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  • flink部署

    参考:

    https://ververica.cn/developers-resources/ 

    #flink参数

    https://blog.csdn.net/qq_35440040/article/details/84992796

    spark使用批处理模拟流计算

    flink使用流框架模拟批计算

     https://ci.apache.org/projects/flink/flink-docs-release-1.8/

    https://flink.apache.org/downloads.html#


    下载包:
    https://flink.apache.org/downloads.html

    tar -xzvf flink-1.8.0-bin-scala_2.11.tgz -C /opt/module/

    vim /etc/profile
    export FLINK_HOME=/opt/module/flink-1.8.0
    export PATH=$PATH:$FLINK_HOME/bin

    cd /opt/module/flink-1.8.0/conf
    mv flink-conf.yaml flink-conf.yaml.bak
    vim flink-conf.yaml
    jobmanager.rpc.address: Fengfeng-dr-algo1
    jobmanager.rpc.port: 6123
    jobmanager.heap.size: 1024m
    taskmanager.heap.size: 1024m
    taskmanager.numberOfTaskSlots: 2
    parallelism.default: 2
    fs.default-scheme: hdfs://Fengfeng-dr-algo1:9820
    #这个是在core-site.xml里配的hdfs集群地址,yarn集群模式主要配这个


    vim masters
    Fengfeng-dr-algo1

    vim slaves
    Fengfeng-dr-algo2
    Fengfeng-dr-algo3
    Fengfeng-dr-algo4


    #配置完成后将文件同步到其他节点
    scp /etc/profile Fengfeng-dr-algo2:/etc/profile
    scp /etc/profile Fengfeng-dr-algo3:/etc/profile
    scp /etc/profile Fengfeng-dr-algo4:/etc/profile
    scp -r /opt/module/flink-1.8.0/ Fengfeng-dr-algo2:/opt/module
    scp -r /opt/module/flink-1.8.0/ Fengfeng-dr-algo3:/opt/module
    scp -r /opt/module/flink-1.8.0/ Fengfeng-dr-algo4:/opt/module

    启动集群start-cluster.sh

    检查TaskManagerRunner服务起来没有:
    [root@Fengfeng-dr-algo1 conf]# ansible all -m shell -a 'jps'
    Fengfeng-dr-algo3 | SUCCESS | rc=0 >>
    20978 DataNode
    22386 TaskManagerRunner
    22490 Jps
    21295 NodeManager

    Fengfeng-dr-algo4 | SUCCESS | rc=0 >>
    24625 NodeManager
    26193 TaskManagerRunner
    24180 DataNode
    24292 SecondaryNameNode
    26297 Jps

    Fengfeng-dr-algo2 | SUCCESS | rc=0 >>
    26753 Jps
    24867 ResourceManager
    24356 DataNode
    25480 NodeManager
    26650 TaskManagerRunner

    Fengfeng-dr-algo1 | SUCCESS | rc=0 >>
    27216 Jps
    24641 NameNode
    24789 DataNode
    27048 StandaloneSessionClusterEntrypoint
    25500 NodeManager

    查看WebUI,端口为8081

    #运行flink测试,1.txt在hdfs上.
    1/ 以standalone模式
    flink run /opt/module/flink-1.8.0/examples/batch/WordCount.jar -c wordcount --input /1.txt
    2/ 以yarn-cluster模式,需要停掉集群模式stop-cluster.sh
    flink run -m yarn-cluster /opt/module/flink-1.8.0/examples/batch/WordCount.jar -c wordcount --input /1.txt

