Flink高手之路2-Flink集群的搭建

文章目录

Flink高手之路2-Flink集群的搭建

一、Flink的安装模式

1.本地local模式

本地单机模式，一般用于测试环境是否搭建成功，很少使用

2.独立集群模式standalone

Flink自带集群，开发测试使用

3.高可用的独立集群模式standalone HA

Flink自带集群，用于开发测试

4.基于yarn模式Flink on yarn

计算资源统一交给hadoop的yarn进行管理，用于生产环境

二、基础环境

• 虚拟机
• jdk1.8
• ssh免密登录

三、Flink的local模式安装

1. 下载安装包

点击：

点击下载：

2. 上传服务器

找到安装包，并上传：

上传成功：

3.解压

tar xzvf flink-1.16.1-bin-scala_2.12.tgz -C /export/servers/

进入 Servers 目录下：

进入 Flink 目录下：

进入 bin 目录下：

4. 配置环境变量

5. 使环境变量起作用

6.测试显示版本

7.测试scala shell交互命令行（可跳过）

需要flink的版本是1.12及以下的版本，在高版本中 scala shell 被舍去了。

1)安装一下 Flink 1.12 版本

上传文件

上传成功：

解压

2)启动命令行

启动 shell

bin/start-scala-shell.sh local

3)web ui查看

4)scala命令行示例-单词计数（批处理）

• 准备好数据文件

benv.readTextFile("/root/a.txt").flatMap(_.split(" ")).map((_,1)).groupBy(0).sum(1).print()

5)scala命令行示例2-窗口计数（流处理）

6)退出命令行

输入 :quit 或者 Ctrl + d

8.local模式测试

启动集群并查看进程

9.查看Flink的web ui

启动失败，需要修改/etc/hosts文件，添加localhost的定义

若直接添加

192.168.92.128 localhost

在启动 Hbase时会出现如下错误

修改完成后，启动成功：

10.local集群运行测试任务-单词计数

1)先准备好数据文件

2)找到单词计数的jar包

3)提交任务到集群上运行

出现错误：org.apache.flink.client.program.ProgramInvocationException: The main method caused an error: java.util.concurrent.ExecutionException: org.apache.flink.runtime.client.JobSubmissionException: Failed to submit JobGraph.

原因：没有启动Flink集群

启动集群：

运行成功：

执行成功后，在/root目录下出现 output 目录

运行结果

4)web ui任务执行过程查看

点击任务

11.Flink本地（local）模式任务执行的原理

Flink程序提交任务到 JobClient ，JobClient 提交任务到 JobManager【Master】，JobManager 分发任务给TaskManager，TaskManager执行任务，执行任务后发送状态给 JobManager，JobManager 将结果返回到 JobClient 。

四、Flink的独立集群Standalone模式的安装及测试

1.集群规划

服务器JobManagerTaskManagerhadoop001✅✅hadoop002❎✅hadoop003❎✅

2.下载安装包并上传服务器解压

同上

3.配置环境变量并使环境变量起作用

同上

4.修改Flink的配置文件

1)修改yaml或者yml文件的注意事项

• 不同的等级用冒号隔开，同时缩进格式
• 次等级的前面是空格，不能使用制表符
• 冒号之后如果有值，那么冒号与值之间用至少一个空格分隔，不能紧贴在一起

2)修改flink-conf.yaml

• flink1.16版本的配置

#################################################################################  Licensed to the Apache Software Foundation (ASF) under one#  or more contributor license agreements.  See the NOTICE file#  distributed with this work for additional information#  regarding copyright ownership.  The ASF licenses this file#  to you under the Apache License, Version 2.0 (the#  "License"); you may not use this file except in compliance#  with the License.  You may obtain a copy of the License at##      http://www.apache.org/licenses/LICENSE-2.0##  Unless required by applicable law or agreed to in writing, software#  distributed under the License is distributed on an "AS IS" BASIS,#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.#  See the License for the specific language governing permissions and# limitations under the License.#################################################################################==============================================================================# Common#==============================================================================# The external address of the host on which the JobManager runs and can be# reached by the TaskManagers and any clients which want to connect. This setting# is only used in Standalone mode and may be overwritten on the JobManager side# by specifying the --host <hostname> parameter of the bin/jobmanager.sh executable.# In high availability mode, if you use the bin/start-cluster.sh script and setup# the conf/masters file, this will be taken care of automatically. Yarn# automatically configure the host name based on the hostname of the node where the# JobManager runs.jobmanager.rpc.address: hadoop001

# The RPC port where the JobManager is reachable.jobmanager.rpc.port:6123# The host interface the JobManager will bind to. By default, this is localhost, and will prevent# the JobManager from communicating outside the machine/container it is running on.# On YARN this setting will be ignored if it is set to 'localhost', defaulting to 0.0.0.0.# On Kubernetes this setting will be ignored, defaulting to 0.0.0.0.## To enable this, set the bind-host address to one that has access to an outside facing network# interface, such as 0.0.0.0.jobmanager.bind-host: 0.0.0.0

