Flink on yarn部署模式
背景
Flink是一个高性能，高吞吐，低延迟的流处理框架。它不仅仅是作为一个流式处理框架，更将批处理统一了起来（在Flink中，批处理是流处理的一种特例）。Flink的这种架构，也更好的解决了传统大数据架构那种繁琐的组件堆积，让批流能够在不改变原有代码的基础上，进行批处理或者流处理。实现了Flink支持多种部署方式local,standalone,yarn以及k8s,现在大多数企业因为大数据平台都以yarn作为资源管理器，所以为了方便管理，很多企业选择了Flink on yarn这种模式。当然随着容器云火热，不少企业选择K8S作为大数据平台的整个资源管理器，这个时候可以选择将Flink部署到K8S之上。下面重点介绍现阶段Flink on yarn在企业中的应用。
Flink on Yarn交互过程概览
flink on yarn的整个交互过程图，如下：
要使得flink运行于yarn上，flink要能找到hadoop配置，因为要连接到yarn的resourcemanager和hdfs。可以使用下面的策略来指定hadoop配置：
-
1.会查看YARN_CONF_DIR，HADOOP_CONF_DIR或者HADOOP_CONF_PATH是否设置，按照顺序检查的。然后，假如配置了就会从该文件夹下读取配置。
-
2.如果上面环境变量都没有配置的话，会使用HADOOP_HOME环境变量。对于hadoop2的话会查找的配置路径是
$HADOOP_HOME/etc/hadoop;对于hadoop1会查找的路径是$HADOOP_HOME/conf.
每当创建一个新flink的yarn session的时候，客户端会首先检查要请求的资源(containers和memory)是否可用。然后，将包含flink相关的jar包盒配置上传到hdfs。
接下来就是客户端会向resourcemanager申请一个yarn container 用以启动ApplicationMaster。由于客户端已经将配置和jar文件注册为了container的资源，所以nodemanager会直接使用这些资源准备好container（例如，下载文件等）。一旦该过程结束，AM就被启动了。
Jobmanager和AM运行于同一个container。一旦创建成功，AM就知道了Jobmanager的地址。它会生成一个新的flink配置文件，这个配置文件是给将要启动的taskManager用的，该配置文件也会上传到hdfs。另外，AM的container也提供了Flink的web接口。Yarn代码申请的端口都是临时端口，目的是为了让用户并行启动多个Flink YARN Session。
最后，AM开始申请启动Flink Taskmanager的containers，这些container会从hdfs上下载jar文件和已修改的配置文件。一旦这些步骤完成，flink就可以接受任务了。
Flink 作业提交
因Flink强大的灵活性及开箱即用的原则， 因此提交作业分为2种情况：
-
yarn seesion
-
Flink run
这2者对于现有大数据平台资源使用率有着很大的区别：
-
1.第一种yarn seesion(Start a long-running Flink cluster on YARN)这种方式需要先启动集群，然后在提交作业，接着会向yarn申请一块空间后，资源永远保持不变。如果资源满了，下一个作业就无法提交，只能等到yarn中的其中一个作业执行完成后，释放了资源，那下一个作业才会正常提交.
-
2.第二种Flink run直接在YARN上提交运行Flink作业(Run a Flink job on YARN)，这种方式的好处是一个任务会对应一个job,即没提交一个作业会根据自身的情况，向yarn申请资源，直到作业执行完成，并不会影响下一个作业的正常运行，除非是yarn上面没有任何资源的情况下。
综合以上这2种的示意图如下：
注意事项:如果是平时的本地测试或者开发，可以采用第一种方案；如果是生产环境推荐使用第二种方案；
Flink on yarn模式部署时，不需要对Flink做任何修改配置，只需要将其解压传输到各个节点之上。但如果要实现高可用的方案，这个时候就需要到Flink相应的配置修改参数，具体的配置文件是FLINK_HOME/conf/flink-conf.yaml。
对于Flink on yarn模式，我们并不需要在conf配置下配置 masters和slaves。因为在指定TM的时候可以通过参数“-n”来标识需要启动几个TM;Flink on yarn启动后，如果是在分离式模式你会发现，在所有的节点只会出现一个 YarnSessionClusterEntrypoint进程；如果是客户端模式会出现2个进程一个YarnSessionClusterEntrypoint和一个FlinkYarnSessionCli进程。
Flink yarn session部署
用yarn session在启动集群时，有2种方式可以进行集群启动分别是：
-
客户端模式
-
分离式模式
客户端模式
默认可以直接执行bin/yarn-session.sh 默认启动的配置是
{masterMemoryMB=1024, taskManagerMemoryMB=1024,numberTaskManagers=1, slotsPerTaskManager=1}
需要自己自定义配置的话，可以使用来查看参数：
bin/yarn-session.sh –help
Usage:
Required
-n,--container <arg> Number of YARN container to allocate (=Number of Task Managers)
Optional
-D <property=value> use value for given property
-d,--detached If present, runs the job in detached mode
-h,--help Help for the Yarn session CLI.
