Flink On Yarn模式配置

Flink On Yarn模式配置
- 引言
- 一、安装JDK
- 二、安装Hadoop
- 三、安装Zookeeper
- 四、安装Flink

Flink On Yarn模式配置

引言

Flink依靠Yarn来实现高可用，由于Yarn依赖于Hadoop，而Hadoop又依赖于Jdk。

准备三台机器

1.1.1.1 node1

1.1.1.2 node2

1.1.1.3 node3

一、安装JDK

1. 下载解压
	tar -xvf jdk-8u271-linux-x64.tar.gz -C /usr/local
	mv jdk_1.8.271 jdk
2. 配置环境变量
export JAVA_HOME=/usr/local/jdk
export PATH=$PATH:$JAVA_HOME/bin
export CLASSPATH=.:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jar

3. 验证 
java -version

二、安装Hadoop

1. 配置hosts，做主机名到ip地址映射,每台机器都要更改
	vi /etc/hosts
	添加如下内容
		1.1.1.1	node1

		1.1.1.2	node2

		1.1.1.3	node3
	
2. 配置ssh免密登录
	ssh-keygen
	ssh-copy-id node2
	ssh-copy-id node3
3. 解压hadoop安装包
	tar -xvf hadoop-2.10.1.tar.gz -C /usr/local
	mv hadoop-2.10.1 hadoop
	
4. 配置环境变量
export HADOOP_HOME=/usr/local/hadoop
export PATH=$PATH:$JAVA_HOME/bin:$HADOOP_HOME/bin

5. 配置HDFS集群
	1. hadoop-env.sh
		添加jdk路径
		export JAVA_HOME=/usr/local/jdk

	2. core-site.xml

        
                hadoop.tmp.dir
                file:/usr/local/hadoop/data/hdfs/tmp
                A base for other temporary directories.
        
        
    	
                io.file.buffer.size
                131072
        
        
                fs.defaultFS
                hdfs://ns
        
    
    	
                hadoop.proxyuser.root.hosts
                *
        
        
                hadoop.proxyuser.root.groups
                *
        
        
                dfs.journalnode.edits.dir
                /usr/local/hadoop/data/hdfs/journal
        
        
        
                ha.zookeeper
                node1:2181,node2:2181,node3:2181
        


	3. hdfs-site.xml

	
        
		dfs.replication
		2
	
    
        
		dfs.block.size
		134217728
	
	
        
		dfs.namenode.name.dir
		file:///usr/local/hadoop/data/hdfs/namenode
		
	
        
		dfs.datanode.data.dir
		file:///usr/local/hadoop/data/hdfs/datanode
	
    
        
		dfs.namenode.edits.dir
		file:///usr/local/hadoop/data/hdfs/nn/edits
	
    
    
		dfs.nameservices
		ns
	
    
    
		dfs.ha.namenodes.ns
		nn1,nn2
	
    
		dfs.namenode.rpc-address.ns.nn1
		node1:9000
	
    
		dfs.namenode.rpc-address.ns.nn2
		node2:9000
	
    
		dfs.namenode.http-address.ns.nn1
		node1:50070
	
    
		dfs.namenode.http-address.ns.nn2
		node2:50070
	
    
		dfs.namenode.shard.edits.dir
		qjournal://node1:8485;node2:8485;node3:8485/ns
	
	
        
		dfs.namenode.secondary.http-address
		node1:9001
	
	
        
		dfs.webhdfs.enabled
		true
	
    
		dfs.ha.automatic-failover.enabled.ns
		true
	
	
        
		dfs.permissions
		false
	
    
		dfs.ha.fencing.methods
		sshfence
	
    
		dfs.ha.fencing.ssh.private-key-files
		~/.ssh/id_rsa
	
    
    
		dfs.client.failover.proxy.provider.ns
		org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider
	