# The total process memory size for the JobManager.## Note this accounts for all memory usage within the JobManager process, including JVM metaspace and other overhead.jobmanager.memory.process.size: 1600m

# The host interface the TaskManager will bind to. By default, this is localhost, and will prevent# the TaskManager from communicating outside the machine/container it is running on.# On YARN this setting will be ignored if it is set to 'localhost', defaulting to 0.0.0.0.# On Kubernetes this setting will be ignored, defaulting to 0.0.0.0.## To enable this, set the bind-host address to one that has access to an outside facing network# interface, such as 0.0.0.0.taskmanager.bind-host: 0.0.0.0

# The address of the host on which the TaskManager runs and can be reached by the JobManager and# other TaskManagers. If not specified, the TaskManager will try different strategies to identify# the address.## Note this address needs to be reachable by the JobManager and forward traffic to one of# the interfaces the TaskManager is bound to (see 'taskmanager.bind-host').## Note also that unless all TaskManagers are running on the same machine, this address needs to be# configured separately for each TaskManager.taskmanager.host: hadoop001

# The total process memory size for the TaskManager.## Note this accounts for all memory usage within the TaskManager process, including JVM metaspace and other overhead.taskmanager.memory.process.size: 1728m

# To exclude JVM metaspace and overhead, please, use total Flink memory size instead of 'taskmanager.memory.process.size'.# It is not recommended to set both 'taskmanager.memory.process.size' and Flink memory.## taskmanager.memory.flink.size: 1280m# The number of task slots that each TaskManager offers. Each slot runs one parallel pipeline.taskmanager.numberOfTaskSlots:2# The parallelism used for programs that did not specify and other parallelism.parallelism.default:2# The default file system scheme and authority.# # By default file paths without scheme are interpreted relative to the local# root file system 'file:///'. Use this to override the default and interpret# relative paths relative to a different file system,# for example 'hdfs://mynamenode:12345'## fs.default-scheme#==============================================================================# High Availability#==============================================================================# The high-availability mode. Possible options are 'NONE' or 'zookeeper'.## high-availability: zookeeper# The path where metadata for master recovery is persisted. While ZooKeeper stores# the small ground truth for checkpoint and leader election, this location stores# the larger objects, like persisted dataflow graphs.# # Must be a durable file system that is accessible from all nodes# (like HDFS, S3, Ceph, nfs, ...) ## high-availability.storageDir: hdfs:///flink/ha/# The list of ZooKeeper quorum peers that coordinate the high-availability# setup. This must be a list of the form:# "host1:clientPort,host2:clientPort,..." (default clientPort: 2181)## high-availability.zookeeper.quorum: localhost:2181# ACL options are based on https://zookeeper.apache.org/doc/r3.1.2/zookeeperProgrammers.html#sc_BuiltinACLSchemes# It can be either "creator" (ZOO_CREATE_ALL_ACL) or "open" (ZOO_OPEN_ACL_UNSAFE)# The default value is "open" and it can be changed to "creator" if ZK security is enabled## high-availability.zookeeper.client.acl: open#==============================================================================# Fault tolerance and checkpointing#==============================================================================# The backend that will be used to store operator state checkpoints if# checkpointing is enabled. Checkpointing is enabled when execution.checkpointing.interval > 0.## Execution checkpointing related parameters. Please refer to CheckpointConfig and ExecutionCheckpointingOptions for more details.## execution.checkpointing.interval: 3min# execution.checkpointing.externalized-checkpoint-retention: [DELETE_ON_CANCELLATION, RETAIN_ON_CANCELLATION]# execution.checkpointing.max-concurrent-checkpoints: 1# execution.checkpointing.min-pause: 0# execution.checkpointing.mode: [EXACTLY_ONCE, AT_LEAST_ONCE]# execution.checkpointing.timeout: 10min# execution.checkpointing.tolerable-failed-checkpoints: 0# execution.checkpointing.unaligned: false## Supported backends are 'hashmap', 'rocksdb', or the# <class-name-of-factory>.## state.backend: hashmap# Directory for checkpoints filesystem, when using any of the default bundled# state backends.## state.checkpoints.dir: hdfs://namenode-host:port/flink-checkpoints# Default target directory for savepoints, optional.## state.savepoints.dir: hdfs://namenode-host:port/flink-savepoints# Flag to enable/disable incremental checkpoints for backends that# support incremental checkpoints (like the RocksDB state backend). ## state.backend.incremental: false# The failover strategy, i.e., how the job computation recovers from task failures.# Only restart tasks that may have been affected by the task failure, which typically includes# downstream tasks and potentially upstream tasks if their produced data is no longer available for consumption.jobmanager.execution.failover-strategy: region

