(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<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)效果演示