关于flink:2FlinkSQL滚动窗口demo演示

滚动窗口（Tumbling Windows）滚动窗口有固定的大小，是一种对数据进行平均切片的划分形式。窗口之间没有重叠，也不会有距离，是“首尾相接”的状态。滚动窗口能够基于工夫定义，也能够基于数据个数定义；须要的参数只有一个，就是窗口的大小（window size）。

demo 演示：场景：接管通过 socket 发送过去的数据，每 30 秒触发一次窗口计算逻辑（1）筹备一个实体对象，音讯对象

package com.pojo;

import java.io.Serializable;

/**

• Created by lj on 2022-07-05.
  */

public class WaterSensor implements Serializable {

private String id;
private long ts;
private int vc;

public WaterSensor(){}

public WaterSensor(String id,long ts,int vc){
    this.id = id;
    this.ts = ts;
    this.vc = vc;
}

public int getVc() {return vc;}

public void setVc(int vc) {this.vc = vc;}

public String getId() {return id;}

public void setId(String id) {this.id = id;}

public long getTs() {return ts;}

public void setTs(long ts) {this.ts = ts;}

}

（2）编写 socket 代码，模仿数据发送
package com.producers;

import java.io.BufferedWriter;
import java.io.IOException;
import java.io.OutputStreamWriter;
import java.net.ServerSocket;
import java.net.Socket;
import java.util.Random;

/**

• Created by lj on 2022-07-05.
  */

public class Socket_Producer {

public static void main(String[] args) throws IOException {

    try {ServerSocket ss = new ServerSocket(9999);
        System.out.println("启动 server ....");
        Socket s = ss.accept();
        BufferedWriter bw = new BufferedWriter(new OutputStreamWriter(s.getOutputStream()));
        String response = "java,1,2";

        // 每 2s 发送一次音讯
        int i = 0;
        Random r=new Random();   
        String[] lang = {"flink","spark","hadoop","hive","hbase","impala","presto","superset","nbi"};

        while(true){Thread.sleep(2000);
            response= lang[r.nextInt(lang.length)] + "," + i + "," + i+"\n";
            System.out.println(response);
            try{bw.write(response);
                bw.flush();
                i++;
            }catch (Exception ex){System.out.println(ex.getMessage());
            }

        }
    } catch (IOException | InterruptedException e) {e.printStackTrace();
    }
}

}

（3）从 socket 端接收数据，并设置 30 秒触发执行一次窗口运算
package com.examples;

import com.pojo.WaterSensor;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.Tumble;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.types.Row;

import static org.apache.flink.table.api.Expressions.$;
import static org.apache.flink.table.api.Expressions.lit;

/**

• Created by lj on 2022-07-06.
  *
• 滚动窗口（Tumbling Windows）滚动窗口有固定的大小，是一种对数据进行平均切片的划分形式。窗口之间没有重叠，也不会有距离，
• 是“首尾相接”的状态。滚动窗口能够基于工夫定义，也能够基于数据个数定义；须要的参数只有一个，
• 就是窗口的大小（window size）。
  */

public class Flink_Group_Window_Tumble {

public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
    env.setParallelism(1);
    StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);
    DataStreamSource<String> streamSource = env.socketTextStream("127.0.0.1", 9999,"\n");
    SingleOutputStreamOperator<WaterSensor> waterDS = streamSource.map(new MapFunction<String, WaterSensor>() {
        @Override
        public WaterSensor map(String s) throws Exception {String[] split = s.split(",");
            return new WaterSensor(split[0], Long.parseLong(split[1]), Integer.parseInt(split[2]));
        }
    });

    // 将流转化为表
    Table table = tableEnv.fromDataStream(waterDS,
            $("id"),
            $("ts"),
            $("vc"),
            $("pt").proctime());

    tableEnv.createTemporaryView("EventTable", table);

    Table result = tableEnv.sqlQuery(
            "SELECT" +
                    "id," +                //window_start, window_end,
                    "COUNT(ts) ,SUM(ts)" +
                    "FROM TABLE(" +
                    "TUMBLE( TABLE EventTable ," +
                    "DESCRIPTOR(pt)," +
                    "INTERVAL'30'SECOND))" +
                    "GROUP BY id , window_start, window_end"
    );

// tableEnv.toChangelogStream(result).print(“count”);
// tableEnv.toDataStream(result).print(“toDataStream”);
// tableEnv.toAppendStream(result, Row.class).print(“toAppendStream”); // 追加模式

    tableEnv.toRetractStream(result, Row.class).print("toRetractStream");       // 缩进模式
    env.execute();}

}
（4）成果演示