一、需求描述
每隔30min 统计最近 1hour的热门商品 top3, 并把统计的结果写入到mysql中。
二、需求分析
- 1.统计每个商品的点击量, 开窗
- 2.分组窗口分组
- 3.over窗口
三、需求实现
3.1、创建数据源示例
input/UserBehavior.csv
543462,1715,1464116,pv,1511658000
662867,2244074,1575622,pv,1511658000
561558,3611281,965809,pv,1511658000
894923,3076029,1879194,pv,1511658000
834377,4541270,3738615,pv,1511658000
315321,942195,4339722,pv,1511658000
625915,1162383,570735,pv,1511658000
578814,176722,982926,pv,1511658000
873335,1256540,1451783,pv,1511658000
429984,4625350,2355072,pv,1511658000
866796,534083,4203730,pv,1511658000
937166,321683,2355072,pv,1511658000
156905,2901727,3001296,pv,1511658000
758810,5109495,1575622,pv,1511658000
107304,111477,4173315,pv,1511658000
452437,3255022,5099474,pv,1511658000
813974,1332724,2520771,buy,1511658000
524395,3887779,2366905,pv,1511658000
3.2、创建目标表
CREATE DATABASE flink_sql; //创建flink_sql库
USE flink_sql;
DROP TABLE IF EXISTS `hot_item`;
CREATE TABLE `hot_item` (`w_end` timestamp NOT NULL,`item_id` bigint(20) NOT NULL,`item_count` bigint(20) NOT NULL,`rk` bigint(20) NOT NULL,PRIMARY KEY (`w_end`,`rk`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8;
3.3、导入JDBC Connector依赖
<!-- 导入JDBC Connector依赖 --><dependency><groupId>org.apache.flink</groupId><artifactId>flink-connector-jdbc_${scala.binary.version}</artifactId><version>${flink.version}</version></dependency>
3.4、代码实现
package com.atguigu.flink.java.chapter_12;import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;/*** @Author lizhenchao@atguigu.cn* @Date 2021/1/31 9:11*/
public class Flink01_HotItem_TopN {public static void main(String[] args) {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();env.setParallelism(2);StreamTableEnvironment tenv = StreamTableEnvironment.create(env);// 使用sql从文件读取数据tenv.executeSql("create table user_behavior(" +" user_id bigint, " +" item_id bigint, " +" category_id int, " +" behavior string, " +" ts bigint, " +" event_time as to_timestamp(from_unixtime(ts, 'yyyy-MM-dd HH:mm:ss')), " +" watermark for event_time as event_time - interval '5' second " +")with(" +" 'connector'='filesystem', " +" 'path'='input/UserBehavior.csv', " +" 'format'='csv')");// 每隔 10m 统计一次最近 1h 的热门商品 top// 1. 计算每每个窗口内每个商品的点击量Table t1 = tenv.sqlQuery("select " +" item_id, " +" hop_end(event_time, interval '10' minute, interval '1' hour) w_end," +" count(*) item_count " +"from user_behavior " +"where behavior='pv' " +"group by hop(event_time, interval '10' minute, interval '1' hour), item_id");tenv.createTemporaryView("t1", t1);// 2. 按照窗口开窗, 对商品点击量进行排名Table t2 = tenv.sqlQuery("select " +" *," +" row_number() over(partition by w_end order by item_count desc) rk " +"from t1");tenv.createTemporaryView("t2", t2);// 3. 取 top3Table t3 = tenv.sqlQuery("select " +" item_id, w_end, item_count, rk " +"from t2 " +"where rk<=3");// 4. 数据写入到mysql// 4.1 创建输出表tenv.executeSql("create table hot_item(" +" item_id bigint, " +" w_end timestamp(3), " +" item_count bigint, " +" rk bigint, " +" PRIMARY KEY (w_end, rk) NOT ENFORCED)" +"with(" +" 'connector' = 'jdbc', " +" 'url' = 'jdbc:mysql://hadoop162:3306/flink_sql?useSSL=false', " +" 'table-name' = 'hot_item', " +" 'username' = 'root', " +" 'password' = 'aaaaaa' " +")");// 4.2 写入到输出表t3.executeInsert("hot_item");}
}
执行结果：
四、总结
Flink 使用 OVER 窗口条件和过滤条件相结合以进行 Top-N 查询。利用 OVER 窗口的 PARTITION BY 子句的功能，Flink 还支持逐组 Top-N 。 例如，每个类别中实时销量最高的前五种产品。批处理表和流处理表都支持基于SQL的 Top-N 查询。
流处理模式需注意: TopN 查询的结果会带有更新。 Flink SQL 会根据排序键对输入的流进行排序；若 top N 的记录发生了变化，变化的部分会以撤销、更新记录的形式发送到下游。 推荐使用一个支持更新的存储作为 Top-N 查询的 sink 。另外，若 top N 记录需要存储到外部存储，则结果表需要拥有与 Top-N 查询相同的唯一键。