    yarn-cluster跑得作业情况可在yarn的web8080端口看

    附: flink yarn-cluster跑wordcount结果

    [root@fengfeng-dr-algo1 hadoop]# flink run -m yarn-cluster /opt/module/flink-1.8.0/examples/batch/WordCount.jar -c wordcount --input /1.txt
    2019-08-15 03:52:50,622 INFO org.apache.hadoop.yarn.client.RMProxy - Connecting to ResourceManager at oride-dr-algo2/172.28.20.168:8032
    2019-08-15 03:52:50,755 INFO org.apache.flink.yarn.cli.FlinkYarnSessionCli - No path for the flink jar passed. Using the location of class org.apache.flink.yarn.YarnClusterDescriptor to locate the jar
    2019-08-15 03:52:50,755 INFO org.apache.flink.yarn.cli.FlinkYarnSessionCli - No path for the flink jar passed. Using the location of class org.apache.flink.yarn.YarnClusterDescriptor to locate the jar
    2019-08-15 03:52:50,922 WARN org.apache.flink.yarn.AbstractYarnClusterDescriptor - Neither the HADOOP_CONF_DIR nor the YARN_CONF_DIR environment variable is set. The Flink YARN Client needs one of these to be set to properly load the Hadoop configuration for accessing YARN.
    2019-08-15 03:52:50,961 INFO org.apache.flink.yarn.AbstractYarnClusterDescriptor - Cluster specification: ClusterSpecification{masterMemoryMB=1024, taskManagerMemoryMB=1024, numberTaskManagers=1, slotsPerTaskManager=2}
    2019-08-15 03:52:51,410 WARN org.apache.flink.yarn.AbstractYarnClusterDescriptor - The configuration directory ('/opt/module/flink-1.8.0/conf') contains both LOG4J and Logback configuration files. Please delete or rename one of them.
    2019-08-15 03:52:52,456 INFO org.apache.flink.yarn.AbstractYarnClusterDescriptor - Submitting application master application_1565840709386_0002
    2019-08-15 03:52:52,481 INFO org.apache.hadoop.yarn.client.api.impl.YarnClientImpl - Submitted application application_1565840709386_0002
    2019-08-15 03:52:52,481 INFO org.apache.flink.yarn.AbstractYarnClusterDescriptor - Waiting for the cluster to be allocated
    2019-08-15 03:52:52,484 INFO org.apache.flink.yarn.AbstractYarnClusterDescriptor - Deploying cluster, current state ACCEPTED
    2019-08-15 03:52:56,776 INFO org.apache.flink.yarn.AbstractYarnClusterDescriptor - YARN application has been deployed successfully.
    Starting execution of program
    Printing result to stdout. Use --output to specify output path.
    (abstractions,1)
    (an,3)
    (and,3)
    (application,1)
    (at,2)
    (be,1)
    (broadcast,2)
    (called,1)
    (can,1)
    (deep,1)
    (dive,1)
    (dynamic,1)
    (event,1)
    (every,1)
    (example,1)
    (explain,1)
    (exposed,1)
    (flink,6)
    (has,1)
    (implementation,1)
    (into,1)
    (is,4)
    (look,1)
    (make,1)
    (of,6)
    (on,1)
    (one,2)
    (physical,1)
    (runtime,1)
    (s,3)
    (stack,3)
    (state,3)
    (the,6)
    (this,2)
    (types,1)
    (up,1)
    (what,1)
    (a,2)
    (about,1)
    (apache,3)
    (applied,1)
    (components,1)
    (core,2)
    (detail,1)
    (evaluates,1)
    (first,1)
    (how,1)
    (in,4)
    (it,1)
    (job,1)
    (module,1)
    (multiple,1)
    (network,3)
    (operator,1)
    (operators,1)
    (optimisations,1)
    (patterns,1)
    (post,2)
    (posts,1)
    (series,1)
    (show,1)
    (sitting,1)
    (stream,2)
    (that,2)
    (their,1)
    (to,2)
    (various,1)
    (we,2)
    (which,2)
    Program execution finished
    Job with JobID 11307954aeb6a6356cd7b4068f0f2160 has finished.
    Job Runtime: 8448 ms
    Accumulator Results:
    - f0f87f15adda6b1c2703a30e110db5ed (java.util.ArrayList) [69 elements]

    公司:

    flink run -p 2 -m yarn-cluster -yn 2 -yqu root.users.airflow -ynm opay-metrics -ys 1 -d -c com.opay.bd.opay.main.OpayOrderMetricsMain bd-flink-project-1.0.jar

    flink run -p 2 -m yarn-cluster -yn 2 -yqu root.users.airflow -ynm oride-metrics -ys 1 -d -c com.opay.bd.oride.main.OrideOrderMetricsMain bd-flink-project-1.0.jar

    -p,--parallelism <parallelism> 运行程序的并行度。 可以选择覆盖配置中指定的默认值
    -yn 分配 YARN 容器的数量(=TaskManager 的数量)
    -yqu,--yarnqueue <arg> 指定 YARN 队列
    -ynm oride-metrics 给应用程序一个自定义的名字显示在 YARN 上
    -ys,--yarnslots <arg> 每个 TaskManager 的槽位数量
    -ys,--yarnslots <arg> 每个 TaskManager 的槽位数量
    -c,--class <classname> 程序入口类
    ("main" 方法 或 "getPlan()" 方法)
    -m yarn-cluster cluster模式

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  • 原文地址:https://www.cnblogs.com/hongfeng2019/p/11354108.html
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