Flink常见的部署模式
Flink部署、执行模式
Flink的部署模式
本地模式、Standalone模式和FlinkonYARN模式是Flink的三种常见部署模式。
1.Local本地模式：
在本地模式下，Flink以单机模式运行，无需启动分布式资源管理器。这种模式适用于本地开发和测试，用于验证Flink代码的正确性和性能。
2.Standalone模式：
在Standalone模式下，Flink作为一个独立的集群运行。需要启动Flink的JobManager和TaskManager，JobManager负责接收和调度任务，而TaskManager负责执行任务。
3.Flink on YARN模式：
在FlinkonYARN模式下，Flink在YARN（Hadoop的资源调度和集群管理系统）之上运行。Flink作为一个YARN应用程序，利用YARN来管理资源分配和任务调度。使用这种模式，可以充分利用Hadoop集群的资源，实现Flink的分布式计算。
Flink的执行模式
Flink可以通过以下三种方式之一执行应用程序：
1.Session Mode：会话模式
会话模式需要先启动一个集群，保持一个会话，在这个会话中通过客户端提交作业。集群启动时所有资源就都已经确定，所有提交的作业会竞争集群中的资源。适合任务规模小，执行时间短的大量作业。
Flink的作业执行环境会一直保留在集群上，直到会话被显式终止。这样，可以提交多个作业，它们可以共享相同的集群资源和状态，从而实现更高的效率和资源利用。
2.Per-Job Mode：单作业模式
每个Flink应用程序作为一个独立的作业被提交和执行。
每次提交的Flink应用程序都会创建一个独立的作业执行环境，该作业执行环境仅用于执行该特定的作业。
当作业完成后，作业执行环境会被释放，集群关闭，资源释放
3.Application Mode：应用模式
应用模式算是前2种模式的升级，前2种模式中，Flink程序代码是在客户端执行，然后客户端提交给JobManager，客户端需要占用大量网络带宽。
应用模式需要为每一个提交的应用单独启动一个JobManager（应用程序在JobManager执行），也就是创建一个集群。这个JobManager只为执行这一个应用而存在，执行结束之后JobManager关闭。
4.三种模式的区别：
集群生命周期和资源隔离保证
应用程序的main()方法是在客户端还是在集群上执行
Local本地模式
Local模式是Flink提供的最简单部署模式，可以在单台服务器上运行，适用于日常的开发和调试。
注意：Flink的运行依赖JAVA环境，需要预先安装好JDK
下载安装
Flink下载地址：
https://archive.apache.org/dist/flink/
下载Flink
wget https://repo.huaweicloud.com/apache/flink/flink-1.17.0/flink-1.17.0-bin-scala_2.12.tgz
解压、重命名
tar -zxvf flink-1.17.0-bin-scala_2.12.tgz
mv flink-1.17.0 flink
启动、停止Flink
不需要进行任何配置，直接使用Flink默认配置，直接运行脚本启动
bin/start-cluster.sh
停止Flink
bin/stop-cluster.sh
直接访问：
http://IP:8081
，可以看到Flink的后台管理界面
每个taskmanager有3个solt
提交测试任务
提交一个测试任务：
./bin/flink run examples/batch/WordCount.jar
在控制台直接看到输出
[root@node01 flink]# ./bin/flink run examples/batch/WordCount.jar
SLF4J:Class path contains multiple SLF4J bindings.SLF4J:Found binding in [jar:file:/usr/local/program/flink/lib/log4j-slf4j-impl-2.17.1.jar!/org/slf4j/impl/StaticLoggerBinder.class]SLF4J:Found binding in [jar:file:/usr/local/program/hadoop/share/hadoop/common/lib/slf4j-log4j12-1.7.25.jar!/org/slf4j/impl/StaticLoggerBinder.class]SLF4J:See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.SLF4J:Actual binding is of type [org.apache.logging.slf4j.Log4jLoggerFactory]ExecutingWordCount example withdefault input data set.
Use--input tospecify file input.
Printing result tostdout.Use--output tospecify output path.
Job has been submitted withJobID a946d0abf84ac6848a823cec43f7056f
Program execution finished
JobwithJobID a946d0abf84ac6848a823cec43f7056f has finished.
