eventTime
package com.bigdata.day08;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
public class _05_flinkSql_Cumulate_eventTime {
/**
* 累积窗口 + eventTime
* 1 分钟 每十秒计算一次 3秒水印
* 数据格式
* {"username":"zs","price":20,"event_time":"2023-07-18 12:12:43.000"}
* {"username":"zs","price":20,"event_time":"2023-07-18 12:12:53.000"}
* {"username":"zs","price":20,"event_time":"2023-07-18 12:13:03.000"}
* {"username":"zs","price":20,"event_time":"2023-07-18 12:13:13.000"}
*/
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(CUMULATE(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 _06_flinkSql_Cumulate_processTime {
/**
* 累积窗口 + processTime
* 1 分钟 每十秒计算一次
* 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(CUMULATE(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();
}
}
topN案例
需求：在每个分钟内找出点击量最多的Top 3网页。
滚动窗口（1分钟）+eventTime+3秒水印
hive sql
with t1 as (
select page_id,sum(clicks) totalSum
from table1
group by page_id
), t2 as(
select page_id,totalSum,
row_number() over ( order by totalSum desc) px
from t1
) select * from t2 where px <=3
flink sql
with t1 as (
select window_start,window_end,page_id,sum(clicks) totalSum
from table(tumble(table table1,DESCRIPTOR(event_time), INTERVAL '60' second ))
group by window_start,window_end,page_id
), t2 as(
select window_start,window_end,page_id,totalSum,
row_number() over (partition by window_start,window_end order by totalSum desc) px
from t1
) select * from t2 where px <=3
* 数据格式
{"ts": "2023-09-05 12:00:10", "page_id": 1, "clicks": 100}
{"ts": "2023-09-05 12:00:20", "page_id": 2, "clicks": 90}
{"ts": "2023-09-05 12:00:30", "page_id": 3, "clicks": 110}
{"ts": "2023-09-05 12:00:40", "page_id": 4, "clicks": 23}
{"ts": "2023-09-05 12:00:50", "page_id": 5, "clicks": 456}
{"ts": "2023-09-05 12:00:55", "page_id": 5, "clicks": 456}
// 触发数据
{"ts": "2023-09-05 12:01:03", "page_id": 5, "clicks": 456}
package com.bigdata.day08;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
public class _07_flinkSql_topN {
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
//3. 通过sql语句统计结果
tenv.executeSql("CREATE TABLE table1 (\n" +
" `page_id` INT,\n" +
" `clicks` INT,\n" +
" `ts` TIMESTAMP(3) ,\n" +
" watermark for ts as ts - 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" +
")");
tenv.executeSql("with t1 as (\n" +
"\tselect window_start,window_end,page_id,sum(clicks) totalSum from table(tumble(table table1,DESCRIPTOR(ts), INTERVAL '60' second )) group by window_start,window_end,page_id\n" +
"), t2 as(\n" +
"\tselect window_start,window_end,page_id,totalSum,row_number() over (partition by window_start,window_end order by totalSum desc) px from t1 \n" +
") select * from t2 where px <=3").print();
//4. sink-数据输出
//5. execute-执行
env.execute();
}
}