Flink-sql 计数窗口

1. Flink 的计数窗口有两种

1.1 计数混动窗口

package com.wudl.flink.sql;

import com.wudl.flink.bean.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.Session;
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.*;

/**
 * @ClassName : Flink_Group_Window  --  基于计数滚动窗口
 * @Description : Flink sql 窗口
 * @Author :wudl
 * @Date: 2021-08-04 23:13
 */

public class Flink_Group_Count_Window {
    public static void main(String[] args) throws Exception {

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        StreamTableEnvironment tableEnvironment = StreamTableEnvironment.create(env);
        DataStreamSource<String> streamSource = env.socketTextStream("192.168.1.180", 9999);
        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 = tableEnvironment.fromDataStream(waterDS,
                $("id"),
                $("ts"),
                $("vc"),
                $("pt").proctime());

        // 开窗滚动窗口计算wordCound
        Table result = table.window(Tumble.over(rowInterval(5L)).on($("pt")).as("cw"))
                .groupBy($("id"), $("cw"))
                .select($("id"), $("id").count());

        // 将结果表转化为流进行输出

        tableEnvironment.toAppendStream(result, Row.class).print();
        env.execute();
    }
}

1.2 计数窗口的滑动

package com.wudl.flink.sql;

import com.wudl.flink.bean.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.Slide;
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.rowInterval;

/**
 * @ClassName : Flink_Group_Window  --  基于计数滑动窗口
 * @Description : Flink sql 窗口
 * @Author :wudl
 * @Date: 2021-08-04 23:13
 */

public class Flink_Group_Count_Sliding_Window {
    public static void main(String[] args) throws Exception {

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        StreamTableEnvironment tableEnvironment = StreamTableEnvironment.create(env);
        DataStreamSource<String> streamSource = env.socketTextStream("192.168.1.180", 9999);
        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 = tableEnvironment.fromDataStream(waterDS,
                $("id"),
                $("ts"),
                $("vc"),
                $("pt").proctime());

        // 开窗滚动窗口计算wordCound
        Table result = table.window(Slide.over(rowInterval(5L)).every(rowInterval(2L)).on($("pt")).as("cw"))
                .groupBy($("id"), $("cw"))
                .select($("id"), $("id").count());

        // 将结果表转化为流进行输出

        tableEnvironment.toAppendStream(result, Row.class).print();
        env.execute();
    }
}