flinkSql中滑动窗口的实现
·
在flinksql 中使用HOP表示滑动窗口
eventTime
package com.bigdata.day08;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
public class _02_flinkSql_eventTime_hop {
/**
* eventTime + 滑动窗口 窗口大小为60s 滑动距离为10s 水印3s
*
* 数据
* {"username":"zs","price":20,"event_time":"2023-07-18 12:12:04.100"}
* 触发数据
* {"username":"zs","price":20,"event_time":"2023-07-18 12:12:13.000"}
* 第一个窗口2023-07-18 12:11:10.000 到 2023-07-18 12:12:10.000
*/
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(HOP(TABLE table1, DESCRIPTOR(event_time), INTERVAL '10' second,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 _04_flinkSql_processTime_hop {
/**
* processTime + 滑动窗口 窗口大小为60 滑动距离的10s
* 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(hop(TABLE table1, DESCRIPTOR(event_time), INTERVAL '10' second ,INTERVAL '60' second))\n" +
"group by window_start,window_end,username").print();
//4. sink-数据输出
//5. execute-执行
env.execute();
}
}
更多推荐


所有评论(0)