	4. mapred-site.xml

    
	
		mapreduce.framework.name
		yarn
	
    
	
		mapreduce.jobhistory.address
		node1:10200
	
    
	
		mapreduce.jobhistory.webapp.address
		node1:19888

6. 配置yarn集群
	yarn-site.xml

    
	
		yarn.nodemanager.aux-services
		mapreduce_shuffle
	
	
		yarn.nodemanager.aux-services.mapreduce.shuffle.class
		org.apache.hadoop.mapred.ShuffleHandler
	
    
		yarn.resourcemanager.ha.enabled
		true
	
    
		yarn.resourcemanager.cluster-id
		ns
	
    
		yarn.resourcemanager.ha.rm-ids
		rm1,rm2
	
    
		yarn.resourcemanager.hostname.rm1
		node1
	
    
		yarn.resourcemanager.hostname.rm2
		node2
	
    
		yarn.resourcemanager.webapp.address.rm1
		node1:8088
	
    
		yarn.resourcemanager.webapp.address.rm2
		node2:8088
	
    
		yarn.resourcemanager.recovery.enabled
		true
	
    
    
		yarn.resourcemanager.store.class
		org.apache.hadoop.yarn.server.resourcemanager.recovery.ZKRMStateStore
	
    
	
		yarn.log-aggregation-enable
		true
	
     
	
		yarn.log-aggregation-retain-seconds
		604800
	
    
	
		yarn.resourcemanager.zk-address
		node1:2181,node2:2181,node3:2181
	
    
	
		yarn.nodemanager.resource.memory-mb
		4096
	
    
    
		yarn.nodemanager.vmem-check-enabled
		false
	
    
		yarn.nodemanager.pmem-check-enabled
		false
	
    
        yarn.client.failover-proxy-provider
        org.apache.hadoop.yarn.client.ConfiguredRMFailoverProxyProvider
   
   
        yarn.resourcemanager.ha.automatic-failover.enabled
        true
   
   
    
        yarn.resourcemanager.am.max-attempts
        10

7. 将/usr/local/hadoop文件夹分发给slave1和slave2
	scp -r hadoop root@node2:/usr/local/
	scp -r hadoop root@node3:/usr/local/

8. 启动集群
	1) 在node1上
		hdfs zkfc -formatZK
	2) 在三个节点分别启动
		hadoop-daemon.sh start journalnode
	3) 在node1
		hdfs namenode -format
		hadoop-daemon.sh start namenode
	4) 在node2上
		hdfs namenode -bootstrapStandby
		hadoop-daemon.sh start namenode
	5) 在node1和node2上
		hadoop-daemon.sh start zkfc
	6) 在三个节点上分别启动
		hadoop-daemon.sh start datanode
	7) 在node1和node2上
		yarn-daemon.sh start resourcemanager
	8) 在三个节点上分别启动
		yarn-daemon.sh start nodemanager
10. 验证
	jps
	
日常启动
	在三个节点分别启动
		hadoop-daemon.sh start journalnode
	在node1和node2启动
		hadoop-daemon.sh start zkfc
	一键启动
		start-dfs.sh
		start-yarn.sh

三、安装Zookeeper

1. 下载解压
	tar -xvf apache-zookeeper-3.5.9-bin.tar.gz -C /usr/local
	mv /usr/local/apache-zookeeper-3.5.9 /usr/local/zookeeper
	
2. 修改用户名和用户组权限
	chown -R root:root zookeeper/

3. 配置环境变量

4. 修改配置文件
	cp zoo_sample.cfg zoo.cfg
	
# The number of milliseconds of each tick
tickTime=2000
# The number of ticks that the initial
# synchronization phase can take
initLimit=10
# The number of ticks that can pass between
# sending a request and getting an acknowledgement
syncLimit=5
# the directory where the snapshot is stored.
# do not use /tmp for storage, /tmp here is just
# example sakes.
dataDir=/usr/local/zookeeper/tmp/data/zookeeper
dataLogDir=/usr/local/zookeeper/tmp/log/zookeeper
# the port at which the clients will connect
clientPort=2181
autopurge.purgeInterval=1
server.1=node1:2888:3888
server.2=node2:2888:3888
server.3=node3:2888:3888
# 注：server.1中的1为服务器id，需要与myid中的id一致

