01 引言
在前面的博客，我们已经大概对Flink有一个初步认识了，有兴趣的同学可以参阅下：
如果要学习Flink必须先搭建好Flink环境，本文来讲解下Flink的环境搭建。
在上一篇博客 《Flink教程（01）- Flink知识图谱》里面的物理部署层，我们知道了Flink有几种部署模式，根据本地或集群分为以下几种：
- Local（本地单机模式）：学习测试时使用
- Standalone（独立集群模式）：Flink自带集群，开发测试环境使用
- StandaloneHA（独立集群高可用模式）：Flink自带集群，开发测试环境使用
- On Yarn（计算资源统一由Hadoop YARN管理）：生产环境使用
本文来讲解下。
02 Local本地单机模式
2.1 工作原理
上图流程如下：
1、 Flink程序由JobClient进行提交；
2、 JobClient将作业提交给JobManager；
3、 JobManager负责协调资源分配和作业执行，资源分配完成后，任务将提交给相应的TaskManager；
4、 TaskManager启动一个线程以开始执行，TaskManager会向JobManager报告状态更改，如开始执行，正在进行或已完成；
5、 作业执行完成后，结果将发送回客户端（JobClient）；
2.2 安装部署
step1：下载安装包
step2：上传flink-1.12.0-bin-scala_2.12.tgz到node1的指定目录
step3：解压
tar -zxvf flink-1.12.0-bin-scala_2.12.tgz
step4：修改权限
chown -R root:root /export/server/flink-1.12.0
step5：改名或创建软链接
mv flink-1.12.0 flink
ln -s /export/server/flink-1.12.0 /export/server/flink
2.3 测试验证
1. 准备文件/root/words.txt
vim /root/words.txt
内容如下：
hello me you her
hello me you
hello me
hello
2. 启动Flink本地“集群”
/export/server/flink/bin/start-cluster.sh
3.使用jps可以查看到下面两个进程
- TaskManagerRunner
- StandaloneSessionClusterEntrypoint
4.访问Flink的Web UI： http://node1:8081/#/overview
slot在Flink里面可以认为是资源组，Flink是通过将任务分成子任务并且将这些子任务分配到slot来并行执行程序。
5. 执行官方示例:
/export/server/flink/bin/flink run
/export/server/flink/examples/batch/WordCount.jar --input
/root/words.txt --output /root/out
6. 停止Flink：
/export/server/flink/bin/stop-cluster.sh
启动shell交互式窗口(目前所有Scala 2.12版本的安装包暂时都不支持Scala Shell)
/export/server/flink/bin/start-scala-shell.sh local
执行如下命令:
benv.readTextFile("/root/words.txt").flatMap(_.split(" ")).map((_,1)).groupBy(0).sum(1).print()
退出shell:
:quit
03 Standalone独立集群模式
3.1 工作原理
工作流程：
1、 client客户端提交任务给JobManager；
2、 JobManager负责申请任务运行所需要的资源并管理任务和资源；
3、 JobManager分发任务给TaskManager执行；
4、 TaskManager定期向JobManager汇报状态；
3.2 安装部署
step1：集群规划
- 服务器: node1(Master + Slave): JobManager + TaskManager
- 服务器: node2(Slave): TaskManager
- 服务器: node3(Slave): TaskManager
step2：修改flink-conf.yaml
vim /export/server/flink/conf/flink-conf.yaml
内容如下：
jobmanager.rpc.address: node1
taskmanager.numberOfTaskSlots: 2
web.submit.enable: true
#历史服务器
jobmanager.archive.fs.dir: hdfs://node1:8020/flink/completed-jobs/
historyserver.web.address: node1
historyserver.web.port: 8082
historyserver.archive.fs.dir: hdfs://node1:8020/flink/completed-jobs/
step3：修改masters
vim /export/server/flink/conf/masters
内容如下：
node1:8081
step4：修改slaves
vim /export/server/flink/conf/workers
内容如下：
node1
node2
node3
step5：添加HADOOP_CONF_DIR环境变量
vim /etc/profile
新增内容：
export HADOOP_CONF_DIR=/export/server/hadoop/etc/hadoop
step6：分发
scp -r /export/server/flink node2:/export/server/flink
scp -r /export/server/flink node3:/export/server/flink
scp /etc/profile node2:/etc/profile
scp /etc/profile node3:/etc/profile
或
for i in {
2..3}; do scp -r flink node$i:$PWD; done
step7：source
source /etc/profile
3.3 测试验证
1. 启动集群，在node1上执行如下命令
/export/server/flink/bin/start-cluster.sh
或者单独启动
/export/server/flink/bin/jobmanager.sh ((start|start-foreground) cluster)|stop|stop-all
/export/server/flink/bin/taskmanager.sh start|start-foreground|stop|stop-all