JobRuntime:584 ms
AccumulatorResults:-1a50b4c9582d4d35a854872c62391768 (java.util.ArrayList)[170 elements](a,5)(action,1)(after,1)(against,1)(all,2)(and,12)(arms,1)(arrows,1)(awry,1)
同样，在Flink的后台管理界面 Completed Jobs 一栏可以看到刚才提交执行的程序：
停止作业
可以直接在 WEB 界面上点击对应作业的 Cancel Job 按钮进行取消，也可以使用命令行进行取消。
使用命令行进行取消时，需要先获取到作业的JobId
bin/flink list
获取到JobId后，使用
flink cancel JobId
命令取消作业
bin/flink cancel a946d0abf84ac6848a823cec43f7056f
Standalone独立模式
Standalone模式是集群模式的一种，独立模式是独立运行的，不依赖任何外部的资源管理平台，存在资源不足，出现故障不会自动扩展或重分配资源的能力，一般用在开发测试或作业非常少的场景下。
优缺点：
部署相对简单，可以支持小规模，少量的任务运行
缺少系统层面对集群中Job的管理，容易遭成资源分配不均匀
资源隔离相对简单，任务之间资源竞争严重
会话模式
会话模式部署需要先启动集群，集群资源固定，通过Web页面客户端提交任务，可以多个任务。
搭建一个Flink集群，参考：搭建Flink集群、集群HA高可用以及配置历史服务器
1.启动 Flink 集群：
通过
bin/start-cluster.sh脚本启动集群
2.打开Flink Web UI
在浏览器中输入
http://node01:8081/地址打开Flink Web UI
3.提交Flink作业
在Flink Web UI中选择要提交的 Flink 作业 jar 包，并指定作业参数和作业名称。
bin/flink run ../examples/streaming/WordCount.jar
4.查看Flink作业
提交作业之后，在 Flink Web UI 上会看到作业的运行状态，可以查看作业日志和监控指标等信息。
5.停止Flink作业
可以在Flink Web UI中停止作业，也可以使用
bin/flink cancel jobID命令停止指定的作业
单作业模式
Standalone集群并不支持单作业模式部署，单作业模式需要借助一些资源管理平台。
应用模式
应用模式下不会提前创建集群，因此不能调用
start-cluster.sh脚本，但是可以使用在bin目录下的
standalone-job.sh来创建一个JobManager。
1.将Flink应用程序的jar包放到Flink的安装路径下的lib目录下。
[root@node01 flink]# mv /root/demo-1.0-SNAPSHOT.jar lib
2.启动netcat
[root@node01~]# nc -lk 8888
3.启动JobManager
直接指定作业入口类，脚本会到lib目录扫描所有的jar包
[root@node01 flink]# bin/standalone-job.sh start --job-classname cn.ybzy.demo.WordCountDemoStarting standalonejob daemon on host node01.
4.启动TaskManager
[root@node01 flink]# bin/taskmanager.sh start
Starting taskexecutor daemon on host node01.
5.查看进程
[root@node01 flink]# jps
11973Jps11240TaskManagerRunner11898StandaloneApplicationClusterEntryPoint
6.查看Web UI
一直是如下所示状态，明显异常：
查看
flink/log/flink-root-standalonejob-1-node01.log
日志
1.异常提示资源不够：
Caused by:java.util.concurrent.CompletionException:org.apache.flink.runtime.jobmanager.scheduler.NoResourceAvailableException:Could not acquire the minimum required resources.
