flink1.18.1配置flink on yarn模式

1.下载

cd /opt
wget http://139.196.32.140:8080/cdh6/flink-1.18.1.tar.gz

2.解压

cd /opt
tar -xvf  flink-1.18.1.tar.gz

3.创建提交用户

useradd flink

4.授权

chonwn -R flink:flink /opt/flink-1.18.1

5.配置flink conf

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#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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execution.target: yarn-per-job
#==============================================================================
# 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: localhost

# 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: localhost


# 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: localhost

# 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: localhost

# 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: 1

# The parallelism used for programs that did not specify and other parallelism.

parallelism.default: 1

# 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.type: 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: 10s
# 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.type: rocksdb

# 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
state.checkpoints.dir: hdfs:///flink/checkpoints
state.savepoints.dir: hdfs:///flink/savepoints
# Flag to enable/disable incremental checkpoints for backends that
# support incremental checkpoints (like the RocksDB state backend). 
#
state.backend.incremental: true

# 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: localhost

# 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: localhost

# 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
env.java.opts.all: "-Dfile.encoding=UTF-8"
classloader.check-leaked-classloader: false

6.创建flink的checkpoint目录

hdfs dfs -mkdir -p /flink/checkpoints
hdfs dfs -mkdir -p /flink/savepoints
hdfs dfs -mkdir -p /user/flink

7.授权

hdfs dfs -chown -R  flink:flink /flink/
hdfs dfs -chown -R  flink:flink /user/flink

8.设置环境变量

#set default jdk1.8 env
export JAVA_HOME=/usr/java/jdk1.8.0_181-cloudera
export JRE_HOME=/usr/java/jdk1.8.0_181-cloudera/jre
export CLASSPATH=.:${JAVA_HOME}/lib:${JRE_HOME}/lib
export PATH=${JAVA_HOME}/bin:${JRE_HOME}/bin:$PATH
export TMOUT=600
export TIME_STYLE="+%Y/%m/%d %H:%M:%S"
export HISTTIMEFORMAT="%F %T `whoami` "
unset MAILCHECK
export HADOOP_HOME=/opt/cloudera/parcels/CDH/lib/hadoop #hadoop 安装目录
export HADOOP_CONF_DIR=/etc/hadoop/conf
export HADOOP_CLASSPATH=`hadoop classpath`
export HIVE_HOME=$HADOOP_HOME/../hive
export HBASE_HOME=$HADOOP_HOME/../hbase
export HADOOP_HDFS_HOME=$HADOOP_HOME/../hadoop-hdfs
export HADOOP_MAPRED_HOME=$HADOOP_HOME/../hadoop-mapreduce
export HADOOP_YARN_HOME=$HADOOP_HOME/../hadoop-yarn
export FLINK_HOME=/opt/flink-1.18.1/
export PATH=$PATH:$FLINK_HOME/bin

source /etc/profile

9.复制yarn和hdfs的配置文件

cp -r /etc/hadoop/conf/yarn-site.xml /opt/flink-1.18.1/conf/
cp -r /etc/hadoop/conf/core-site.xml /opt/flink-1.18.1/conf/
cp -r /etc/hadoop/conf/hdfs-site.xml /opt/flink-1.18.1/conf/
记得以上要重新授权
hdfs dfs -chown -R  flink:flink /flink/
注意：要把里面的yarn-site.xml、core-site.xml、hdfs-site.xml替换成自己本地环境的

10.测试命令

flink run-application -t yarn-application   -Dyarn.application.name="flink test"   -Dyarn.queue.name=flink   -Dpipeline.fixed-delay=1000   /opt/flink-1.18.1/examples/batch/WordCount.jar

说明：flink1.18.1 flink on yarn的提交

提交命令参考一下此文档：https://zhuanlan.zhihu.com/p/694696005

在Flink 1.18.1中，使用Flink on YARN提交作业，你可以通过以下步骤进行：

1. 确保你的环境中已经安装了Hadoop，并且YARN服务是运行状态。
2. 确保Flink的YARN客户端（flink-yarn_2.11-1.18.1.jar）与Hadoop版本兼容。
3. 准备好Flink作业的jar包和所需的配置文件。

以下是提交Flink作业到YARN的示例命令：

./bin/flink run-application -t yarn-application \  -Dyarn.application.name="My Flink Job" \  -Dyarn.queue.name=myQueue \  -Dpipeline.fixed-delay=1000 \  -c com.example.MyFlinkJob \  /path/to/myflinkjob.jar
./bin/flink run-application -t yarn-application \  -Dyarn.application.name="My Flink Job" \  -Dyarn.queue.name=myQueue \  -Dpipeline.fixed-delay=1000 \  -c com.example.MyFlinkJob \  /path/to/myflinkjob.jar

在这个命令中，-t yarn-application 指定了提交到YARN的模式。其他参数是根据你的作业和YARN集群进行设置的。-c 后面跟着的是主类的全路径，而 .jar 文件指向你的Flink作业的jar包。

确保你的Flink配置文件（如flink-conf.yaml）中已经正确设置了YARN的相关配置，例如yarn.application.cluster-id 和 yarn.nodemanager.heartbeat-interval-ms。