2. 启动历史服务器
/export/server/flink/bin/historyserver.sh start
3. 访问Flink UI界面或使用jps查看
TaskManager界面：可以查看到当前Flink集群中有多少个TaskManager，每个TaskManager的slots、内存、CPUCore是多少
4. 执行官方测试案例
/export/server/flink/bin/flink run
/export/server/flink/examples/batch/WordCount.jar --input
hdfs://node1:8020/wordcount/input/words.txt --output
hdfs://node1:8020/wordcount/output/result.txt --parallelism 2
5. 查看历史日志
6. 停止Flink集群
/export/server/flink/bin/stop-cluster.sh
04 Standalone-HA高可用集群模式
4.1 工作原理
从之前的架构中我们可以很明显的发现 JobManager有明显的单点问题（SPOF，single point of failure)。JobManager 肩负着任务调度以及资源分配，一旦 JobManager出现意外，其后果可想而知。
工作原理：
- 在 Zookeeper的帮助下，一个 Standalone的Flink集群会同时有多个活着的 JobManager，其中只有一个处于工作状态，其他处于Standby状态。
- 当工作中的 JobManager 失去连接后（如宕机或Crash），Zookeeper会从 Standby中选一个新的 JobManager 来接管 Flink 集群。
4.2 安装部署
step1：集群规划
- 服务器: node1(Master + Slave): JobManager + TaskManager
- 服务器: node2(Master + Slave):JobManager + TaskManager
- 服务器:node3(Slave): TaskManager
step2：启动ZooKeeper
zkServer.sh status
zkServer.sh stop
zkServer.sh start
step3：启动HDFS
/export/serves/hadoop/sbin/start-dfs.sh
step4：停止Flink集群
/export/server/flink/bin/stop-cluster.sh
step5：修改flink-conf.yaml
vim /export/server/flink/conf/flink-conf.yaml
增加如下内容:
state.backend: filesystem
state.backend.fs.checkpointdir: hdfs://node1:8020/flink-checkpoints
high-availability: zookeeper
high-availability.storageDir: hdfs://node1:8020/flink/ha/
high-availability.zookeeper.quorum: node1:2181,node2:2181,node3:2181
配置解释:
#开启HA，使用文件系统作为快照存储
state.backend: filesystem
#启用检查点，可以将快照保存到HDFS
state.backend.fs.checkpointdir: hdfs://node1:8020/flink-checkpoints
#使用zookeeper搭建高可用
high-availability: zookeeper
# 存储JobManager的元数据到HDFS
high-availability.storageDir: hdfs://node1:8020/flink/ha/
# 配置ZK集群地址
high-availability.zookeeper.quorum: node1:2181,node2:2181,node3:2181
step6：修改masters
vim /export/server/flink/conf/masters
node1:8081
node2:8081
step7：同步
scp -r /export/server/flink/conf/flink-conf.yaml node2:/export/server/flink/conf/
scp -r /export/server/flink/conf/flink-conf.yaml node3:/export/server/flink/conf/
scp -r /export/server/flink/conf/masters node2:/export/server/flink/conf/
scp -r /export/server/flink/conf/masters node3:/export/server/flink/conf/
step8：修改node2上的flink-conf.yaml
vim /export/server/flink/conf/flink-conf.yaml
修改内容如下：
jobmanager.rpc.address: node2
step9：重新启动Flink集群,node1上执行
/export/server/flink/bin/stop-cluster.sh
/export/server/flink/bin/start-cluster.sh
step10：使用jps命令查看，发现没有Flink相关进程被启动
step11：查看日志
cat /export/server/flink/log/flink-root-standalonesession-0-node1.log
发现如下错误：
因为在Flink1.8版本后，Flink官方提供的安装包里没有整合HDFS的jar
step12：下载jar包并在Flink的lib目录下放入该jar包并分发使Flink能够支持对Hadoop的操作
- 下载地址：https://flink.apache.org/downloads.html
- 放入lib目录（cd /export/server/flink/lib）
- 分发（for i in {2..3}; do scp -r flink-shaded-hadoop-2-uber-2.7.5-10.0.jar node
i:PWD; done）
step13：重新启动Flink集群,node1上执行
/export/server/flink/bin/start-cluster.sh
step14：使用jps命令查看,发现三台机器已经ok
4.3 测试验证
1. 访问WebUI
2. 执行wc
/export/server/flink/bin/flink run
/export/server/flink/examples/batch/WordCount.jar
