc.Client$Connection.setupIOstreams(Client.java:726) ... 18 more

解决：出现以上错误是因为datanode的服务线程连接数都被占用，导致Yarn等待超时

1. 修改datanode的处理线程数量： hdfs-site.xml的 dfs.datanode.handler.count，默认是10

2.修改客户端的超时时间： hdfs-site.xml的 dfs.client.socket-timeout，默认是60000ms

修改完之后，同步到每个节点：ansible all -m copy -a "src=/usr/local/hadoop-2.7.4/etc/hadoop/hdfs-site.xml dest=/usr/local/hadoop-2.7.4/etc/hadoop/hdfs-site.xml"

刷新配置信息：hdfs dfsadmin -refreshNodes

 

问题2:

Error: java.io.IOException: All datanodes DatanodeInfoWithStorage[10.0.0.219:50010,DS-538c6742-6e34-453c-b4c9-c89efaf63905,DISK] are bad. Aborting... 
at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.setupPipelineForAppendOrRecovery(DFSOutputStream.java:1224) 
at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.processDatanodeError(DFSOutputStream.java:990) 
at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.run(DFSOutputStream.java:507) Container killed by the ApplicationMaster. Container killed on request. Exit code is 143 Container exited with a non-zero exit code 143

解决：可能是datanode的数据盘太少，而container的数量太多，在并发写盘时会出这个问题，后来每台机器的container数量降到了8个，没再出现。

 

Hibench是如何计算map的container的数量的呢？

我在跑wordcount这个case的时候，设置的是64个并行度，那么wordcount准备数据时，一共创建了64个文件，如下：

23.9 G /HiBench/Wordcount/Input/part-m-00000
23.9 G /HiBench/Wordcount/Input/part-m-00001
23.9 G /HiBench/Wordcount/Input/part-m-00002
23.9 G /HiBench/Wordcount/Input/part-m-00003
23.9 G /HiBench/Wordcount/Input/part-m-00004

.......

在进行wordcount分析时，一共产生了6144个map，这是怎么算的呢？先看两个参数：

<property>
    <name>file.blocksize</name>
    <value>67108864</value> 67108864 / 1024 /1024 = 64mb
    <source>core-default.xml</source>
</property>

<property>
    <name>mapreduce.input.fileinputformat.split.minsize</name>
    <value>268435456</value> 268435456 / 1024 /1024=256mb
    <source>mapred-site.xml</source>
</property>　　

1.5 T /HiBench/Wordcount/Input 一共输入文件大小是 1.5 * 1024 * 1024 = 1572864 mb，1572864 /6144 = 256，也就说每个map处理256mb的数据，也就是 mapreduce.input.fileinputformat.split.minsize 起了作用

因此 wordcount一共启动了6144个map container

 

参考文档：https://blog.csdn.net/gangchengzhong/article/details/54861082