-id,--applicationId <arg> Attach to running YARN session
-j,--jar <arg> Path to Flink jar file
-jm,--jobManagerMemory <arg> Memory for JobManager Container with optional unit (default: MB)
-m,--jobmanager <arg> Address of the JobManager (master) to which to connect. Use this flag to connect to a different JobManager than the one specified in the configuration.
-n,--container <arg> Number of YARN container to allocate (=Number of Task Managers)
-nl,--nodeLabel <arg> Specify YARN node label for the YARN application
-nm,--name <arg> Set a custom name for the application on YARN
-q,--query Display available YARN resources (memory, cores)
-qu,--queue <arg> Specify YARN queue.
-s,--slots <arg> Number of slots per TaskManager
-sae,--shutdownOnAttachedExit If the job is submitted in attached mode, perform a best-effort cluster shutdown when the CLI is terminated abruptly, e.g., in response to a user interrupt, such
as typing Ctrl + C.
-st,--streaming Start Flink in streaming mode
-t,--ship <arg> Ship files in the specified directory (t for transfer)
-tm,--taskManagerMemory <arg> Memory per TaskManager Container with optional unit (default: MB)
-yd,--yarndetached If present, runs the job in detached mode (deprecated; use non-YARN specific option instead)
-z,--zookeeperNamespace <arg> Namespace to create the Zookeeper sub-paths for high availability mode
yarn-session的参数介绍
-n ： 指定TaskManager的数量；
-d: 以分离模式运行；
-id：指定yarn的任务ID；
-j:Flink jar文件的路径;
-jm：JobManager容器的内存（默认值：MB）;
-nl：为YARN应用程序指定YARN节点标签;
-nm:在YARN上为应用程序设置自定义名称;
-q:显示可用的YARN资源（内存，内核）;
-qu:指定YARN队列;
-s:指定TaskManager中slot的数量;
-st:以流模式启动Flink;
-tm:每个TaskManager容器的内存（默认值：MB）;
-z:命名空间，用于为高可用性模式创建Zookeeper子路径;
我们启动一个yarn-session有2个Taskmanager，jobmanager内存2GB，taskManager2GB内存，那么脚本编写应该是这样的：
./bin/yarn-session.sh -n 2 -jm 1024 -tm 1024
启动的进程
[iknow@data-hadoop-50-63 ~]$ jps
186144 SecondaryNameNode
301825 ResourceManager
185845 DataNode
76966 Worker
457498 Jps
302171 NodeManager
457097 FlinkYarnSessionCli
185597 NameNode
[iknow@data-hadoop-50-64 ~]$ jps
269396 Jps
248059 NodeManager
39624 Worker
246509 DataNode
[iknow@data-hadoop-50-64 ~]$ jps
269697 Jps
248059 NodeManager
39624 Worker
269576 YarnSessionClusterEntrypoint
246509 DataNode
系统默认使用con/flink-conf.yaml里的配置。Flink on yarn将会覆盖掉几个参数：jobmanager.rpc.address因为jobmanager的在集群的运行位置并不是实现确定的，前面也说到了就是am的地址；taskmanager.tmp.dirs使用yarn给定的临时目录;parallelism.default也会被覆盖掉，如果在命令行里指定了slot数。
如果你想保证conf/flink-conf.yaml仅是全局末日配置，然后针对要启动的每一个yarn-session.sh都设置自己的配置，那么可以考虑使用-D修饰。
日志如下：
iknow@data-hadoop-50-63 flink-1.7.2]$ ./bin/yarn-session.sh -n 2 -jm 1024 -tm 1024
2019-02-22 11:26:54,048 INFO org.apache.flink.configuration.GlobalConfiguration - Loading configuration property: jobmanager.rpc.address, 192.168.50.63
2019-02-22 11:26:54,049 INFO org.apache.flink.configuration.GlobalConfiguration - Loading configuration property: jobmanager.rpc.port, 6123
2019-02-22 11:26:54,050 INFO org.apache.flink.configuration.GlobalConfiguration - Loading configuration property: jobmanager.heap.size, 1024m