3. kill掉其中一个master
4.重新执行wc，还是可以正常执行
/export/server/flink/bin/flink run
/export/server/flink/examples/batch/WordCount.jar
5. 停止集群
/export/server/flink/bin/stop-cluster.sh
05 Flink On Yarn模式
5.1 使用Yarn优势
在实际开发中，使用Flink时，更多的使用方式是Flink On Yarn模式，原因如下：
原因1：Yarn的资源可以按需使用，提高集群的资源利用率
原因2：Yarn的任务有优先级，根据优先级运行作业
原因3：基于Yarn调度系统，能够自动化地处理各个角色的 Failover(容错)
- JobManager进程和TaskManager进程都由 Yarn NodeManager监控
- 如果 JobManager进程异常退出，则 Yarn ResourceManager会重新调度 JobManager到其他机器
- 如果TaskManager 进程异常退出，JobManager会收到消息并重新向Yarn ResourceManager 申请资源，重新启动 TaskManager
5.2 工作原理
工作原理如下：
1、 Client上传jar包和配置文件到HDFS集群上；
2、 Client向YarnResourceManager提交任务并申请资源；
3、 ResourceManager分配Container资源并启动AppMaster；
4、 然后AppMaster加载Flink的Jar包和配置构建环境，启动JobManager，JobManager和ApplicationMaster运行在同一个container上；
5、 一旦它们被成功启动，AppMaster就知道JobManager的地址（AppMaster它自己所在的机器），它就会为TaskManager生成一个新的Flink配置文件（他们就可以连接到JobManager)，这个配置文件也被上传到HDFS上；
6、 此外，AppMaster容器也提供了Flink的web服务接口，YARN所分配的所有端口都是临时端口，这允许用户并行执行多个Flink；
7、 ApplicationMaster向ResourceManager申请工作资源,NodeManager加载Flink的Jar包和配置构建环境并启动TaskManager；
8、 TaskManager启动后向JobManager发送心跳包，并等待JobManager向其分配任务；
5.3 两种方式
5.3.1 Session模式
特点：需要事先申请资源，启动JobManager和TaskManger
优点：不需要每次递交作业申请资源，而是使用已经申请好的资源，从而提高执行效率
缺点：作业执行完成以后，资源不会被释放，因此一直会占用系统资源
应用场景：适合作业递交比较频繁的场景，小作业比较多的场景
5.3.2 Per-Job模式
特点：每次递交作业都需要申请一次资源
优点：作业运行完成，资源会立刻被释放，不会一直占用系统资源
缺点：每次递交作业都需要申请资源，会影响执行效率，因为申请资源需要消耗时间
应用场景：适合作业比较少的场景、大作业的场景
5.4 安装部署
step1：关闭yarn的内存检查
vim /export/server/hadoop/etc/hadoop/yarn-site.xml
添加内容：
<!-- 关闭yarn内存检查 -->
<property>
<name>yarn.nodemanager.pmem-check-enabled</name>
<value>false</value>
</property>
<property>
<name>yarn.nodemanager.vmem-check-enabled</name>
<value>false</value>
</property>
说明:
- 是否启动一个线程检查每个任务正使用的虚拟内存量，如果任务超出分配值，则直接将其杀掉，默认是true。
- 在这里面我们需要关闭，因为对于flink使用yarn模式下，很容易内存超标，这个时候yarn会自动杀掉job
step2：同步
scp -r /export/server/hadoop/etc/hadoop/yarn-site.xml node2:/export/server/hadoop/etc/hadoop/yarn-site.xml
scp -r /export/server/hadoop/etc/hadoop/yarn-site.xml node3:/export/server/hadoop/etc/hadoop/yarn-site.xml
step3：重启yarn
/export/server/hadoop/sbin/stop-yarn.sh
/export/server/hadoop/sbin/start-yarn.sh
5.5 测试验证
5.5.1 Session模式
yarn-session.sh(开辟资源) +flink run(提交任务)
1. 在yarn上启动一个Flink会话，node1上执行以下命令
/export/server/flink/bin/yarn-session.sh -n 2 -tm 800 -s 1 -d
说明：申请2个CPU、1600M内存
# -n 表示申请2个容器，这里指的就是多少个taskmanager
# -tm 表示每个TaskManager的内存大小
# -s 表示每个TaskManager的slots数量
# -d 表示以后台程序方式运行
注意该警告不用管：
WARN org.apache.hadoop.hdfs.DFSClient - Caught exception
java.lang.InterruptedException
2. 查看UI界面：http://node1:8088/cluster
3.使用flink run提交任务：
/export/server/flink/bin/flink run
/export/server/flink/examples/batch/WordCount.jar
运行完之后可以继续运行其他的小任务
/export/server/flink/bin/flink run
/export/server/flink/examples/batch/WordCount.jar
4. 通过上方的ApplicationMaster可以进入Flink的管理界面：
5. 关闭yarn-session：
yarn application -kill application_1599402747874_0001
rm -rf /tmp/.yarn-properties-root
5.5.2 Per-Job分离模式
1. 直接提交job
/export/server/flink/bin/flink run -m yarn-cluster -yjm 1024 -ytm 1024
/export/server/flink/examples/batch/WordCount.jar
