- 尚硅谷大数据技术-教程-学习路线-笔记汇总表【课程资料下载】
- 视频地址：尚硅谷大数据Flink1.17实战教程从入门到精通_哔哩哔哩_bilibili
- 尚硅谷大数据Flink1.17实战教程-笔记01【Flink概述、Flink快速上手】
- 尚硅谷大数据Flink1.17实战教程-笔记02【Flink部署】
- 尚硅谷大数据Flink1.17实战教程-笔记03【】
- 尚硅谷大数据Flink1.17实战教程-笔记04【】
- 尚硅谷大数据Flink1.17实战教程-笔记05【】
- 尚硅谷大数据Flink1.17实战教程-笔记06【】
- 尚硅谷大数据Flink1.17实战教程-笔记07【】
- 尚硅谷大数据Flink1.17实战教程-笔记08【】
目录
基础篇
第03章 Flink部署
P011【011_Flink部署_集群角色】03:07
P012【012_Flink部署_集群搭建_集群启动】14:22
P013【013_Flink部署_集群搭建_WebUI提交作业】13:58
P014【014_Flink部署_集群搭建_命令行提交作业】03:46
P015【015_Flink部署_部署模式介绍】10:17
P016【016_Flink部署_Standalone运行模式】08:16
P017【017_Flink部署_YARN运行模式_环境准备】07:41
P018【018_Flink部署_YARN运行模式_会话模式】18:11
P019【019_Flink部署_YARN运行模式_会话模式的停止】04:10
P020【020_Flink部署_YARN运行模式_单作业模式】09:49
P021【021_Flink部署_YARN运行模式_应用模式】12:51
P022【022_Flink部署_历史服务器】08:11
基础篇
第03章 Flink部署
P011【011_Flink部署_集群角色】03:07
第 3 章 Flink 部署
3.1 集群角色
P012【012_Flink部署_集群搭建_集群启动】14:22
表3-1 集群角色分配 节点服务器
hadoop102
hadoop103
hadoop104
角色
JobManager
TaskManager
TaskManager
TaskManager
[atguigu@node001 module]$ cd flink
[atguigu@node001 flink]$ cd flink-1.17.0/
[atguigu@node001 flink-1.17.0]$ bin/start-cluster.sh
Starting cluster.
Starting standalonesession daemon on host node001.
Starting taskexecutor daemon on host node001.
Starting taskexecutor daemon on host node002.
Starting taskexecutor daemon on host node003.
[atguigu@node001 flink-1.17.0]$ jpsall
================ node001 ================
3408 Jps
2938 StandaloneSessionClusterEntrypoint
3276 TaskManagerRunner
================ node002 ================
2852 TaskManagerRunner
2932 Jps
================ node003 ================
2864 TaskManagerRunner
2944 Jps
[atguigu@node001 flink-1.17.0]$ 
P013【013_Flink部署_集群搭建_WebUI提交作业】13:58
3.2.2 向集群提交作业
<build><plugins><plugin><groupId>org.apache.maven.plugins</groupId><artifactId>maven-shade-plugin</artifactId><version>3.2.4</version><executions><execution><phase>package</phase><goals><goal>shade</goal></goals><configuration><artifactSet><excludes><exclude>com.google.code.findbugs:jsr305</exclude><exclude>org.slf4j:*</exclude><exclude>log4j:*</exclude></excludes></artifactSet><filters><filter><!-- Do not copy the signatures in the META-INF folder.Otherwise, this might cause SecurityExceptions when using the JAR. --><artifact>*:*</artifact><excludes><exclude>META-INF/*.SF</exclude><exclude>META-INF/*.DSA</exclude><exclude>META-INF/*.RSA</exclude></excludes></filter></filters><transformers combine.children="append"><transformerimplementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"></transformer></transformers></configuration></execution></executions></plugin></plugins>
</build>
com.atguigu.wc.WordCountStreamUnboundedDemo
P014【014_Flink部署_集群搭建_命令行提交作业】03:46
3.2.2 向集群提交作业
4）命令行提交作业
连接成功
Last login: Fri Jun 16 14:44:01 2023 from 192.168.10.1
[atguigu@node001 ~]$ cd /opt/module/flink/flink-1.17.0/
[atguigu@node001 flink-1.17.0]$ cd bin
[atguigu@node001 bin]$ ./start-cluster.sh
Starting cluster.