at java.util.concurrent.CompletableFuture.encodeThrowable(CompletableFuture.java:292)~[?:1.8.0_371]
at java.util.concurrent.CompletableFuture.completeThrowable(CompletableFuture.java:308)~[?:1.8.0_371]
at java.util.concurrent.CompletableFuture.uniApply(CompletableFuture.java:607)~[?:1.8.0_371]
at java.util.concurrent.CompletableFuture$UniApply.tryFire(CompletableFuture.java:591)~[?:1.8.0_371]
at java.util.concurrent.CompletableFuture.postComplete(CompletableFuture.java:488)~[?:1.8.0_371]
at java.util.concurrent.CompletableFuture.completeExceptionally(CompletableFuture.java:1990)~[?:1.8.0_371]
修改配置文件，调大资源，发现无效。
# jobmanager.memory.process.size:1600m
jobmanager.memory.process.size:2000m
#taskmanager.memory.process.size:1728m
taskmanager.memory.process.size:2600m
后来仔细观察日志，发现一处核心异常如下异常：
org.apache.flink.runtime.resourcemanager.slotmanager.DeclarativeSlotManager[]-Received resource requirements from job 6f4f54c45d7bb59531f537b966776793:[ResourceRequirement{resourceProfile=ResourceProfile{UNKNOWN}, numberOfRequiredSlots=3}]
关键词
numberOfRequiredSlots=3
尤为重要，JobManager启动默认只有1Slot，Slot请求资源不够！
编辑conf/flink-conf.yaml文件
# taskmanager.numberOfTaskSlots:1
# 修改Slot数量为3
taskmanager.numberOfTaskSlots:3
停止taskmanager、standalone-job，重新启动，Web UI显示明显正常
发送测试数据
[root@node01~]# nc -lk 8888
abc bcd cdf
7.停止集群
[root@node01 flink]# bin/taskmanager.sh stop
Stopping taskexecutor daemon (pid:14117) on host node01.[root@node01 flink]# bin/standalone-job.sh stop
No standalonejob daemon (pid:14813) is running anymore on node01.
8.总结：
在Flink中，Slot是Flink作业管理的资源基本单位，一个任务不一定会占用1个Slot。
当向Flink提交一个任务时，Flink会为该任务分配所需的Slot数量。通常取决于以下几个因素：
任务的并行度（Parallelism）：如果任务的并行度很高，即需要同时执行多个子任务，则可能需要使用多个Slot。
TaskManager的资源：如果TaskManager的资源非常丰富，例如拥有多个CPU或GPU核心，则可以分配更多的Slot来运行任务。反之，则可能只能分配较少的Slot。
任务的资源需求：如果任务需要大量的内存或计算资源，则可能需要分配更多的Slot来满足需求。
个人在编写Flink程序时，设置了并行度，打包上传运行，由于JobManager的默认numberOfTaskSlots配置为1，Solt数量不够，故出现上述异常。
StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(3);
YARN运行模式
客户端把Flink应用提交给Yarn的ResourceManager，Yarn的ResourceManager会向Yarn的NodeManager申请容器。在这些容器上，Flink会部署JobManager和TaskManager的实例，从而启动集群。Flink会根据运行在JobManger上的作业所需要的Slot数量动态分配TaskManager资源。
1.安装Hadoop
安装Hadoop参考：搭建Hadoop3.X完全分布式集群环境
2.配置环境变量
# Hadoop
export HADOOP_HOME=/usr/local/program/hadoop
export PATH=$HADOOP_HOME/bin:$HADOOP_HOME/sbin:$PATH
# Flink
export HADOOP_CONF_DIR=${HADOOP_HOME}/etc/hadoop
export HADOOP_CLASSPATH=`hadoop classpath`
3.启动Hadoop集群，包括HDFS和YARN
[root@node01 hadoop]# sbin/start-all.sh
4.启动netcat
nc -lk 8888
会话模式
YARN的会话模式需要首先申请一个YARN会话（YARN Session）来启动Flink集群。
启动Hadoop集群
启动Hadoop集群，包括HDFS和YARN
[root@node01 hadoop]# sbin/start-all.sh
申请一个YARN会话
查看
yarn-session.sh
命令帮助
[root@node01 flink]# bin/yarn-session.sh --help
Usage:Optional-at,--applicationType <arg>Set a custom application type for the application on YARN-D<property=value> use value for given property
-d,--detached If present, runs the job in detached mode
-h,--help Helpfor the Yarn session CLI.-id,--applicationId <arg>AttachtorunningYARN session
-j,--jar <arg>PathtoFlink jar file
