关于flink:4FlinkSQL将socket数据写入到mysql方式一

本章节次要演示从 socket 接收数据，通过滚动窗口每 30 秒运算一次窗口数据，而后将后果写入 Mysql 数据库

（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 com.sinks.RetractStream_Mysql;
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.bridge.java.StreamTableEnvironment;
import org.apache.flink.types.Row;

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

/**

• Created by lj on 2022-07-06.
  */

public class Flink_Group_Window_Tumble_Sink_Mysql {

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.toRetractStream(result, Row.class).addSink(new RetractStream_Mysql()); 
    env.execute();}

}

（4）定义一个写入到 mysql 的 sink

package com.sinks;

import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.PreparedStatement;

import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.functions.sink.RichSinkFunction;
import org.apache.flink.types.Row;

/**

• Created by lj on 2022-07-06.
  */

public class RetractStream_Mysql extends RichSinkFunction<Tuple2<Boolean, Row>> {

private static final long serialVersionUID = -4443175430371919407L;
PreparedStatement ps;
private Connection connection;

/**
 * open() 办法中建设连贯，这样不必每次 invoke 的时候都要建设连贯和开释连贯
 *
 * @param parameters
 * @throws Exception
 */
@Override
public void open(Configuration parameters) throws Exception {super.open(parameters);
    connection = getConnection();}

@Override
public void close() throws Exception {super.close();
    // 敞开连贯和开释资源
    if (connection != null) {connection.close();
    }
    if (ps != null) {ps.close();
    }
}

/**
 * 每条数据的插入都要调用一次 invoke() 办法
 *
 * @param context
 * @throws Exception
 */
@Override
public void invoke(Tuple2<Boolean, Row> userPvEntity, Context context) throws Exception {String sql = "INSERT INTO flinkcomponent(componentname,componentcount,componentsum) VALUES(?,?,?);";
    ps = this.connection.prepareStatement(sql);

    ps.setString(1,userPvEntity.f1.getField(0).toString());
    ps.setInt(2, Integer.parseInt(userPvEntity.f1.getField(1).toString()));
    ps.setInt(3, Integer.parseInt(userPvEntity.f1.getField(2).toString()));
    ps.executeUpdate();}

private static Connection getConnection() {
    Connection con = null;
    try {Class.forName("com.mysql.jdbc.Driver");
        con = DriverManager.getConnection("jdbc:mysql://localhost:3306/testdb?useUnicode=true&characterEncoding=UTF-8&useSSL=false","root","root");
    } catch (Exception e) {System.out.println("-----------mysql get connection has exception , msg ="+ e.getMessage());
    }
    return con;
}

}

（5）成果演示，每 30 秒往数据库写一次数据