# 每个节点重复以上步骤

5. 设置服务器id
	touch /usr/local/zookeeper/tmp/data/zookeeper/myid
	echo 1 > /usr/local/zookeeper/tmp/data/zookeeper/myid
# node2 2 ， node3中echo 3

6. 启动服务器
zkServer.sh start

7. 连接客户端
zkCli.sh -server node1:2181

四、安装Flink

1. 下载解压
	tar -xvf flink-1.13.2-bin-scala_2.11.tgz -C /usr/local/
	mv /usr/local/flink-1.13.2 /usr/local/flink
    
2. 配置环境变量
	export HADOOP_CLASSPATH=`/usr/local/hadoop/bin/hadoop classpath`
	export Flink_HOME=/usr/local/flink

3. 编辑配置文件
	vi flink-conf.yaml
# JobManager内存主要分为四部分：JVM Heap、Off-Heap Memory、JVM metaspace、JVM Overhead
# JobManager总内存设置为2048m，则JVM Overhead可根据0.1的fraction换算得到204.8m，即JVM Overhead内存为205m
# JVM metaspace默认为256m
# Off-Heap Memory默认为128m
# JVM Heap最终被推断为2048m-205m-256m - 128m = 1459m，即1.42g
# 但gc算法会占用一小部分固定内存作为Non-Heap，占用大小为0.05g
# JVM Heap实际大小为1.42g - 0.05g = 1.38g
jobmanager.rpc.address: node1

jobmanager.rpc.port: 6123
#JobManager jvm堆大小,主要取决于运行的作业数量、作业结构及用户代码的要求
jobmanager.heap.size: 1024m
#进程总内存
jobmanager.memory.process.size: 2048m

taskmanager.memory.process.size: 4096m
#每个TaskManager提供的任务Slots数量，建议与cpu核数一致
taskmanager.numberOfTaskSlots: 4

parallelism.default: 1

env.hadoop.conf.dir: /usr/local/hadoop/etc/hadoop

high-availability: zookeeper
# flink在重启时，尝试的最大次数
yarn.application-attempts: 10

high-availability.storageDir: hdfs://ns/flink/recovery

high-availability.zookeeper.quorum: node1:2181,node2:2181,node3:2181

high-availability.zookeeper.path.root: /flink
#用于存储和检查点状态
state.backend: filesystem

state.checkpoints.dir: hdfs://ns/flink/checkpoints

state.savepoints.dir: hdfs://ns/flink/savepoints
#故障转移策略
jobmanager.execution.failover-strategy: region

rest.port: 8081
#是否启动web提交
web.submit.enable: true

io.tmp.dirs: /usr/local/flink/data/tmp

env.log.dir: /usr/local/flink/data/logs

taskmanager.memory.network.fraction: 0.1
taskmanager.memory.network.min: 64mb
taskmanager.memory.network.max: 1gb
fs.hdfs.hadoopconf: /usr/local/hadoop/etc/hadoop

historyserver.web.address: 0.0.0.0

historyserver.web.port: 8082

historyserver.archive.fs.refresh-interval: 10000

4. 修改masters
	node1:8081
	node2:8081
	
5. 修改workers
	node1
	node2
	node3
6. 修改zoo.cfg
	tickTime=2000
	
	initLimit=10
	
	syncLimit=5
	
	dataDir=/usr/local/flink/data/tmp/zookeeper/dataDir
	dataLogDir=/usr/local/flink/data/tmp/zookeeper/dataLogDir
	
	clientPort=2181
	server.1=node1:2888:3888
	server.2=node2:2888:3888
	server.3=node3:2888:3888

7. 添加jar包
	flink-shaded-hadoop-2-uber-2.8.3-10.0.jar
	
8. 启动flink yarn session模式
	yarn-session.sh

Flink On Yarn模式配置

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