eventTime
测试数据如下：
{"username":"zs","price":20,"event_time":"2023-07-17 10:10:10"}
{"username":"zs","price":15,"event_time":"2023-07-17 10:10:30"}
{"username":"zs","price":20,"event_time":"2023-07-17 10:10:40"}
{"username":"zs","price":20,"event_time":"2023-07-17 10:11:03"}
{"username":"zs","price":20,"event_time":"2023-07-17 10:11:04"}
{"username":"zs","price":20,"event_time":"2023-07-17 10:12:04"}
{"username":"zs","price":20,"event_time":"2023-07-17 11:12:04"}
{"username":"zs","price":20,"event_time":"2023-07-17 11:12:04"}
{"username":"zs","price":20,"event_time":"2023-07-17 12:12:04"}
{"username":"zs","price":20,"event_time":"2023-07-18 12:12:04"}需求：每隔1分钟统计这1分钟的每个用户的总消费金额和消费次数
需要用到滚动窗口
编写好sql:
CREATE TABLE table1 (`username` string,`price` int,`event_time` TIMESTAMP(3),watermark for event_time as event_time - interval '3' second
) WITH ('connector' = 'kafka','topic' = 'topic1','properties.bootstrap.servers' = 'bigdata01:9092','properties.group.id' = 'g1','scan.startup.mode' = 'latest-offset','format' = 'json'
);编写sql：
select window_start,window_end,username,count(1) zongNum,sum(price) totalMoney from table(TUMBLE(TABLE table1, DESCRIPTOR(event_time), INTERVAL '60' second))
group by window_start,window_end,username;分享一个错误：
Exception in thread "main" org.apache.flink.table.api.ValidationException: SQL validation failed. The window function TUMBLE(TABLE table_name, DESCRIPTOR(timecol), datetime interval) requires the timecol is a time attribute type, but is VARCHAR(2147483647).
at org.apache.flink.table.planner.calcite.FlinkPlannerImpl.org$apache$flink$table$planner$calcite$FlinkPlannerImpl$$validate(FlinkPlannerImpl.scala:156)
at org.apache.flink.table.planner.calcite.FlinkPlannerImpl.validate(FlinkPlannerImpl.scala:107)
说明创建窗口的时候，使用的字段不是时间字段，需要写成时间字段TIMESTAMP(3)，使用了eventtime需要添加水印，否则报错。
需求：按照滚动窗口和EventTime进行统计，每隔1分钟统计每个人的消费总额是多少
package com.bigdata.day08;import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;/*** @基本功能:* @program:FlinkDemo* @author: 闫哥* @create:2023-11-28 14:12:28**/
public class _03EventTimeGunDongWindowDemo {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',\n" +" 'properties.group.id' = 'testGroup1',\n" +" 'scan.startup.mode' = 'group-offsets',\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();}
}统计结果如下：
测试一下滑动窗口，每隔10秒钟，计算前1分钟的数据：
package com.bigdata.day08;import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;/*** @基本功能:* @program:FlinkDemo* @author: 闫哥* @create:2023-11-28 14:12:28**/
public class _03EventTimeGunDongWindowDemo {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',\n" +" 'properties.group.id' = 'testGroup1',\n" +" 'scan.startup.mode' = 'group-offsets',\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();}
}结果如图所示：
package com.bigdata.day08;import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;/*** @基本功能:* @program:FlinkDemo* @author: 闫哥* @create:2023-11-28 14:12:28**/
public class _03EventTimeGunDongWindowDemo {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',\n" +" 'properties.group.id' = 'testGroup1',\n" +" 'scan.startup.mode' = 'group-offsets',\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 '1' hours,INTERVAL '1' days))\n" +"group by window_start,window_end,username").print();//4. sink-数据输出//5. execute-执行env.execute();}
}累积窗口演示效果：
processTime
测试数据：
{"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}/*** 滚动窗口大小1分钟 延迟时间3秒** {"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}**/
package com.bigdata.day08;import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;/*** @基本功能:* @program:FlinkDemo* @author: 闫哥* @create:2023-11-28 14:12:28**/
public class _04ProcessingTimeGunDongWindowDemo {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',\n" +" 'properties.group.id' = 'testGroup1',\n" +" 'scan.startup.mode' = 'group-offsets',\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();}
}计算结果：
结果需要等1分钟，才能显示出来，不要着急！
窗口分为滚动和滑动，时间分为事件时间和处理时间，两两组合，4个案例。
以下是滑动窗口+处理时间：