2019-02-22 11:26:54,050 INFO org.apache.flink.configuration.GlobalConfiguration - Loading configuration property: taskmanager.heap.size, 1024m
2019-02-22 11:26:54,050 INFO org.apache.flink.configuration.GlobalConfiguration - Loading configuration property: taskmanager.numberOfTaskSlots, 1
2019-02-22 11:26:54,050 INFO org.apache.flink.configuration.GlobalConfiguration - Loading configuration property: parallelism.default, 1
2019-02-22 11:26:54,051 INFO org.apache.flink.configuration.GlobalConfiguration - Loading configuration property: rest.port, 18081
2019-02-22 11:26:54,392 WARN org.apache.hadoop.util.NativeCodeLoader - Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
2019-02-22 11:26:54,450 INFO org.apache.flink.runtime.security.modules.HadoopModule - Hadoop user set to iknow (auth:SIMPLE)
2019-02-22 11:26:54,506 INFO org.apache.hadoop.yarn.client.RMProxy - Connecting to ResourceManager at /0.0.0.0:8032
2019-02-22 11:26:54,606 INFO org.apache.flink.yarn.cli.FlinkYarnSessionCli - The argument n is deprecated in will be ignored.
2019-02-22 11:26:54,713 INFO org.apache.flink.yarn.AbstractYarnClusterDescriptor - Cluster specification: ClusterSpecification{masterMemoryMB=1024, taskManagerMemoryMB=1024, numberTaskManagers=2, slotsPerTaskManager=1}
2019-02-22 11:26:55,023 WARN org.apache.flink.yarn.AbstractYarnClusterDescriptor - The configuration directory ('/home/iknow/zhangzhiyong/flink-1.7.2/conf') contains both LOG4J and Logback configuration files. Please delete or rename one of them.
2019-02-22 11:26:56,027 INFO org.apache.flink.yarn.AbstractYarnClusterDescriptor - Submitting application master application_1550804667415_0004
2019-02-22 11:26:56,058 INFO org.apache.hadoop.yarn.client.api.impl.YarnClientImpl - Submitted application application_1550804667415_0004
2019-02-22 11:26:56,058 INFO org.apache.flink.yarn.AbstractYarnClusterDescriptor - Waiting for the cluster to be allocated
2019-02-22 11:26:56,060 INFO org.apache.flink.yarn.AbstractYarnClusterDescriptor - Deploying cluster, current state ACCEPTED
2019-02-22 11:26:59,340 INFO org.apache.flink.yarn.AbstractYarnClusterDescriptor - YARN application has been deployed successfully.
2019-02-22 11:26:59,652 INFO org.apache.flink.runtime.rest.RestClient - Rest client endpoint started.
Flink JobManager is now running on data-hadoop-50-63.bjrs.xxx.com:37730 with leader id 00000000-0000-0000-0000-000000000000.
JobManager Web Interface: http://data-hadoop-50-63.bjrs.xxx.com:37730
JobManager Web Interface: http://data-hadoop-50-63.bjrs.xxx.com:37730
是jobManager的ui，查看yarn上的任务，发现0004一直是运行状态
点击ApplicationMaster，看到有一个任务运行完成了。
这个任务是通过下面的命令提交的，运行任务之前要把flink 下的LICENSE文件上传到hdfs
./bin/flink run ./examples/batch/WordCount.jar -input hdfs://192.168.50.63:9000/LICENSE -output hdfs://192.168.50.63:9000/wordcount-result_1.txt
运行日志如下：
[iknow@data-hadoop-50-63 flink-1.7.2]$ ./bin/flink run ./examples/batch/WordCount.jar -input hdfs://192.168.50.63:9000/LICENSE -output hdfs://192.168.50.63:9000/wordcount-result_1.txt
2019-02-22 11:36:58,256 INFO org.apache.flink.yarn.cli.FlinkYarnSessionCli - Found Yarn properties file under /tmp/.yarn-properties-iknow.