# -m jobmanager的地址
# -yjm 1024 指定jobmanager的内存信息
# -ytm 1024 指定taskmanager的内存信息
2. 查看UI界面：http://node1:8088/cluster
3.注意：
在之前版本中如果使用的是
flink on yarn方式，想切换回standalone模式的话，如果报错需要删除：【/tmp/.yarn-properties-root】即：rm -rf /tmp/.yarn-properties-root
因为默认查找当前yarn集群中已有的yarn-session信息中的jobmanager
06 参数总结
[root@node1 bin]# /export/server/flink/bin/flink --help
./flink <ACTION> [OPTIONS] [ARGUMENTS]
The following actions are available:
Action "run" compiles and runs a program.
Syntax: run [OPTIONS] <jar-file> <arguments>
"run" action options:
-c,--class <classname> Class with the program entry point
("main()" method). Only needed if the
JAR file does not specify the class in
its manifest.
-C,--classpath <url> Adds a URL to each user code
classloader on all nodes in the
cluster. The paths must specify a
protocol (e.g. file://) and be
accessible on all nodes (e.g. by means
of a NFS share). You can use this
option multiple times for specifying
more than one URL. The protocol must
be supported by the {
@link
java.net.URLClassLoader}.
-d,--detached If present, runs the job in detached
mode
-n,--allowNonRestoredState Allow to skip savepoint state that
cannot be restored. You need to allow
this if you removed an operator from
your program that was part of the
program when the savepoint was
triggered.
-p,--parallelism <parallelism> The parallelism with which to run the
program. Optional flag to override the
default value specified in the
configuration.
-py,--python <pythonFile> Python script with the program entry
point. The dependent resources can be
configured with the --pyFiles
option.
-pyarch,--pyArchives <arg> Add python archive files for job. The
archive files will be extracted to the
working directory of python UDF
worker. Currently only zip-format is
supported. For each archive file, a
target directory be specified. If the
target directory name is specified,
the archive file will be extracted to
a name can directory with the
specified name. Otherwise, the archive
file will be extracted to a directory
with the same name of the archive
file. The files uploaded via this
option are accessible via relative
path. '#' could be used as the
separator of the archive file path and
the target directory name. Comma (',')
could be used as the separator to
specify multiple archive files. This
option can be used to upload the
virtual environment, the data files
used in Python UDF (e.g.: --pyArchives
file:///tmp/py37.zip,file:///tmp/data.
zip#data --pyExecutable
py37.zip/py37/bin/python). The data
files could be accessed in Python UDF,
e.g.: f = open('data/data.txt', 'r').
-pyexec,--pyExecutable <arg> Specify the path of the python
interpreter used to execute the python
UDF worker (e.g.: --pyExecutable
/usr/local/bin/python3). The python
UDF worker depends on Python 3.5+,
Apache Beam (version == 2.23.0), Pip
(version >= 7.1.0) and SetupTools
(version >= 37.0.0). Please ensure
that the specified environment meets
the above requirements.
-pyfs,--pyFiles <pythonFiles> Attach custom python files for job.
