Flink高可用集群搭建

文章目录

1.高可用集群搭建1.1上传安装包1.2解压1.3重命名1.4配置环境变量1.5修改配置文件1.5.1masters1.5.2slaves1.5.3flink-conf.yaml 1.6拷贝配置文件1.7远程发送文件 2.WordCount程序2.1java版本2.2scala版本 安装节点要求：

jdk1.8hadoop2.7.6scala2.11.8zookeeper3.4.10

节点分配

JobManagerTaskManagerZooKeeperhadoop01√√√hadoop02√√√hadoop03√√

1.高可用集群搭建

1.1上传安装包

rz -E C:/flink-1.7.2-bin-hadoop27-scala_2.11.tgz

1.2解压

tar -zxvf flink-1.7.2-bin-hadoop27-scala_2.11.tgz -C ~/apps/

1.3重命名

mv flink-1.7.2 flink

1.4配置环境变量

vim ~/.bash_profile export FLINK_HOME=/home/hadoop/apps/flink export PATH=$PATH:$FLINK_HOME/bin

重新加载配置文件

source ~/.bash_profile

1.5修改配置文件

1.5.1masters

vi $FLINK_HOME/conf/masters hadoop01:8081 hadoop02:8081

1.5.2slaves

vi $FLINK_HOME/conf/slaves hadoop01 hadoop02 hadoop03

1.5.3flink-conf.yaml

vi $FLINK_HOME/conf/flink-conf.yaml ################################################################################ # 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/Mesos # automatically configure the host name based on the hostname of the node where the # JobManager runs. #指定主节点，可以为localhost，这样在哪里启动谁就是JobManager jobmanager.rpc.address: hadoop01 # The RPC port where the JobManager is reachable. jobmanager.rpc.port: 6123 # The heap size for the JobManager JVM jobmanager.heap.size: 1024m # The heap size for the TaskManager JVM taskmanager.heap.size: 1024m # 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: 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'. # 指定使用 zookeeper 进行 HA 协调 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://bd1906/flink172/hastorage/ # 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: hadoop01:2181,hadoop02:2181,hadoop03:2181 # 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. # # Supported backends are 'jobmanager', 'filesystem', 'rocksdb', or the # <class-name-of-factory>. # # 指定 checkpoint 的类型和对应的数据存储目录 state.backend: filesystem state.backend.fs.checkpointdir: hdfs://bd1906/flink-checkpoints # 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-checkpoints # Flag to enable/disable incremental checkpoints for backends that # support incremental checkpoints (like the RocksDB state backend). # # state.backend.incremental: false #============================================================================== # Web Frontend #============================================================================== # The address under which the web-based runtime monitor listens. # #web.address: 0.0.0.0 # The port under which the web-based runtime monitor listens. # A value of -1 deactivates the web server. rest.port: 8081 # Flag to specify whether job submission is enabled from the web-based # runtime monitor. Uncomment to disable. #web.submit.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 or Mesos, 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 # Specify whether TaskManager's managed memory should be allocated when starting # up (true) or when memory is requested. # # We recommend to set this value to 'true' only in setups for pure batch # processing (DataSet API). Streaming setups currently do not use the TaskManager's # managed memory: The 'rocksdb' state backend uses RocksDB's own memory management, # while the 'memory' and 'filesystem' backends explicitly keep data as objects # to save on serialization cost. # # taskmanager.memory.preallocate: false # 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, teh default max is 1GB. # # taskmanager.network.memory.fraction: 0.1 # taskmanager.network.memory.min: 64mb # taskmanager.network.memory.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

1.6拷贝配置文件

拷贝zoo.cfg、hdfs-site.xml、core-site.xml到flink配置文件目录

cp $ZOOKEEPER_HOME/conf/zoo.cfg $FLINK_HOME/conf/ cp $HADOOP_HOME/etc/hadoop/hdfs-site.xml $FLINK_HOME/conf/ cp $HADOOP_HOME/etc/hadoop/core-site.xml $FLINK_HOME/conf/

1.7远程发送文件

scp -r flink hadoop02:$PWD scp -r flink hadoop03:$PWD scp ~/.bash_profile hadoop02:/home/hadoop/ scp ~/.bash_profile hadoop03:/home/hadoop/