2019-02-22 11:36:58,256 INFO org.apache.flink.yarn.cli.FlinkYarnSessionCli - Found Yarn properties file under /tmp/.yarn-properties-iknow.
2019-02-22 11:36:58,504 INFO org.apache.flink.yarn.cli.FlinkYarnSessionCli - YARN properties set default parallelism to 2
2019-02-22 11:36:58,504 INFO org.apache.flink.yarn.cli.FlinkYarnSessionCli - YARN properties set default parallelism to 2
YARN properties set default parallelism to 2
2019-02-22 11:36:58,543 INFO org.apache.hadoop.yarn.client.RMProxy - Connecting to ResourceManager at /0.0.0.0:8032
2019-02-22 11:36:58,633 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
2019-02-22 11:36:58,633 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
2019-02-22 11:36:58,699 INFO org.apache.flink.yarn.AbstractYarnClusterDescriptor - Found application JobManager host name 'data-hadoop-50-63.bjrs.zybang.com' and port '37730' from supplied application id 'application_1550804667415_0004'
Starting execution of program
Program execution finished
Job with JobID 6d1a05cfd324111904bb1749c50ef5d6 has finished.
Job Runtime: 9851 ms
Flink ui上点击进这个任务
对于客户端模式而言，你可以启动多个yarn session，一个yarn session模式对应一个JobManager,并按照需求提交作业，同一个Session中可以提交多个Flink作业。如果想要停止Flink Yarn Application，需要通过yarn application -kill命令来停止.
[iknow@data-hadoop-50-63 ~]$ yarn application -kill application_1550836652097_0002
杀掉任务之后再查看进程
[iknow@data-hadoop-50-63 ~]$ jps
1809 Jps
186144 SecondaryNameNode
301825 ResourceManager
185845 DataNode
76966 Worker
302171 NodeManager
185597 NameNode
[iknow@data-hadoop-50-64 ~]$ jps
269842 Jps
248059 NodeManager
39624 Worker
246509 DataNode
分离式模式
对于分离式模式，并不像客户端那样可以启动多个yarn session，如果启动多个，会出现下面的session一直处在等待状态。JobManager的个数只能是一个，同一个Session中可以提交多个Flink作业。如果想要停止Flink Yarn Application，需要通过yarn application -kill命令来停止。通过-d指定分离模式，即客户端在启动Flink Yarn Session后，就不再属于Yarn Cluster的一部分。
yarn-session启动的时候可以指定-nm的参数，这个就是给你的yarn-session起一个名字。比如
bin/yarn-session.sh -nm yarn-session_test
这个时候要停止该yarn-session.sh必须要用yarn的命令了yarn application –kill <appid>
[iknow@data-hadoop-50-63 ~]$ yarn application -kill application_1550836652097_0007
19/02/24 09:13:11 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
Killing application application_1550836652097_0006
19/02/24 09:13:12 INFO impl.YarnClientImpl: Killed application application_1550836652097_0006
Flink run 方式提交
对于前面介绍的yarn session需要先启动一个集群，然后在提交作业。对于Flink run直接提交作业就相对比较简单，不需要额外的去启动一个集群，直接提交作业，即可完成Flink作业。
flink run参数介绍：
-c：如果没有在jar包中指定入口类，则需要在这里通过这个参数指定;
-m：指定需要连接的jobmanager(主节点)地址，使用这个参数可以指定一个不同于配置文件中的jobmanager，可以说是yarn集群名称;
-p：指定程序的并行度。可以覆盖配置文件中的默认值;
-n:允许跳过保存点状态无法恢复。 你需要允许如果您从中删除了一个运算符你的程序是的一部分保存点时的程序触发;
-q:如果存在，则禁止将日志记录输出标准出来;
-s:保存点的路径以还原作业来自（例如hdfs:///flink/savepoint-1537);
还有参数如果在yarn-session当中没有指定，可以在yarn-session参数的基础上前面加“y”，即可控制所有的资源，这里就不獒述了。
./bin/flink run -m yarn-cluster -yn 1 -yjm 1024 -ytm 1024 ./examples/batch/WordCount.jar
日志如下：
[iknow@data-hadoop-50-63 flink-1.7.2]$ ./bin/flink run -m yarn-cluster -yn 1 -yjm 1024 -ytm 1024 ./examples/batch/WordCount.jar
2019-02-21 19:54:06,718 INFO org.apache.hadoop.yarn.client.RMProxy - Connecting to ResourceManager at /0.0.0.0:8032
2019-02-21 19:54:06,815 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
2019-02-21 19:54:06,815 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
2019-02-21 19:54:06,822 INFO org.apache.flink.yarn.cli.FlinkYarnSessionCli - The argument yn is deprecated in will be ignored.