Starting standalonesession daemon on host node001.
Starting taskexecutor daemon on host node001.
Starting taskexecutor daemon on host node002.
Starting taskexecutor daemon on host node003.
[atguigu@node001 bin]$ jpsall
================ node001 ================
2723 TaskManagerRunner
2855 Jps
2380 StandaloneSessionClusterEntrypoint
================ node002 ================
2294 TaskManagerRunner
2367 Jps
================ node003 ================
2292 TaskManagerRunner
2330 Jps
[atguigu@node001 bin]$ cd ..
[atguigu@node001 flink-1.17.0]$ bin/flink run -m node001:8081 -c com.atguigu.wc.WordCountStreamUnboundedDemo ./
bin/ conf/ examples/ lib/ LICENSE licenses/ log/ NOTICE opt/ plugins/ README.txt
[atguigu@node001 flink-1.17.0]$ bin/flink run -m node001:8081 -c com.atguigu.wc.WordCountStreamUnboundedDemo ../
flink-1.17.0/ jar/
[atguigu@node001 flink-1.17.0]$ bin/flink run -m node001:8081 -c com.atguigu.wc.WordCountStreamUnboundedDemo ../jar/FlinkTutorial-1.17-1.0-SNAPSHOT.jar
Job has been submitted with JobID 59ae9d6532523b0c48cdb8b6c9105356
P015【015_Flink部署_部署模式介绍】10:17
3.3 部署模式
在一些应用场景中，对于集群资源分配和占用的方式，可能会有特定的需求。Flink为各种场景提供了不同的部署模式，主要有以下三种：会话模式（Session Mode）、单作业模式（Per-Job Mode）、应用模式（Application Mode）。
它们的区别主要在于：集群的生命周期以及资源的分配方式；以及应用的main方法到底在哪里执行——客户端（Client）还是JobManager。
P016【016_Flink部署_Standalone运行模式】08:16
3.4 Standalone运行模式（了解）
独立模式是独立运行的，不依赖任何外部的资源管理平台；当然独立也是有代价的：如果资源不足，或者出现故障，没有自动扩展或重分配资源的保证，必须手动处理。所以独立模式一般只用在开发测试或作业非常少的场景下。
[atguigu@node001 ~]$ cd /opt/module/flink/flink-1.17.0/bin
[atguigu@node001 bin]$ ./stop-cluster.sh
Stopping taskexecutor daemon (pid: 2723) on host node001.
Stopping taskexecutor daemon (pid: 2294) on host node002.
Stopping taskexecutor daemon (pid: 2292) on host node003.
Stopping standalonesession daemon (pid: 2380) on host node001.
[atguigu@node001 bin]$ jpsall
================ node001 ================
5120 Jps
================ node002 ================
3212 Jps
================ node003 ================
3159 Jps
[atguigu@node001 bin]$ ls
bash-java-utils.jar flink historyserver.sh kubernetes-session.sh sql-client.sh start-cluster.sh stop-zookeeper-quorum.sh zookeeper.sh
config.sh flink-console.sh jobmanager.sh kubernetes-taskmanager.sh sql-gateway.sh start-zookeeper-quorum.sh taskmanager.sh
find-flink-home.sh flink-daemon.sh kubernetes-jobmanager.sh pyflink-shell.sh standalone-job.sh stop-cluster.sh yarn-session.sh
[atguigu@node001 bin]$ cd ../lib/
[atguigu@node001 lib]$ ls
flink-cep-1.17.0.jar flink-dist-1.17.0.jar flink-table-api-java-uber-1.17.0.jar FlinkTutorial-1.17-1.0-SNAPSHOT.jar log4j-core-2.17.1.jar
flink-connector-files-1.17.0.jar flink-json-1.17.0.jar flink-table-planner-loader-1.17.0.jar log4j-1.2-api-2.17.1.jar log4j-slf4j-impl-2.17.1.jar
flink-csv-1.17.0.jar flink-scala_2.12-1.17.0.jar flink-table-runtime-1.17.0.jar log4j-api-2.17.1.jar
[atguigu@node001 lib]$ cd ../
[atguigu@node001 flink-1.17.0]$ bin/standalone-job.sh start --job-classname com.atguigu.wc.WordCountStreamUnboundedDemo
Starting standalonejob daemon on host node001.