三台机器都要重新加载配置文件

source ~/.bash_profile

如果前面修改了jobmanager.rpc.address的值，请修改hadoop02上的flink-conf.yaml中jobmanager.rpc.address的值为hadoop02，hadoop03可改可不改，这样才能看出高可用集群的效果！！

依次启动zk、hdfs、flink

zkServer.sh start start-dfs.sh start-cluster.sh

查看进程

jps

查看Web UI http://hadoop01:8081/

可以跑一个官方案例测试一下（输入文件为flink文件夹中的README.txt文件）

flink run -m hadoop02:8081 \ $FLINK_HOME/examples/batch/WordCount.jar

至此集群搭建成功！！

停止集群命令

stop-cluster.sh

2.WordCount程序

Maven依赖

<properties> <flink.version>1.7.2</flink.version> <hadoop.version>2.7.6</hadoop.version> <scala.version>2.11.8</scala.version> </properties> <dependencies> <!-- flink核心API --> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-java</artifactId> <version>${flink.version}</version> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-scala_2.11</artifactId> <version>${flink.version}</version> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-streaming-java_2.11</artifactId> <version>${flink.version}</version> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-streaming-scala_2.11</artifactId> <version>${flink.version}</version> </dependency> </dependencies>

2.1java版本

WordCountJava.java

package wc; import org.apache.flink.api.common.functions.FlatMapFunction; import org.apache.flink.api.common.functions.MapFunction; import org.apache.flink.api.common.typeinfo.Types; import org.apache.flink.api.java.ExecutionEnvironment; import org.apache.flink.api.java.operators.AggregateOperator; import org.apache.flink.api.java.operators.DataSource; import org.apache.flink.api.java.operators.FlatMapOperator; import org.apache.flink.api.java.operators.MapOperator; import org.apache.flink.api.java.tuple.Tuple2; /** * @Author Daniel * @Description java版本Flink wordcount 程序 **/ public class WordCountJava { public static void main(String[] args) { //编程入口 ExecutionEnvironment batchEnv = ExecutionEnvironment.getExecutionEnvironment(); //数据源 DataSource<String> dataSource = batchEnv.fromElements("hadoop hadoop", "spark saprk saprk", "flink flink flink"); //flatMap算子，一行转多行 FlatMapOperator<String, String> wordDataSet = dataSource.flatMap((FlatMapFunction<String, String>) (value, out) -> { String[] words = value.split(" "); for (String word : words) { out.collect(word); } }).returns(Types.STRING); //map算子，计数 MapOperator<String, Tuple2<String, Integer>> wordAndOneDataSet = wordDataSet.map((MapFunction<String, Tuple2<String, Integer>>) value -> new Tuple2(value, 1)) .returns(Types.TUPLE(Types.STRING, Types.INT)); //分组并计数 AggregateOperator<Tuple2<String, Integer>> lastResult = wordAndOneDataSet.groupBy(0) .sum(1); try { //Sink打印结果 lastResult.print(); // batchEnv.execute("WordCountJava");//批处理不用此方法，流处理得使用 } catch (Exception e) { e.printStackTrace(); } } }

2.2scala版本

WordCountScala.scala

package wc import org.apache.flink.streaming.api.scala.{DataStream, StreamExecutionEnvironment, _} /** * @Author Daniel * @Description scala版本Flink wordcount 程序 **/ object WordCountScala { def main(args: Array[String]): Unit = { //获取flink编程入口 val streamEnv = StreamExecutionEnvironment.getExecutionEnvironment //从网络端口读取流数据 val dS = streamEnv.socketTextStream("hadoop01", 9999) // 主要业务逻辑 val resultDS = dS.flatMap(line => line.toString.split(" ")) .map(word => Word(word, 1)) .keyBy("word") .sum("count") //输出 resultDS.print() //进行流数据处理，不间断的运行 streamEnv.execute("StreamWordCountScala") } } //良好的数据结构 case class Word(word: String, count: Int) nc -lk hadoop01 9999 > hadoop hadoop spark spark spark flink flink flink flink