package com.bigdata.sql;import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;/*** @基本功能:* @program:FlinkDemo* @author: 闫哥* @create:2024-11-29 14:28:19**/
public class _04_FlinkSQLProcessTime_HOP {public static void main(String[] args) throws Exception {//1. env-准备环境StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);// 获取tableEnv对象// 通过env 获取一个table 环境StreamTableEnvironment tEnv = StreamTableEnvironment.create(env);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',\n" +" 'properties.group.id' = 'g1',\n" +" 'scan.startup.mode' = 'latest-offset',\n" +" 'format' = 'json'\n" +")");// 语句中的 ; 不能添加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();//5. execute-执行env.execute();}
}
测试时假如你的控制台不出数据，触发不了，请进入如下操作：
1、重新创建一个新的 topic，分区数为 1
2、kafka 对接的 server，写全 bigdata01:9092,bigdata02:9092,bigdata03:9092
二、窗口TopN(不是新的技术)
需求：在每个小时内找出点击量最多的Top 3网页。
测试数据
{"ts": "2023-09-05 12:00:00", "page_id": 1, "clicks": 100}
{"ts": "2023-09-05 12:01:00", "page_id": 2, "clicks": 90}
{"ts": "2023-09-05 12:10:00", "page_id": 3, "clicks": 110}
{"ts": "2023-09-05 12:20:00", "page_id": 4, "clicks": 23}
{"ts": "2023-09-05 12:30:00", "page_id": 5, "clicks": 456}
{"ts": "2023-09-05 13:10:00", "page_id": 5, "clicks": 456}假如没有每隔1小时的需求，仅仅是统计点击量最多的Top 3网页，结果如下
select * from (
select page_id,totalSum, row_number() over (order by totalSum desc) pxfrom (select page_id,sum(clicks) totalSumfrom kafka_page_clicks group by page_id ) ) where px <=3;根据以上代码，添加滚动窗口的写法：
select window_start,window_end,page_id,sum(clicks) totalSum from table ( tumble( table kafka_page_clicks, descriptor(ts), INTERVAL '1' HOUR ) ) group by window_start,window_end,page_id;在这个基础之上添加排名的写法：
select window_start,window_end,page_id,pmfrom (
select window_start,window_end,page_id,row_number() over(partition by window_start,window_end order by totalSum desc ) pmfrom (
select window_start,window_end,page_id,sum(clicks) totalSum from table ( tumble( table kafka_page_clicks, descriptor(ts), INTERVAL '1' HOUR ) ) group by window_start,window_end,page_id ) t2 ) t1 where pm <= 3;编写建表语句：
{"ts": "2023-09-05 12:00:00", "page_id": 1, "clicks": 100}CREATE TABLE kafka_page_clicks (`ts` TIMESTAMP(3),`page_id` int,`clicks` int,watermark for ts as ts - interval '3' second
) WITH ('connector' = 'kafka','topic' = 'topic1','properties.bootstrap.servers' = 'bigdata01:9092','properties.group.id' = 'g1','scan.startup.mode' = 'latest-offset','format' = 'json'
)package com.bigdata.day08;import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;/*** @基本功能:* @program:FlinkDemo* @author: 闫哥* @create:2023-11-28 15:23:46**/
public class _05TopNDemo {public static void main(String[] args) throws Exception {//1. env-准备环境StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();// ctrl + y 删除光标所在的那一行数据 ctrl + d 复制当前行StreamTableEnvironment tenv = StreamTableEnvironment.create(env);//2. source-加载数据// 一定要注意：ts 是一个年月日时分秒的数据，所以在建表时一定要是TIMESTAMP，否则进行WATERMARK 报错// 因为使用的是event_time 所以，需要指定WATERMARKtenv.executeSql("CREATE TABLE kafka_page_clicks (" +" `ts` TIMESTAMP(3),\n" +" page_id INT,\n" +" clicks INT,\n" +" WATERMARK FOR ts AS ts - INTERVAL '10' SECOND \n" +") WITH (\n" +" 'connector' = 'kafka',\n" +" 'topic' = 'topic1',\n" +" 'properties.bootstrap.servers' = 'bigdata01:9092',\n" +" 'scan.startup.mode' = 'group-offsets',\n" +" 'format' = 'json'\n" +")");tenv.executeSql("select \n" +" window_start,\n" +" window_end,\n" +" page_id,\n" +" pm\n" +" from (\n" +"select \n" +" window_start,\n" +" window_end,\n" +" page_id,\n" +" row_number() over(partition by window_start,window_end order by totalSum desc ) pm\n" +" from (\n" +"select \n" +" window_start,\n" +" window_end,\n" +" page_id,\n" +" sum(clicks) totalSum \n" +" from \n" +" table ( \n" +" tumble( table kafka_page_clicks, descriptor(ts), INTERVAL '1' HOUR ) \n" +" ) \n" +" group by window_start,window_end,page_id ) t2 ) t1 where pm <= 3").print();//4. sink-数据输出//5. execute-执行env.execute();}
}最后的运行结果如下：