#==============================================================================# Rest & web frontend#==============================================================================# The port to which the REST client connects to. If rest.bind-port has# not been specified, then the server will bind to this port as well.#rest.port:8081# The address to which the REST client will connect to#rest.address: hadoop001

# Port range for the REST and web server to bind to.##rest.bind-port: 8080-8090# The address that the REST & web server binds to# By default, this is localhost, which prevents the REST & web server from# being able to communicate outside of the machine/container it is running on.## To enable this, set the bind address to one that has access to outside-facing# network interface, such as 0.0.0.0.#rest.bind-address: 0.0.0.0

# Flag to specify whether job submission is enabled from the web-based# runtime monitor. Uncomment to disable.#web.submit.enable: false# Flag to specify whether job cancellation is enabled from the web-based# runtime monitor. Uncomment to disable.#web.cancel.enable: false#==============================================================================# Advanced#==============================================================================# Override the directories for temporary files. If not specified, the# system-specific Java temporary directory (java.io.tmpdir property) is taken.## For framework setups on Yarn, Flink will automatically pick up the# containers' temp directories without any need for configuration.## Add a delimited list for multiple directories, using the system directory# delimiter (colon ':' on unix) or a comma, e.g.:#     /data1/tmp:/data2/tmp:/data3/tmp## Note: Each directory entry is read from and written to by a different I/O# thread. You can include the same directory multiple times in order to create# multiple I/O threads against that directory. This is for example relevant for# high-throughput RAIDs.## io.tmp.dirs: /tmp# The classloading resolve order. Possible values are 'child-first' (Flink's default)# and 'parent-first' (Java's default).## Child first classloading allows users to use different dependency/library# versions in their application than those in the classpath. Switching back# to 'parent-first' may help with debugging dependency issues.## classloader.resolve-order: child-first# The amount of memory going to the network stack. These numbers usually need # no tuning. Adjusting them may be necessary in case of an "Insufficient number# of network buffers" error. The default min is 64MB, the default max is 1GB.# # taskmanager.memory.network.fraction: 0.1# taskmanager.memory.network.min: 64mb# taskmanager.memory.network.max: 1gb#==============================================================================# Flink Cluster Security Configuration#==============================================================================# Kerberos authentication for various components - Hadoop, ZooKeeper, and connectors -# may be enabled in four steps:# 1. configure the local krb5.conf file# 2. provide Kerberos credentials (either a keytab or a ticket cache w/ kinit)# 3. make the credentials available to various JAAS login contexts# 4. configure the connector to use JAAS/SASL# The below configure how Kerberos credentials are provided. A keytab will be used instead of# a ticket cache if the keytab path and principal are set.# security.kerberos.login.use-ticket-cache: true# security.kerberos.login.keytab: /path/to/kerberos/keytab# security.kerberos.login.principal: flink-user# The configuration below defines which JAAS login contexts# security.kerberos.login.contexts: Client,KafkaClient#==============================================================================# ZK Security Configuration#==============================================================================# Below configurations are applicable if ZK ensemble is configured for security# Override below configuration to provide custom ZK service name if configured# zookeeper.sasl.service-name: zookeeper# The configuration below must match one of the values set in "security.kerberos.login.contexts"# zookeeper.sasl.login-context-name: Client#==============================================================================# HistoryServer#==============================================================================# The HistoryServer is started and stopped via bin/historyserver.sh (start|stop)# Directory to upload completed jobs to. Add this directory to the list of# monitored directories of the HistoryServer as well (see below).#jobmanager.archive.fs.dir: hdfs:///completed-jobs/# The address under which the web-based HistoryServer listens.#historyserver.web.address: 0.0.0.0# The port under which the web-based HistoryServer listens.#historyserver.web.port: 8082# Comma separated list of directories to monitor for completed jobs.#historyserver.archive.fs.dir: hdfs:///completed-jobs/# Interval in milliseconds for refreshing the monitored directories.#historyserver.archive.fs.refresh-interval: 10000

• Flink1.12版本的配置

3)master

4)workers

5.分发文件

1)分发flink

2)分发/etc/profile

3)使得配置文件起作用

6.启动Flink集群，并查看相关进程

7.web ui查看

8.集群测试

1)提交单词计数的任务，使用默认的参数

2)提交单词计数的任务，使用自定义参数

准备好数据文件

上传hdfs

首先要确保 hdfs 集群已经启动

发现我们以前已经上传过了

提交命令

flink run ./WordCount.jar --input hdfs://hadoop001:9000/input --output hdfs://hadoop001:9000/output

出现错误：

org.apache.flink.core.fs.UnsupportedFileSystemSchemeException:Hadoop is not in the classpath/dependencies.