These files will be added to the
PYTHONPATH of both the local client
and the remote python UDF worker. The
standard python resource file suffixes
such as .py/.egg/.zip or directory are
all supported. Comma (',') could be
used as the separator to specify
multiple files (e.g.: --pyFiles
file:///tmp/myresource.zip,hdfs:///$na
menode_address/myresource2.zip).
-pym,--pyModule <pythonModule> Python module with the program entry
point. This option must be used in
conjunction with --pyFiles.
-pyreq,--pyRequirements <arg> Specify a requirements.txt file which
defines the third-party dependencies.
These dependencies will be installed
and added to the PYTHONPATH of the
python UDF worker. A directory which
contains the installation packages of
these dependencies could be specified
optionally. Use '#' as the separator
if the optional parameter exists
(e.g.: --pyRequirements
file:///tmp/requirements.txt#file:///t
mp/cached_dir).
-s,--fromSavepoint <savepointPath> Path to a savepoint to restore the job
from (for example
hdfs:///flink/savepoint-1537).
-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.
Options for Generic CLI mode:
-D <property=value> Allows specifying multiple generic configuration
options. The available options can be found at
https://ci.apache.org/projects/flink/flink-docs-stabl
e/ops/config.html
-e,--executor <arg> DEPRECATED: Please use the -t option instead which is
also available with the "Application Mode".
The name of the executor to be used for executing the
given job, which is equivalent to the
"execution.target" config option. The currently
available executors are: "remote", "local",
"kubernetes-session", "yarn-per-job", "yarn-session".
-t,--target <arg> The deployment target for the given application,
which is equivalent to the "execution.target" config
option. For the "run" action the currently available
targets are: "remote", "local", "kubernetes-session",
"yarn-per-job", "yarn-session". For the
"run-application" action the currently available
targets are: "kubernetes-application",
"yarn-application".
Options for yarn-cluster mode:
-d,--detached If present, runs the job in detached
mode
-m,--jobmanager <arg> Set to yarn-cluster to use YARN
execution mode.
-yat,--yarnapplicationType <arg> Set a custom application type for the
application on YARN
-yD <property=value> use value for given property
-yd,--yarndetached If present, runs the job in detached
mode (deprecated; use non-YARN
specific option instead)
-yh,--yarnhelp Help for the Yarn session CLI.
-yid,--yarnapplicationId <arg> Attach to running YARN session
-yj,--yarnjar <arg> Path to Flink jar file
-yjm,--yarnjobManagerMemory <arg> Memory for JobManager Container with
optional unit (default: MB)
-ynl,--yarnnodeLabel <arg> Specify YARN node label for the YARN
application
-ynm,--yarnname <arg> Set a custom name for the application
on YARN
-yq,--yarnquery Display available YARN resources
(memory, cores)
-yqu,--yarnqueue <arg> Specify YARN queue.
-ys,--yarnslots <arg> Number of slots per TaskManager
-yt,--yarnship <arg> Ship files in the specified directory
(t for transfer)
-ytm,--yarntaskManagerMemory <arg> Memory per TaskManager Container with
optional unit (default: MB)
-yz,--yarnzookeeperNamespace <arg> Namespace to create the Zookeeper
sub-paths for high availability mode
-z,--zookeeperNamespace <arg> Namespace to create the Zookeeper
sub-paths for high availability mode
Options for default mode:
-D <property=value> Allows specifying multiple generic
configuration options. The available
options can be found at
https://ci.apache.org/projects/flink/flink-
docs-stable/ops/config.html
-m,--jobmanager <arg> Address of the JobManager to which to
connect. Use this flag to connect to a
different JobManager than the one specified
in the configuration. Attention: This
option is respected only if the
high-availability configuration is NONE.
-z,--zookeeperNamespace <arg> Namespace to create the Zookeeper sub-paths
for high availability mode
Action "run-application" runs an application in Application Mode.
Syntax: run-application [OPTIONS] <jar-file> <arguments>
Options for Generic CLI mode:
-D <property=value> Allows specifying multiple generic configuration
options. The available options can be found at
https://ci.apache.org/projects/flink/flink-docs-stabl
e/ops/config.html
-e,--executor <arg> DEPRECATED: Please use the -t option instead which is
also available with the "Application Mode".
The name of the executor to be used for executing the
given job, which is equivalent to the
"execution.target" config option. The currently
available executors are: "remote", "local",
"kubernetes-session", "yarn-per-job", "yarn-session".