2019-02-21 19:54:06,822 INFO org.apache.flink.yarn.cli.FlinkYarnSessionCli - The argument yn is deprecated in will be ignored.
2019-02-21 19:54:06,931 INFO org.apache.flink.yarn.AbstractYarnClusterDescriptor - Cluster specification: ClusterSpecification{masterMemoryMB=1024, taskManagerMemoryMB=1024, numberTaskManagers=1, slotsPerTaskManager=1}
2019-02-21 19:54:07,231 WARN org.apache.flink.yarn.AbstractYarnClusterDescriptor - The configuration directory ('/home/iknow/zhangzhiyong/flink-1.7.2/conf') contains both LOG4J and Logback configuration files. Please delete or rename one of them.
2019-02-21 19:54:10,039 INFO org.apache.flink.yarn.AbstractYarnClusterDescriptor - Submitting application master application_1550749966977_0002
2019-02-21 19:54:10,400 INFO org.apache.hadoop.yarn.client.api.impl.YarnClientImpl - Submitted application application_1550749966977_0002
2019-02-21 19:54:10,400 INFO org.apache.flink.yarn.AbstractYarnClusterDescriptor - Waiting for the cluster to be allocated
2019-02-21 19:54:10,403 INFO org.apache.flink.yarn.AbstractYarnClusterDescriptor - Deploying cluster, current state ACCEPTED
2019-02-21 19:54:14,451 INFO org.apache.flink.yarn.AbstractYarnClusterDescriptor - YARN application has been deployed successfully.
Starting execution of program
Executing WordCount example with default input data set.
Use --input to specify file input.
Printing result to stdout. Use --output to specify output path.
(a,5)
(action,1)
(after,1)
(against,1)
(all,2)
(and,12)
(arms,1)
(arrows,1)
(awry,1)
(ay,1)
(bare,1)
(be,4)
(bear,3)
(bodkin,1)
(bourn,1)
(but,1)
(by,2)
(calamity,1)
(cast,1)
(coil,1)
(come,1)
(conscience,1)
(consummation,1)
(contumely,1)
(country,1)
(cowards,1)
(currents,1)
(d,4)
(death,2)
(delay,1)
(despis,1)
(devoutly,1)
(die,2)
(does,1)
(dread,1)
(dream,1)
(dreams,1)
(end,2)
(enterprises,1)
(er,1)
(fair,1)
(fardels,1)
(flesh,1)
(fly,1)
(for,2)
(fortune,1)
(from,1)
(give,1)
(great,1)
(grunt,1)
(have,2)
(he,1)
(heartache,1)
(heir,1)
(himself,1)
(his,1)
(hue,1)
(ills,1)
(in,3)
(insolence,1)
(is,3)
(know,1)
(law,1)
(life,2)
(long,1)
(lose,1)
(love,1)
(make,2)
(makes,2)
(man,1)
(may,1)
(merit,1)
(might,1)
(mind,1)
(moment,1)
(more,1)
(mortal,1)
(must,1)
(my,1)
(name,1)
(native,1)
(natural,1)
(no,2)
(nobler,1)
(not,2)
(now,1)
(nymph,1)
(o,1)
(of,15)
(off,1)
(office,1)
(ophelia,1)
(opposing,1)
(oppressor,1)
(or,2)
(orisons,1)
(others,1)
(outrageous,1)
(pale,1)
(pangs,1)
(patient,1)
(pause,1)
(perchance,1)
(pith,1)
(proud,1)
(puzzles,1)
(question,1)
(quietus,1)
(rather,1)
(regard,1)
(remember,1)
(resolution,1)
(respect,1)
(returns,1)
(rub,1)
(s,5)
(say,1)
(scorns,1)
(sea,1)
(shocks,1)
(shuffled,1)
(sicklied,1)
(sins,1)
(sleep,5)
(slings,1)
(so,1)
(soft,1)
(something,1)
(spurns,1)
(suffer,1)
(sweat,1)
(take,1)
(takes,1)
(than,1)
(that,7)
(the,22)
(their,1)
(them,1)
(there,2)
(these,1)
(this,2)
(those,1)
(thought,1)
(thousand,1)
(thus,2)
(thy,1)
(time,1)
(tis,2)
(to,15)
(traveller,1)
(troubles,1)
(turn,1)
(under,1)
(undiscover,1)
(unworthy,1)
(us,3)
(we,4)
(weary,1)
(what,1)
(when,2)
(whether,1)
(whips,1)
(who,2)
(whose,1)
(will,1)
(wish,1)
(with,3)
(would,2)
(wrong,1)
(you,1)
Program execution finished
Job with JobID 8fb799387976bc6426b3ebdcf1e80dfd has finished.