这个错误需要把flink-1.16.1与hadoop3进行集成。

3）添加hadoop classpath配置

exportHADOOP_CLASSPATH=`hadoop classpath`

4）分发并激活环境变量

5）下载flink和hadoop的连接工具，上传到flink的lib文件夹

去maven中央仓库下载如下jar包并上传到 flink/lib文件夹中

https://mvnrepository.com/artifact/commons-cli/commons-cli/1.5.0

https://mvnrepository.com/artifact/org.apache.flink/flink-shaded-hadoop-3-uber

这是为了集成hadoop，而shaded依赖已经解决了相关的jar包冲突等问题，该jar包属于第三方jar包，官网有链接，但是并没有hadoop 3.X的，这个直接在maven中央仓库搜索倒是可以搜得到。

上传 jar 包到lib目录下

分发 lib 目录到hadoop002和hadoop003

6）重新启动flink集群

7）重新提交单词计数的任务，使用自定义参数

查看 flink web ui

查看 hdfs web UI

点击一个文件查看

9.工作原理

五、独立集群高可用Standalone-HA搭建

1.集群规划

服务器JobManagerTaskManagerhadoop001yyhadoop002yyhadoop003ny

2.修改flink的配置文件

1)修改flink-conf.yaml文件

2)修改masters文件

3)不用修改workers文件

3.同步配置文件

分发到Hadoop002：

分发到Hadoop003：

4.修改hadoop002上的flink-conf.yaml文件

注意：12.7版本下只需要修改一处就可以了，16.1需要修改3处，否则会提交任务失败。

5.启动集群

1)启动zookeeper

启动ZooKeeper，查看ZooKeeper的状态：

2)启动hdfs

3)启动yarn

4)启动flink集群

6.flink的web ui查看

7.集群的测试

1)单词计数使用默认的参数

2)杀掉hadoop001的master进程

此时查看web ui，hadoop001无法访问，hadoop002还可以继续访问

3)再次提交单词计数的任务（使用默认参数）

集群能正常工作，说明高可用在起作用

4)接着杀掉hadoop002的master

此时，node2的web ui也无法访问

再次提交任务，出现错误，无法运行任务

5)单词计数，使用自定义参数

重启集群

删除hdfs上以前创建的output文件夹

提交任务，使用之前上传的数据

flink run examples/batch/WordCount.jar --input hdfs://hadoop001:9000/input --output hdfs://hadoop001:9000/output

查看结果

杀掉hadoop001的master进程，并再次提交任务

再次删除hdfs上之前创建的output文件夹

再次提交任务，可以正常运行并查看结果，说明高可用搭建成功

8.工作原理

六、Flink on Yarn模式集群搭建及测试

1.为什么要使用Flink on Yarn

• yarn管理资源，可以按需使用，提高整个集群的资源利用率
• 任务有优先级，可以根据优先级合理的安排任务运行作用
• 基于yarn的调度系统，能够自动化的处理各个角色的容错

2.集群规划

跟standalone保持一致
服务器JobManagerTaskManagerhadoop001yyhadoop002yyhadoop003ny

3.修改yarn的配置

4.启动相关的服务

• zookeeper
• hdfs
• yarn
• flink
• historyserver（可选）

启动历史服务器

5.flink on yarn提交任务的模式

有两种模式

• session模式 ：会话模式
• per-job模式：每任务模式

6.Session模式提交任务

1)开启会话（session）

语法：

yarn-session.sh -n 2-tm 800-s 1-d

说明：

• n:表示申请容器的数量，也就是worker的数量，也就是cpu的核心数
• tm：表示给个worker（TaskManager）的内存大小
• s:表示每个worker的slot的数量
• d:表示后台运行

启动一个会话

yarn-session.sh -n 2 -tm 800 -s 1 -d

此时的进程

web ui的查看

2)提交任务-单词计数

使用的默认的参数，提交任务

查看yarn的web ui

3)再次提交任务

再次查看yarn的web ui

7.关闭yarn-session

关闭会话

查看进程

查看yarn的web ui

8.Per-Job模式提交任务

1)语法

flink run -m yarn-cluster -yjm 1024 -ytm 1024 examples/batch/WordCount.jar 

说明：

• m：jobmanager的地址
• yjm：jobmanager的内存大小
• ytm：taskmanager的内存大小

2)提交任务

3)查看yarn的web ui

执行过程中出现错误

解决错误，可以修改flink的配置

分发配置文件，并重启flink

4)再次提交任务

5)查看jps，并没有相关的进程，也就是当任务执行完成后，进程自动关闭

9.flink任务提交参数总结

参考文章：

flink启动后web访问问题

Flink高手之路：Flink的环境搭建

org.apache.flink.core.fs.UnsupportedFileSystemSchemeException:Hadoop is not in the classpath/dependencies

flink 1.15.2集群搭建（Flink Standalone模式）