（2）FlinkSQL滚动窗口demo演示

（2）FlinkSQL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

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