一、环境准备

1、准备两台服务器server115 和server116安装好hadoop环境，其中server115配置hdfs的namenode，在server116上配置hdfs的SecondaryNameNode，server116配置yarn的 ResourceManager，启动hadoop集群

2、配置hadoop环境变量

   vim  /etc/profile

export HADOOP_CLASSPATH=`hadoop classpath`

二、配置FLink集群环境

关于flink的架构图

 部署配置：

(1) 进入server115节点，编辑flink的配置文件flink-conf.yml

# JobManager runs.
jobmanager.rpc.address: server116
jobmanager.bind-host: server116
taskmanager.bind-host: server115
taskmanager.host: server115
rest.port: 8081
rest.address: server116

(2) 进入server116节点，编辑flink的配置文件flink-conf.yml

# JobManager runs.
jobmanager.rpc.address: server116
jobmanager.bind-host: server116
taskmanager.bind-host: server116
taskmanager.host:server116
rest.port: 8081
rest.address: server116

(3)在server115、server116的节点workers配置

server115
server116

三、启动flink

 ./flink/bin/yarn-session.sh    -d

 JobManager Web Interface: http://server116:40617

四、任务测试

  1、 开启一个tcp服务

nc  -l  -p  9999

 2、编写java侦听代码

public class SocketFlinkExecute {public static void main(String[] args) {StreamExecutionEnvironment env = StreamExecutionEnvironment.createRemoteEnvironment("server116", 40617);DataStream> dataStream = env.socketTextStream("server116", 9999).flatMap(new MySplitter()).keyBy(value -> value.f0).window(TumblingProcessingTimeWindows.of(Time.seconds(5))).sum(1);dataStream.print();try {env.execute("Window WordCount Remote");} catch (Exception e) {e.printStackTrace();}}public static class MySplitter implements FlatMapFunction> {@Overridepublic void flatMap(String sentence, Collector> out) throws Exception {for (String word: sentence.split(" ")) {out.collect(new Tuple2(word, 1));}}}}

运行程序，tcp服务端发送文本数据