[atguigu@node001 flink-1.17.0]$ jpsall
================ node001 ================
5491 StandaloneApplicationClusterEntryPoint
5583 Jps
================ node002 ================
3326 Jps
================ node003 ================
3307 Jps
[atguigu@node001 flink-1.17.0]$ bin/taskmanager.sh
Usage: taskmanager.sh (start|start-foreground|stop|stop-all)
[atguigu@node001 flink-1.17.0]$ bin/taskmanager.sh start
Starting taskexecutor daemon on host node001.
[atguigu@node001 flink-1.17.0]$ jpsall
================ node001 ================
5491 StandaloneApplicationClusterEntryPoint
5995 Jps
5903 TaskManagerRunner
================ node002 ================
3363 Jps
================ node003 ================
3350 Jps
[atguigu@node001 flink-1.17.0]$ bin/taskmanager.sh stop
Stopping taskexecutor daemon (pid: 5903) on host node001.
[atguigu@node001 flink-1.17.0]$ bin/standalone-job.sh stop
No standalonejob daemon (pid: 5491) is running anymore on node001.
[atguigu@node001 flink-1.17.0]$ xcall jps
=============== node001 ===============
6682 Jps
=============== node002 ===============
3429 Jps
=============== node003 ===============
3419 Jps
[atguigu@node001 flink-1.17.0]$ P017【017_Flink部署_YARN运行模式_环境准备】07:41
3.5 YARN运行模式（重点）
YARN上部署的过程是：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。
[atguigu@node001 flink-1.17.0]$ source /etc/profile.d/my_env.sh
[atguigu@node001 flink-1.17.0]$ myhadoop.sh s
Input Args Error...
[atguigu@node001 flink-1.17.0]$ myhadoop.sh start================ 启动 hadoop集群 ================---------------- 启动 hdfs ----------------
Starting namenodes on [node001]
Starting datanodes
Starting secondary namenodes [node003]--------------- 启动 yarn ---------------
Starting resourcemanager
Starting nodemanagers--------------- 启动 historyserver ---------------
[atguigu@node001 flink-1.17.0]$ jpsall
================ node001 ================
9200 JobHistoryServer
8416 NameNode
8580 DataNode
9284 Jps
8983 NodeManager
================ node002 ================
3892 ResourceManager
3690 DataNode
4365 Jps
4015 NodeManager
================ node003 ================
3680 DataNode
3778 SecondaryNameNode
3911 NodeManager
4044 Jps
[atguigu@node001 flink-1.17.0]$ P018【018_Flink部署_YARN运行模式_会话模式】18:11
[atguigu@node001 bin]$ ./yarn-session.sh --help
[atguigu@node001 bin]$ ./yarn-session.sh
[atguigu@node001 bin]$ ./yarn-session.sh -d -nm test
P019【019_Flink部署_YARN运行模式_会话模式的停止】04:10
3.5.3 单作业模式部署
在YARN环境中，由于有了外部平台做资源调度，所以我们也可以直接向YARN提交一个单独的作业，从而启动一个Flink集群。
P020【020_Flink部署_YARN运行模式_单作业模式】09:49
3.5.3 单作业模式部署
（1）执行命令提交作业
P021【021_Flink部署_YARN运行模式_应用模式】12:51
3.5.4 应用模式部署
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。
[atguigu@node001 flink-1.17.0]$ bin/flink run-application -t yarn-application -c com.atguigu.wc.WordCountStreamUnboundedDemo ./FlinkTutorial-1.17-1.0-SNAPSHOT.jar
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/opt/module/flink/flink-1.17.0/lib/log4j-slf4j-impl-2.17.1.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/opt/module/hadoop/hadoop-3.1.3/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]
2023-06-19 14:31:05,693 INFO org.apache.flink.yarn.cli.FlinkYarnSessionCli [] - Found Yarn properties file under /tmp/.yarn-properties-atguigu.
