大数据-玩转数据-Flink SQL编程实战 (热门商品TOP N)

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
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18

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;
1
2
3
4
5
6
7
8
9
10

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>
1
2
3
4
5
6

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 top3
        Table 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");
    }
}
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83

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。