flinksql的滚动窗口实现

滚动窗口在flinksql中是TUMBLE

eventTime

package com.bigdata.day08;


import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;


public class _01_flinkSql_eventTime_tumble {
    /**
     * eventTime + 滚动窗口 60秒 + 3秒的水印
     * 
     * 
     * 数据格式
     * {"username":"zs","price":20,"event_time":"2023-07-18 12:12:04"}
     * {"username":"zs","price":20,"event_time":"2023-07-18 12:13:00"}
     * {"username":"zs","price":20,"event_time":"2023-07-18 12:13:03"}
     * {"username":"zs","price":20,"event_time":"2023-07-18 12:14:03"}
     */

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

        //1. env-准备环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        StreamTableEnvironment tenv = StreamTableEnvironment.create(env);

        //2. 创建表
        tenv.executeSql("CREATE TABLE table1 (\n" +
                "  `username` String,\n" +
                "  `price` int,\n" +
                "  `event_time` TIMESTAMP(3),\n" +
                "   watermark for event_time as event_time - interval '3' second\n" +
                ") WITH (\n" +
                "  'connector' = 'kafka',\n" +
                "  'topic' = 'topic1',\n" +
                "  'properties.bootstrap.servers' = 'bigdata01:9092,bigdata02:9092,bigdata03:9092',\n" +
                "  'properties.group.id' = 'testGroup1',\n" +
                "  'scan.startup.mode' = 'latest-offset',\n" +
                "  'format' = 'json'\n" +
                ")");
        //3. 通过sql语句统计结果

        tenv.executeSql("select \n" +
                "   window_start,\n" +
                "   window_end,\n" +
                "   username,\n" +
                "   count(1) zongNum,\n" +
                "   sum(price) totalMoney \n" +
                "   from table(TUMBLE(TABLE table1, DESCRIPTOR(event_time), INTERVAL '60' second))\n" +
                "group by window_start,window_end,username").print();
        //4. sink-数据输出



        //5. execute-执行
        env.execute();
    }
}

processTime

package com.bigdata.day08;


import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;


public class _03_flinkSql_processTime_tumble {
    /**
     * process + 滚动窗口60秒
     * 
     * 数据格式
     * {"username":"zs","price":20}
     * {"username":"lisi","price":15}
     * {"username":"lisi","price":20}
     * {"username":"zs","price":20}
     * {"username":"zs","price":20}
     * {"username":"zs","price":20}
     * {"username":"zs","price":20}
     */

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

        //1. env-准备环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        StreamTableEnvironment tenv = StreamTableEnvironment.create(env);

        //2. 创建表
        tenv.executeSql("CREATE TABLE table1 (\n" +
                "  `username` String,\n" +
                "  `price` int,\n" +
                "  `event_time` as proctime()\n" +
                ") WITH (\n" +
                "  'connector' = 'kafka',\n" +
                "  'topic' = 'topic1',\n" +
                "  'properties.bootstrap.servers' = 'bigdata01:9092,bigdata02:9092,bigdata03:9092',\n" +
                "  'properties.group.id' = 'testGroup1',\n" +
                "  'scan.startup.mode' = 'latest-offset',\n" +
                "  'format' = 'json'\n" +
                ")");
        //3. 通过sql语句统计结果

        tenv.executeSql("select \n" +
                "   window_start,\n" +
                "   window_end,\n" +
                "   username,\n" +
                "   count(1) zongNum,\n" +
                "   sum(price) totalMoney \n" +
                "   from table(TUMBLE(TABLE table1, DESCRIPTOR(event_time), INTERVAL '60' second))\n" +
                "group by window_start,window_end,username").print();
        //4. sink-数据输出



        //5. execute-执行
        env.execute();
    }
}