flink偽分布式搭建及其本地idea測flink連接

下載安裝flink：
上傳壓縮包：flink-1.7.2-bin-scala_2.12.tgz
解壓：tar -zxvf /flink-1.7.2-bin-scala_2.12.tgz -C ../hone
復制解壓文件到子節點：
scp -r /home/flink-1.7.2/ root@slave1:/home/
scp -r /home/flink-1.7.2/ root@slave2:/home/
修改配置文件：選擇一個master節點，配置conf/flink-conf.yaml
vi conf/flink-conf.yaml
設置jobmanager.rpc.address 配置項為該節點的IP 或者主機名
jobmanager.rpc.address: 10.108.4.202
然后添加子節點配置：
在所有的節點中：flink目錄下：vi conf/slaves
添加所有子節點ip然后保存
啟動本地的flink集群：
cd 到flink目錄下
./bin/start-cluster.sh
查看webui:ip:8081
啟動監聽：nc -lk 9000
當報nc命令不存在時（yum install nc）
然后執行測試jar：
停止flink集群：bin/stop-cluster.sh
以集群方式提交任務：在flink目錄下
./bin/flink run -m yarn-cluster -c com.demo.florian.WordCount $DEMO_DIR/target/flink-demo-1.0-SNAPSHOT.jar --port 9000

新建maven程序
pom.xml依賴如下：
然后新建一個TestSocketWindowWordCount類具體代碼如下
然后啟動flink集群->新建一個監聽:nc -lk 6666
然后啟動TestSocketWindowWordCount類
在linux監聽頁面輸入代碼
觀察在idea控制臺就有統計的輸出
-------pom.xml開始-----------
<dependencies>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-java</artifactId>
<version>1.9.0</version>
<scope>compile</scope>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-streaming-java_2.11</artifactId>
<version>1.9.0</version>
<scope>compile</scope>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-streaming-scala_2.11</artifactId>
<version>1.6.2</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-clients_2.11</artifactId>
<version>1.6.2</version>
</dependency>
</dependencies>
-------pom.xml結束-----------
-------TestSocketWindowWordCount開始------------------
package com.gyb;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.ReduceFunction;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.util.Collector;

import javax.xml.soap.Text;

public class TestSocketWindowWordCount {
public static void main(String args[]) {
String hostname = "192.168.198.130";
int port = 6666;
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
DataStream text = env.socketTextStream(hostname, port, "\n");//獲取執行環境
SingleOutputStreamOperator windowCounts = text
.flatMap(new FlatMapFunction<String, SocketWindowWordCount.WordWithCount>() {@Override
br/>@Override
for (String word : value.split("\s")) {
out.collect(new SocketWindowWordCount.WordWithCount(word, 1L));
}
}
})
.keyBy("word")
.timeWindow(Time.seconds(5), Time.seconds(5))
.reduce(new ReduceFunction<SocketWindowWordCount.WordWithCount>() {@Override
br/>@Override
return new SocketWindowWordCount.WordWithCount(a.word, a.count + b.count);
}
});

// print the results with a single thread, rather than in parallel
windowCounts.print().setParallelism(1);
//env.execute("Socket Window WordCount");
try {
    env.execute("Socket Window WordCount");
} catch (Exception e) {
    e.printStackTrace();
}

}

public static class WordWithCount {

    public String word;
    public long count;

    public WordWithCount() {}

    public WordWithCount(String word, long count) {
        this.word = word;
        this.count = count;
    }

    @Override
    public String toString() {
        return word + " : " + count;
    }
}

}
-------TestSocketWindowWordCount結束------------------