-jm,--jobManagerMemory <arg>MemoryforJobManagerContainerwithoptional unit (default:MB)-m,--jobmanager <arg>Settoyarn-cluster touseYARN execution mode.-nl,--nodeLabel <arg>SpecifyYARN 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>SpecifyYARN queue.-s,--slots <arg>Number of slots per TaskManager-t,--ship <arg>Ship files in the specified directory (t for transfer)-tm,--taskManagerMemory <arg>Memory per TaskManagerContainerwithoptional unit (default:MB)-yd,--yarndetached If present, runs the job in detached mode (deprecated; use non-YARN specific option instead)-z,--zookeeperNamespace <arg>Namespacetocreate the Zookeeper sub-paths for high availability mode
主要参数：
-d：分离模式，让FlinkYARN客户端后台运行，即YARN session可以后台运行
-jm（--jobManagerMemory）：配置JobManager所需内存，默认单位MB-nm（--name）：配置在YARNUI界面上显示的任务名
-qu（--queue）：指定YARN队列名
-tm（--taskManager）：配置每个TaskManager所使用内存
执行脚本命令向YARN集群申请资源，开启一个YARN会话，启动Flink集群
[root@node01 flink]# bin/yarn-session.sh -nm flink-test
......2023-06-1222:03:01,088INFOorg.apache.hadoop.hdfs.protocol.datatransfer.sasl.SaslDataTransferClient[]-SASL encryption trust check: localHostTrusted =false, remoteHostTrusted =false2023-06-1222:03:01,428INFOorg.apache.hadoop.hdfs.protocol.datatransfer.sasl.SaslDataTransferClient[]-SASL encryption trust check: localHostTrusted =false, remoteHostTrusted =false2023-06-1222:03:01,457INFOorg.apache.hadoop.hdfs.protocol.datatransfer.sasl.SaslDataTransferClient[]-SASL encryption trust check: localHostTrusted =false, remoteHostTrusted =false2023-06-1222:03:01,476INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-Cannot use kerberos delegation token manager, no valid kerberos credentials provided.2023-06-1222:03:01,480INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-Submitting application master application_1686577483648_0001
2023-06-1222:03:01,613INFOorg.apache.hadoop.yarn.client.api.impl.YarnClientImpl[]-Submitted application application_1686577483648_0001
2023-06-1222:03:01,613INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-Waitingfor the cluster tobe allocated
2023-06-1222:03:01,615INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-Deploying cluster, current state ACCEPTED2023-06-1222:03:06,406INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-YARN application has been deployed successfully.2023-06-1222:03:06,407INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-FoundWebInterface node03:37824 of application 'application_1686577483648_0001'.JobManagerWebInterface: http://node03:37824
查看Yarn、Flink
访问
http://node01:8088/cluster
查看yarn
YARN Session启动之后会给出一个Web UI地址以及一个YARN application ID
2023-06-1222:03:06,406INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-YARN application has been deployed successfully.2023-06-1222:03:06,407INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-FoundWebInterface node03:37824 of application 'application_1686577483648_0001'.JobManagerWebInterface: http://node03:37824
访问给出的地址：
http://node03:37824
提交作业
可以通过Web UI或者命令行两种方式提交作业
a.通过Web UI提交作业
b.通过命令行提交作业
1.将Flink程序打Jar包并上传至集群
2.执行命令将任务提交到已经开启的Yarn-Session中运行
客户端可以自行确定JobManager的地址，也可以通过-m或者-jobmanager参数指定JobManager的地址。同时JobManager的地址在YARN Session的启动页面中可以找到。
[root@node01~]# /usr/local/program/flink/bin/flink run -c cn.ybzy.demo.WordCountDemo/root/demo-1.0-SNAPSHOT.jar
