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[英]Getting existing mapreduce job from cluster (the job could be running or completed)
[英]Mapreduce Job running in local mode instead of cluster
完成配置以在纱线上方以群集模式运行mapreduce作业,但以本地模式运行。 无法找出问题所在。
下面是yarn-site.xml(在主节点上)
<property>
<name>yarn.resourcemanager.resource-tracker.address</name>
<value>namenode:8031</value>
</property>
<property>
<name>yarn.nodemanager.aux-services</name> //node manager servi
<value>mapreduce_shuffle</value> //This will specify that how mapper reducer work
</property>
<property>
<name>yarn.resourcemanager.scheduler.address</name>
<value>namenode:8030</value>
</property>
<property>
<name>yarn.resourcemanager.address</name>
<value>namenode:8032</value>
</property>
<property>
<name>yarn.resourcemanager.hostname</name>
<value>namenode</value>
</property>
<property>
<name>yarn.nodemanager.resource.memory-mb</name>
<value>2042</value>
</property>
<property>
<name>yarn.nodemanager.vmem-check-enabled</name>
<value>false</value>
</property>
yarn-site.xml(在从节点上)
<property>
<name>yarn.nodemanager.aux-services</name> //node manager service
<value>mapreduce_shuffle</value> //This will specify that how mapper reducer work
</property>
<property>
<name>yarn.nodemanager.aux-services.mapreduce_shuffle.class</name>
<value>org.apache.hadoop.mapred.ShuffleHandler</value>
</property>
<property>
<name>yarn.resourcemanager.resource-tracker.address</name>
<value>namenode:8031</value> //Tell the ip_address of resource tracker
</property>
mapred-site.xml(在主节点和从节点上)
<property>
<name>mapreduce.framework.name</name>
<value>yarn</value>
</property>
<property>
<name>yarn.app.mapreduce.am.resource.mb</name>
<value>2048</value>
</property>
<property>
<name>mapreduce.map.memory.mb</name>
<value>2048</value>
</property>
<property>
<name>mapreduce.reduce.memory.mb</name>
<value>2048</value>
</property>
提交后,作业输出如下。
18/12/06 16:20:43 INFO input.FileInputFormat: Total input paths to process : 1
18/12/06 16:20:43 INFO mapreduce.JobSubmitter: number of splits:2
18/12/06 16:20:43 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_local1556004420_0001
18/12/06 16:20:43 INFO mapreduce.Job: The url to track the job: http://localhost:8080/
18/12/06 16:20:43 INFO mapreduce.Job: Running job: job_local1556004420_0001
18/12/06 16:20:43 INFO mapred.LocalJobRunner: OutputCommitter set in config null
18/12/06 16:20:43 INFO mapred.LocalJobRunner: OutputCommitter is org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter
18/12/06 16:20:43 INFO mapred.LocalJobRunner: Waiting for map tasks
18/12/06 16:20:43 INFO mapred.LocalJobRunner: Starting task: attempt_local1556004420_0001_m_000000_0
18/12/06 16:20:43 INFO mapred.Task: Using ResourceCalculatorProcessTree : [ ]
18/12/06 16:20:43 INFO mapred.MapTask: Processing split: hdfs://namenode:9001/all-the-news/articles1.csv:0+134217728
18/12/06 16:20:43 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
18/12/06 16:20:43 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100
18/12/06 16:20:43 INFO mapred.MapTask: soft limit at 83886080
18/12/06 16:20:43 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
18/12/06 16:20:43 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
18/12/06 16:20:43 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
18/12/06 16:20:44 INFO mapreduce.Job: Job job_local1556004420_0001 running in uber mode : false
18/12/06 16:20:44 INFO mapreduce.Job: map 0% reduce 0%
18/12/06 16:20:49 INFO mapred.LocalJobRunner: map > map
18/12/06 16:20:50 INFO mapreduce.Job: map 1% reduce 0%
18/12/06 16:20:52 INFO mapred.LocalJobRunner: map > map
18/12/06 16:20:55 INFO mapred.LocalJobRunner: map > map
18/12/06 16:20:56 INFO mapreduce.Job: map 2% reduce 0%
18/12/06 16:20:58 INFO mapred.LocalJobRunner: map > map
18/12/06 16:21:01 INFO mapred.LocalJobRunner: map > map
18/12/06 16:21:02 INFO mapreduce.Job: map 3% reduce 0%
18/12/06 16:21:04 INFO mapred.LocalJobRunner: map > map
为什么以本地模式运行。 我在具有3个节点2个datanode和1个namenode的200MB文件上运行此作业。
etc / hosts文件如下所示
127.0.0.1 localhost
127.0.1.1 anil-Lenovo-Product
# The following lines are desirable for IPv6 capable hosts
::1 ip6-localhost ip6-loopback
fe00::0 ip6-localnet
ff00::0 ip6-mcastprefix
ff02::1 ip6-allnodes
ff02::2 ip6-allrouters
192.168.8.98 namenode
192.168.8.99 datanode
192.168.8.100 datanode2
请先检查以下配置是否有效:默认情况下为http:// {your-resource-manager-host}:8088 / conf或您配置的UI地址: http:// namenode:8088 / conf
然后确保配置了以下属性:
在mapred-site.xml中
<property>
<name>mapreduce.framework.name</name>
<value>yarn</value>
</property>
<property>
<name>yarn.app.mapreduce.am.env</name>
<value>HADOOP_MAPRED_HOME=${HADOOP_HOME}</value>
</property>
<property>
<name>mapreduce.map.env</name>
<value>HADOOP_MAPRED_HOME=${HADOOP_HOME}</value>
</property>
<property>
<name>mapreduce.reduce.env</name>
<value>HADOOP_MAPRED_HOME=${HADOOP_HOME}</value>
</property>
在yarn-site.xml中
<property>
<name>yarn.nodemanager.aux-services</name>
<value>mapreduce_shuffle</value>
</property>
重新启动YARN服务并检查其是否有效。
作业由ClientProtocol接口提交,服务启动时将创建其两个实现之一:
根据MRConfig.FRAMEWORK_NAME(值是“ mapreduce.framework.name”)配置,其有效选项是classic
, yarn
, local
。
祝好运!
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