-t,--target <arg> The deployment target for the given application,
which is equivalent to the "execution.target" config
option. For the "run" action the currently available
targets are: "remote", "local", "kubernetes-session",
"yarn-per-job", "yarn-session". For the
"run-application" action the currently available
targets are: "kubernetes-application",
"yarn-application".
Action "info" shows the optimized execution plan of the program (JSON).
Syntax: info [OPTIONS] <jar-file> <arguments>
"info" action options:
-c,--class <classname> Class with the program entry point
("main()" method). Only needed if the JAR
file does not specify the class in its
manifest.
-p,--parallelism <parallelism> The parallelism with which to run the
program. Optional flag to override the
default value specified in the
configuration.
Action "list" lists running and scheduled programs.
Syntax: list [OPTIONS]
"list" action options:
-a,--all Show all programs and their JobIDs
-r,--running Show only running programs and their JobIDs
-s,--scheduled Show only scheduled programs and their JobIDs
Options for Generic CLI mode:
-D <property=value> Allows specifying multiple generic configuration
options. The available options can be found at
https://ci.apache.org/projects/flink/flink-docs-stabl
e/ops/config.html
-e,--executor <arg> DEPRECATED: Please use the -t option instead which is
also available with the "Application Mode".
The name of the executor to be used for executing the
given job, which is equivalent to the
"execution.target" config option. The currently
available executors are: "remote", "local",
"kubernetes-session", "yarn-per-job", "yarn-session".
-t,--target <arg> The deployment target for the given application,
which is equivalent to the "execution.target" config
option. For the "run" action the currently available
targets are: "remote", "local", "kubernetes-session",
"yarn-per-job", "yarn-session". For the
"run-application" action the currently available
targets are: "kubernetes-application",
"yarn-application".
Options for yarn-cluster mode:
-m,--jobmanager <arg> Set to yarn-cluster to use YARN execution
mode.
-yid,--yarnapplicationId <arg> Attach to running YARN session
-z,--zookeeperNamespace <arg> Namespace to create the Zookeeper
sub-paths for high availability mode
Options for default mode:
-D <property=value> Allows specifying multiple generic
configuration options. The available
options can be found at
https://ci.apache.org/projects/flink/flink-
docs-stable/ops/config.html
-m,--jobmanager <arg> Address of the JobManager to which to
connect. Use this flag to connect to a
different JobManager than the one specified
in the configuration. Attention: This
option is respected only if the
high-availability configuration is NONE.
-z,--zookeeperNamespace <arg> Namespace to create the Zookeeper sub-paths
for high availability mode
Action "stop" stops a running program with a savepoint (streaming jobs only).
Syntax: stop [OPTIONS] <Job ID>
"stop" action options:
-d,--drain Send MAX_WATERMARK before taking the
savepoint and stopping the pipelne.
-p,--savepointPath <savepointPath> Path to the savepoint (for example
hdfs:///flink/savepoint-1537). If no
directory is specified, the configured
default will be used
("state.savepoints.dir").
Options for Generic CLI mode:
-D <property=value> Allows specifying multiple generic configuration
options. The available options can be found at
https://ci.apache.org/projects/flink/flink-docs-stabl
e/ops/config.html
-e,--executor <arg> DEPRECATED: Please use the -t option instead which is
also available with the "Application Mode".
The name of the executor to be used for executing the
given job, which is equivalent to the
"execution.target" config option. The currently
available executors are: "remote", "local",
"kubernetes-session", "yarn-per-job", "yarn-session".
-t,--target <arg> The deployment target for the given application,
which is equivalent to the "execution.target" config
option. For the "run" action the currently available
targets are: "remote", "local", "kubernetes-session",
"yarn-per-job", "yarn-session". For the
"run-application" action the currently available
targets are: "kubernetes-application",
"yarn-application".
Options for yarn-cluster mode:
-m,--jobmanager <arg> Set to yarn-cluster to use YARN execution
mode.
-yid,--yarnapplicationId <arg> Attach to running YARN session
-z,--zookeeperNamespace <arg> Namespace to create the Zookeeper
sub-paths for high availability mode
Options for default mode:
-D <property=value> Allows specifying multiple generic
configuration options. The available
options can be found at
https://ci.apache.org/projects/flink/flink-
docs-stable/ops/config.html
-m,--jobmanager <arg> Address of the JobManager to which to
connect. Use this flag to connect to a
different JobManager than the one specified
in the configuration. Attention: This
option is respected only if the
high-availability configuration is NONE.