Job Runtime: 8027 ms
Accumulator Results:
- 464ea1424ef5011784a0d0cbc837baba (java.util.ArrayList) [170 elements]
Yarn上也是有flink任务的
程序正在运行，过一段时间再看，状态是successed
192.168.50.63上测试从hdfs读入数据，写到hdfs
[iknow@data-hadoop-50-63 flink-1.7.2]$ hdfs dfs -put LICENSE /
[iknow@data-hadoop-50-63 flink-1.7.2]$ ./bin/flink run -m yarn-cluster -yn 1 -yjm 1024 -ytm 1024 ./examples/batch/WordCount.jar -input hdfs://192.168.50.63:9000/LICENSE -output hdfs://192.168.50.63:9000/wordcount-result.txt
运行日志如下：
[iknow@data-hadoop-50-63 flink-1.7.2]$ ./bin/flink run -m yarn-cluster -yn 1 -yjm 1024 -ytm 1024 ./examples/batch/WordCount.jar -input hdfs://192.168.50.63:9000/LICENSE -output hdfs://192.168.50.63:9000/wordcount-result.txt
2019-02-22 11:20:26,060 INFO org.apache.hadoop.yarn.client.RMProxy - Connecting to ResourceManager at /0.0.0.0:8032
2019-02-22 11:20:26,154 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
2019-02-22 11:20:26,154 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
2019-02-22 11:20:26,161 INFO org.apache.flink.yarn.cli.FlinkYarnSessionCli - The argument yn is deprecated in will be ignored.
2019-02-22 11:20:26,161 INFO org.apache.flink.yarn.cli.FlinkYarnSessionCli - The argument yn is deprecated in will be ignored.
2019-02-22 11:20:26,273 INFO org.apache.flink.yarn.AbstractYarnClusterDescriptor - Cluster specification: ClusterSpecification{masterMemoryMB=1024, taskManagerMemoryMB=1024, numberTaskManagers=1, slotsPerTaskManager=1}
2019-02-22 11:20:26,581 WARN org.apache.flink.yarn.AbstractYarnClusterDescriptor - The configuration directory ('/home/iknow/zhangzhiyong/flink-1.7.2/conf') contains both LOG4J and Logback configuration files. Please delete or rename one of them.
2019-02-22 11:20:28,005 INFO org.apache.flink.yarn.AbstractYarnClusterDescriptor - Submitting application master application_1550804667415_0003
2019-02-22 11:20:28,028 INFO org.apache.hadoop.yarn.client.api.impl.YarnClientImpl - Submitted application application_1550804667415_0003
2019-02-22 11:20:28,028 INFO org.apache.flink.yarn.AbstractYarnClusterDescriptor - Waiting for the cluster to be allocated
2019-02-22 11:20:28,030 INFO org.apache.flink.yarn.AbstractYarnClusterDescriptor - Deploying cluster, current state ACCEPTED
2019-02-22 11:20:31,563 INFO org.apache.flink.yarn.AbstractYarnClusterDescriptor - YARN application has been deployed successfully.
Starting execution of program
Program execution finished
Job with JobID 16e83cc5a60f9cd82ed39d72bcc62110 has finished.
Job Runtime: 8281 ms
查看文件wordcount-result.txt是有数据的
参考：
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