2023-06-1222:21:08,468INFOorg.apache.flink.yarn.cli.FlinkYarnSessionCli[]-FoundYarn properties file under /tmp/.yarn-properties-root.2023-06-1222:21:08,468INFOorg.apache.flink.yarn.cli.FlinkYarnSessionCli[]-FoundYarn properties file under /tmp/.yarn-properties-root.2023-06-1222:21:08,824WARNorg.apache.flink.yarn.configuration.YarnLogConfigUtil[]-The configuration directory ('/usr/local/program/flink/conf') already contains a LOG4J config file.If you want touse logback, then please delete or rename the log configuration file.2023-06-1222:21:08,860INFOorg.apache.hadoop.yarn.client.RMProxy[]-ConnectingtoResourceManager at node01/192.168.1.100:80322023-06-1222:21:08,986INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-No path for the flink jar passed. Using the location of classorg.apache.flink.yarn.YarnClusterDescriptortolocate the jar
2023-06-1222:21:09,049INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-FoundWebInterface node03:37824 of application 'application_1686577483648_0001'.Job has been submitted withJobID cdf1ff7b48472b3d7bc413a1ee9700e8
查看、测试作业
通过Flink的Web UI页面查看提交任务的运行情况，Flink会根据运行在JobManger上的作业所需要的Slot数量动态分配TaskManager资源。
发送数据测试
[root@node01 program]# nc -lk 8888
abc bcd cdf
单作业模式
在YARN环境中，由于有了外部平台做资源调度，因此也可以直接向YARN提交一个单独的作业，从而启动一个Flink集群。
提交作业
执行命令提交作业
[root@node01 flink]# bin/flink run -t yarn-per-job -c cn.ybzy.demo.WordCountDemo/root/demo-1.0-SNAPSHOT.jar
.....2023-06-1222:46:26,984INFOorg.apache.hadoop.hdfs.protocol.datatransfer.sasl.SaslDataTransferClient[]-SASL encryption trust check: localHostTrusted =false, remoteHostTrusted =false2023-06-1222:46:27,009INFOorg.apache.hadoop.hdfs.protocol.datatransfer.sasl.SaslDataTransferClient[]-SASL encryption trust check: localHostTrusted =false, remoteHostTrusted =false2023-06-1222:46:27,029INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-Cannot use kerberos delegation token manager, no valid kerberos credentials provided.2023-06-1222:46:27,034INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-Submitting application master application_1686577483648_0004
2023-06-1222:46:27,061INFOorg.apache.hadoop.yarn.client.api.impl.YarnClientImpl[]-Submitted application application_1686577483648_0004
2023-06-1222:46:27,061INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-Waitingfor the cluster tobe allocated
2023-06-1222:46:27,063INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-Deploying cluster, current state ACCEPTED2023-06-1222:46:31,086INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-YARN application has been deployed successfully.2023-06-1222:46:31,087INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-FoundWebInterface node02:42192 of application 'application_1686577483648_0004'.Job has been submitted withJobID dfcb72ebf4a5f33d8e7967d6beaaf96d
注意：在使用
-d
参数启动时，启动过程中可能会出现如下异常：
Exception in thread "Thread-5"java.lang.IllegalStateException:Tryingtoaccess closed classloader. Please check if you store classloaders directly or indirectly in staticfields. If the stacktrace suggests that the leak occurs in a third party library and cannot be fixed immediately, you can disable this check withthe configuration 'classloader.check-leaked-classloader'.