2023-06-19 14:31:05,693 INFO org.apache.flink.yarn.cli.FlinkYarnSessionCli [] - Found Yarn properties file under /tmp/.yarn-properties-atguigu.
2023-06-19 14:31:06,142 WARN org.apache.flink.yarn.configuration.YarnLogConfigUtil [] - The configuration directory ('/opt/module/flink/flink-1.17.0/conf') already contains a LOG4J config file.If you want to use logback, then please delete or rename the log configuration file.
2023-06-19 14:31:06,632 INFO org.apache.hadoop.yarn.client.RMProxy [] - Connecting to ResourceManager at node002/192.168.10.102:8032
2023-06-19 14:31:07,195 INFO org.apache.flink.yarn.YarnClusterDescriptor [] - No path for the flink jar passed. Using the location of class org.apache.flink.yarn.YarnClusterDescriptor to locate the jar[atguigu@node001 flink-1.17.0]$ bin/flink run-application -t yarn-application -c com.atguigu.wc.WordCountStreamUnboundedDemo ./FlinkTutorial-1.17-1.0-SNAPSHOT.jar
SLF4J: Class path contains multiple SLF4J bindings.
[atguigu@node001 flink-1.17.0]$ bin/flink run-application -t yarn-application -Dyarn.provided.lib.dirs="hdfs://node001:8020/flink-dist" -c com.atguigu.wc.WordCountStreamUnboundedDemo hdfs://node001:8020/flink-jars/FlinkTutorial-1.17-1.0-SNAPSHOT.jar
P022【022_Flink部署_历史服务器】08:11
3.6 K8S 运行模式（了解）
容器化部署是如今业界流行的一项技术，基于Docker镜像运行能够让用户更加方便地对应用进行管理和运维。容器管理工具中最为流行的就是Kubernetes（k8s），而Flink也在最近的版本中支持了k8s部署模式。基本原理与YARN是类似的，具体配置可以参见官网说明，这里我们就不做过多讲解了。
3.7 历史服务器
运行 Flink job 的集群一旦停止，只能去 yarn 或本地磁盘上查看日志，不再可以查看作业挂掉之前的运行的 Web UI，很难清楚知道作业在挂的那一刻到底发生了什么。如果我们还没有 Metrics 监控的话，那么完全就只能通过日志去分析和定位问题了，所以如果能还原之前的 Web UI，我们可以通过 UI 发现和定位一些问题。
Flink提供了历史服务器，用来在相应的 Flink 集群关闭后查询已完成作业的统计信息。我们都知道只有当作业处于运行中的状态，才能够查看到相关的WebUI统计信息。通过 History Server 我们才能查询这些已完成作业的统计信息，无论是正常退出还是异常退出。
此外，它对外提供了 REST API，它接受 HTTP 请求并使用 JSON 数据进行响应。Flink 任务停止后，JobManager 会将已经完成任务的统计信息进行存档，History Server 进程则在任务停止后可以对任务统计信息进行查询。比如：最后一次的 Checkpoint、任务运行时的相关配置。
[atguigu@node001 flink-1.17.0]$ bin/historyserver.sh start
Starting historyserver daemon on host node001.
[atguigu@node001 flink-1.17.0]$ bin/flink run -t yarn-per-job -d -c com.atguigu.wc.WordCountStreamUnboundedDemo ../jar/FlinkTutorial-1.17-1.0-SNAPSHOT.jar
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/opt/module/flink/flink-1.17.0/lib/log4j-slf4j-impl-2.17.1.jar!/org/slf4j/impl/StaticLoggerBinder.class]