-z,--zookeeperNamespace <arg> Namespace to create the Zookeeper sub-paths
for high availability mode
Action "cancel" cancels a running program.
Syntax: cancel [OPTIONS] <Job ID>
"cancel" action options:
-s,--withSavepoint <targetDirectory> **DEPRECATION WARNING**: Cancelling
a job with savepoint is deprecated.
Use "stop" instead.
Trigger savepoint and cancel job.
The target directory is optional. If
no directory is specified, the
configured default directory
(state.savepoints.dir) is used.
Options for Generic CLI mode:
-D <property=value> Allows specifying multiple generic configuration
options. The available options can be found at
https://ci.apache.org/projects/flink/flink-docs-stabl
e/ops/config.html
-e,--executor <arg> DEPRECATED: Please use the -t option instead which is
also available with the "Application Mode".
The name of the executor to be used for executing the
given job, which is equivalent to the
"execution.target" config option. The currently
available executors are: "remote", "local",
"kubernetes-session", "yarn-per-job", "yarn-session".
-t,--target <arg> The deployment target for the given application,
which is equivalent to the "execution.target" config
option. For the "run" action the currently available
targets are: "remote", "local", "kubernetes-session",
"yarn-per-job", "yarn-session". For the
"run-application" action the currently available
targets are: "kubernetes-application",
"yarn-application".
Options for yarn-cluster mode:
-m,--jobmanager <arg> Set to yarn-cluster to use YARN execution
mode.
-yid,--yarnapplicationId <arg> Attach to running YARN session
-z,--zookeeperNamespace <arg> Namespace to create the Zookeeper
sub-paths for high availability mode
Options for default mode:
-D <property=value> Allows specifying multiple generic
configuration options. The available
options can be found at
https://ci.apache.org/projects/flink/flink-
docs-stable/ops/config.html
-m,--jobmanager <arg> Address of the JobManager to which to
connect. Use this flag to connect to a
different JobManager than the one specified
in the configuration. Attention: This
option is respected only if the
high-availability configuration is NONE.
-z,--zookeeperNamespace <arg> Namespace to create the Zookeeper sub-paths
for high availability mode
Action "savepoint" triggers savepoints for a running job or disposes existing ones.
Syntax: savepoint [OPTIONS] <Job ID> [<target directory>]
"savepoint" action options:
-d,--dispose <arg> Path of savepoint to dispose.
-j,--jarfile <jarfile> Flink program JAR file.
Options for Generic CLI mode:
-D <property=value> Allows specifying multiple generic configuration
options. The available options can be found at
https://ci.apache.org/projects/flink/flink-docs-stabl
e/ops/config.html
-e,--executor <arg> DEPRECATED: Please use the -t option instead which is
also available with the "Application Mode".
The name of the executor to be used for executing the
given job, which is equivalent to the
"execution.target" config option. The currently
available executors are: "remote", "local",
"kubernetes-session", "yarn-per-job", "yarn-session".
-t,--target <arg> The deployment target for the given application,
which is equivalent to the "execution.target" config
option. For the "run" action the currently available
targets are: "remote", "local", "kubernetes-session",
"yarn-per-job", "yarn-session". For the
"run-application" action the currently available
targets are: "kubernetes-application",
"yarn-application".
Options for yarn-cluster mode:
-m,--jobmanager <arg> Set to yarn-cluster to use YARN execution
mode.
-yid,--yarnapplicationId <arg> Attach to running YARN session
-z,--zookeeperNamespace <arg> Namespace to create the Zookeeper
sub-paths for high availability mode
Options for default mode:
-D <property=value> Allows specifying multiple generic
configuration options. The available
options can be found at
https://ci.apache.org/projects/flink/flink-
docs-stable/ops/config.html
-m,--jobmanager <arg> Address of the JobManager to which to
connect. Use this flag to connect to a
different JobManager than the one specified
in the configuration. Attention: This
option is respected only if the
high-availability configuration is NONE.
-z,--zookeeperNamespace <arg> Namespace to create the Zookeeper sub-paths
for high availability mode
07 文末
本文主要讲解了Flink的本地和集群的安装部署方式，谢谢各位的阅读，本文完！