at org.apache.flink.util.FlinkUserCodeClassLoaders$SafetyNetWrapperClassLoader.ensureInner(FlinkUserCodeClassLoaders.java:184)
at org.apache.flink.util.FlinkUserCodeClassLoaders$SafetyNetWrapperClassLoader.getResource(FlinkUserCodeClassLoaders.java:208)
at org.apache.hadoop.conf.Configuration.getResource(Configuration.java:2780)
at org.apache.hadoop.conf.Configuration.getStreamReader(Configuration.java:3036)
at org.apache.hadoop.conf.Configuration.loadResource(Configuration.java:2995)
at org.apache.hadoop.conf.Configuration.loadResources(Configuration.java:2968)
at org.apache.hadoop.conf.Configuration.getProps(Configuration.java:2848)
at org.apache.hadoop.conf.Configuration.get(Configuration.java:1200)
at org.apache.hadoop.conf.Configuration.getTimeDuration(Configuration.java:1812)
at org.apache.hadoop.conf.Configuration.getTimeDuration(Configuration.java:1789)
at org.apache.hadoop.util.ShutdownHookManager.getShutdownTimeout(ShutdownHookManager.java:183)
at org.apache.hadoop.util.ShutdownHookManager.shutdownExecutor(ShutdownHookManager.java:145)
at org.apache.hadoop.util.ShutdownHookManager.access$300(ShutdownHookManager.java:65)
at org.apache.hadoop.util.ShutdownHookManager$1.run(ShutdownHookManager.java:102)
解决方案是在flink的
/conf/flink-conf.yaml
配置文件中设置
classloader.check-leaked-classloader:false
查看Yarn、Flink
访问
http://node01:8088/cluster
查看
打开Flink Web UI页面进行监控
a.访问启动日志中的JobManager地址，如：
node02:42192
b.也可以在
http://node01:8088/cluster
页面中跳转到Flink的Web UI界面
查看、取消作业
[root@node01 flink]# bin/flink list -t yarn-per-job -Dyarn.application.id=application_1686577483648_0004
2023-06-1222:55:43,755INFOorg.apache.flink.yarn.cli.FlinkYarnSessionCli[]-FoundYarn properties file under /tmp/.yarn-properties-root.2023-06-1222:55:43,755INFOorg.apache.flink.yarn.cli.FlinkYarnSessionCli[]-FoundYarn properties file under /tmp/.yarn-properties-root.2023-06-1222:55:43,864WARNorg.apache.flink.yarn.configuration.YarnLogConfigUtil[]-The configuration directory ('/usr/local/program/flink/conf') already contains a LOG4J config file.If you want touse logback, then please delete or rename the log configuration file.2023-06-1222:55:43,927INFOorg.apache.hadoop.yarn.client.RMProxy[]-ConnectingtoResourceManager at node01/192.168.1.100:80322023-06-1222:55:44,087INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-No path for the flink jar passed. Using the location of classorg.apache.flink.yarn.YarnClusterDescriptortolocate the jar
2023-06-1222:55:44,159INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-FoundWebInterface node02:42192 of application 'application_1686577483648_0004'.Waitingfor response...------------------Running/RestartingJobs-------------------12.06.202322:46:30: dfcb72ebf4a5f33d8e7967d6beaaf96d :FlinkStreamingJob(RUNNING)--------------------------------------------------------------No scheduled jobs.
取消作业
# 如果取消作业，整个Flink集群会停掉
bin/flink cancel -t yarn-per-job -Dyarn.application.id=application_XXXX <jobId>
[root@node01 flink]# bin/flink cancel -t yarn-per-job -Dyarn.application.id=application_1686577483648_0004 dfcb72ebf4a5f33d8e7967d6beaaf96d
SLF4J:Actual binding is of type [org.apache.logging.slf4j.Log4jLoggerFactory]2023-06-1222:57:06,430INFOorg.apache.flink.yarn.cli.FlinkYarnSessionCli[]-FoundYarn properties file under /tmp/.yarn-properties-root.2023-06-1222:57:06,430INFOorg.apache.flink.yarn.cli.FlinkYarnSessionCli[]-FoundYarn properties file under /tmp/.yarn-properties-root.
Cancelling job dfcb72ebf4a5f33d8e7967d6beaaf96d.2023-06-1222:57:06,560WARNorg.apache.flink.yarn.configuration.YarnLogConfigUtil[]-The configuration directory ('/usr/local/program/flink/conf') already contains a LOG4J config file.If you want touse logback, then please delete or rename the log configuration file.2023-06-1222:57:06,638INFOorg.apache.hadoop.yarn.client.RMProxy[]-ConnectingtoResourceManager at node01/192.168.1.100:80322023-06-1222:57:06,830INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-No path for the flink jar passed. Using the location of classorg.apache.flink.yarn.YarnClusterDescriptortolocate the jar
2023-06-1222:57:06,895INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-FoundWebInterface node02:42192 of application 'application_1686577483648_0004'.Cancelled job dfcb72ebf4a5f33d8e7967d6beaaf96d.
