（2）FlinkSQL流计算-滚动窗口demo演示

滚动窗口（Tumbling Windows） 滚动窗口有固定的大小，是一种对数据进行均匀切片的划分方式。窗口之间没有重叠，也不会有间隔，是“首尾相接”的状态。滚动窗口可以基于时间定义，也可以基于数据个数定义；需要的参数只有一个，就是窗口的大小（window size）。

demo演示：

场景：接收通过socket发送过来的数据，每30秒触发一次窗口计算逻辑

（1）准备一个实体对象，消息对象

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，模拟数据发送

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 发送一次消息
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，是一种对数据进行均匀切片的划分方式。窗口之间没有重叠，也不会有间隔，
* 是“首尾相接”的状态。滚动窗口可以基于时间定义，也可以基于数据个数定义；需要的参数只有一个，
* 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 streamSource = env.socketTextStream("127.0.0.1", 9999,"\n");
SingleOutputStreamOperator waterDS = streamSource.map(new MapFunction() {
@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）效果演示