应用模式
应用模式同样非常简单，与单作业模式类似，直接执行flink run-application命令即可。
提交作业
执行命令提交作业
[root@node01 flink]# bin/flink run-application -t yarn-application -c cn.ybzy.demo.WordCountDemo/root/demo-1.0-SNAPSHOT.jar
2023-06-1223:01:00,465INFOorg.apache.hadoop.hdfs.protocol.datatransfer.sasl.SaslDataTransferClient[]-SASL encryption trust check: localHostTrusted =false, remoteHostTrusted =false2023-06-1223:01:00,751INFOorg.apache.hadoop.hdfs.protocol.datatransfer.sasl.SaslDataTransferClient[]-SASL encryption trust check: localHostTrusted =false, remoteHostTrusted =false2023-06-1223:01:00,799INFOorg.apache.hadoop.hdfs.protocol.datatransfer.sasl.SaslDataTransferClient[]-SASL encryption trust check: localHostTrusted =false, remoteHostTrusted =false2023-06-1223:01:00,817INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-Cannot use kerberos delegation token manager, no valid kerberos credentials provided.2023-06-1223:01:00,821INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-Submitting application master application_1686577483648_0005
2023-06-1223:01:00,847INFOorg.apache.hadoop.yarn.client.api.impl.YarnClientImpl[]-Submitted application application_1686577483648_0005
2023-06-1223:01:00,848INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-Waitingfor the cluster tobe allocated
2023-06-1223:01:00,849INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-Deploying cluster, current state ACCEPTED2023-06-1223:01:05,123INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-YARN application has been deployed successfully.2023-06-1223:01:05,124INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-FoundWebInterface node03:40762 of application 'application_1686577483648_0005'.
查看、取消作业
查看作业
[root@node01 flink]# bin/flink list -t yarn-application -Dyarn.application.id=application_1686577483648_0005
2023-06-1223:02:55,490INFOorg.apache.flink.yarn.cli.FlinkYarnSessionCli[]-FoundYarn properties file under /tmp/.yarn-properties-root.2023-06-1223:02:55,490INFOorg.apache.flink.yarn.cli.FlinkYarnSessionCli[]-FoundYarn properties file under /tmp/.yarn-properties-root.2023-06-1223:02:55,630WARNorg.apache.flink.yarn.configuration.YarnLogConfigUtil[]-The configuration directory ('/usr/local/program/flink/conf') already contains a LOG4J config file.If you want touse logback, then please delete or rename the log configuration file.2023-06-1223:02:55,689INFOorg.apache.hadoop.yarn.client.RMProxy[]-ConnectingtoResourceManager at node01/192.168.1.100:80322023-06-1223:02:55,844INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-No path for the flink jar passed. Using the location of classorg.apache.flink.yarn.YarnClusterDescriptortolocate the jar
2023-06-1223:02:55,905INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-FoundWebInterface node03:40762 of application 'application_1686577483648_0005'.Waitingfor response...------------------Running/RestartingJobs-------------------12.06.202323:01:05: a66d8fa98d23210d36b5b005ff0a1c53 :FlinkStreamingJob(RUNNING)--------------------------------------------------------------No scheduled jobs.
取消作业
[root@node01 flink]# bin/flink cancel -t yarn-application -Dyarn.application.id=application_1686577483648_0005 a66d8fa98d23210d36b5b005ff0a1c53
2023-06-1223:03:49,038INFOorg.apache.flink.yarn.cli.FlinkYarnSessionCli[]-FoundYarn properties file under /tmp/.yarn-properties-root.2023-06-1223:03:49,038INFOorg.apache.flink.yarn.cli.FlinkYarnSessionCli[]-FoundYarn properties file under /tmp/.yarn-properties-root.
Cancelling job a66d8fa98d23210d36b5b005ff0a1c53.2023-06-1223:03:49,156WARNorg.apache.flink.yarn.configuration.YarnLogConfigUtil[]-The configuration directory ('/usr/local/program/flink/conf') already contains a LOG4J config file.If you want touse logback, then please delete or rename the log configuration file.2023-06-1223:03:49,204INFOorg.apache.hadoop.yarn.client.RMProxy[]-ConnectingtoResourceManager at node01/192.168.1.100:80322023-06-1223:03:49,364INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-No path for the flink jar passed. Using the location of classorg.apache.flink.yarn.YarnClusterDescriptortolocate the jar
2023-06-1223:03:49,427INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-FoundWebInterface node03:40762 of application 'application_1686577483648_0005'.Cancelled job a66d8fa98d23210d36b5b005ff0a1c53.
从HDFS读取提交任务
通过
yarn.provided.lib.dirs配置选项指定位置，将flink的依赖上传到远程
将Flink本身的依赖和用户jar预先上传到HDFS，而不需要单独发送到集群，这就使得作业提交更加轻量了
上传flink的lib和plugins到HDFS上
[root@node01 flink]# hadoop fs -mkdir /flink-dist
[root@node01 flink]# hadoop fs -put lib//flink-dist
[root@node01 flink]# hadoop fs -put plugins//flink-dist
上传Flink开发程序jar包到HDFS
[root@node01 flink]# hadoop fs -mkdir /flink-jar
[root@node01 flink]# hadoop fs -put /root/demo-1.0-SNAPSHOT.jar /flink-jar
提交作业
[root@node01 flink]# bin/flink run-application -t yarn-application -Dyarn.provided.lib.dirs="hdfs://node01:9000/flink-dist"-c cn.ybzy.demo.WordCountDemo hdfs://node01:9000/flink-jar/demo-1.0-SNAPSHOT.jar
2023-06-1223:19:20,128INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-Cluster specification:ClusterSpecification{masterMemoryMB=2500, taskManagerMemoryMB=2200, slotsPerTaskManager=3}2023-06-1223:19:20,617INFOorg.apache.hadoop.hdfs.protocol.datatransfer.sasl.SaslDataTransferClient[]-SASL encryption trust check: localHostTrusted =false, remoteHostTrusted =false2023-06-1223:19:20,721INFOorg.apache.hadoop.hdfs.protocol.datatransfer.sasl.SaslDataTransferClient[]-SASL encryption trust check: localHostTrusted =false, remoteHostTrusted =false2023-06-1223:19:20,783INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-Cannot use kerberos delegation token manager, no valid kerberos credentials provided.2023-06-1223:19:20,788INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-Submitting application master application_1686577483648_0009
2023-06-1223:19:20,816INFOorg.apache.hadoop.yarn.client.api.impl.YarnClientImpl[]-Submitted application application_1686577483648_0009
2023-06-1223:19:20,816INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-Waitingfor the cluster tobe allocated
2023-06-1223:19:20,817INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-Deploying cluster, current state ACCEPTED2023-06-1223:19:24,086INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-YARN application has been deployed successfully.2023-06-1223:19:24,086INFOorg.apache.flink.yarn.YarnClusterDescriptor[]-FoundWebInterface node02:43653 of application 'application_1686577483648_0009'.
Yarn模式高可用
Standalone模式中, 同时启动多个Jobmanager, 一个为leader其他为standby, 当leader挂了, 其他的才会有一个成为leader
yarn的高可用是同时只启动一个Jobmanager, 当这个Jobmanager挂了之后, yarn会再次启动一个, 其实是利用的yarn的重试次数来实现的高可用
在yarn-site.xml中配置
<property><name>yarn.resourcemanager.am.max-attempts</name><value>4</value><description>The maximum number of application master execution attempts.</description></property>
在flink-conf.yaml中配置
# 次数应该小于yarn-site.xml中配置重试次数
yarn.application-attempts:3
high-availability.type: zookeeper
high-availability.storageDir: hdfs://node01:9000/flink/yarn/ha
high-availability.zookeeper.quorum: node01:2181,node02:2181,node03:2181
high-availability.zookeeper.path.root:/flink-yarn
启动yarn-session
[root@node01 flink]# bin/yarn-session.sh -nm flink-test
kill一个Jobmanager，查看复活情况
jps
kill -9 pid
本文转载自: https://blog.csdn.net/qq_38628046/article/details/131178976
版权归原作者 CodeDevMaster 所有， 如有侵权，请联系我们删除。
版权归原作者 CodeDevMaster 所有， 如有侵权，请联系我们删除。