CREATE TABLE user_behavior (
user_id BIGINT,
item_id BIGINT,
category_id BIGINT,
behavior STRING,
ts TIMESTAMP(3),
proctime as PROCTIME(), – 处理时间列
WATERMARK FOR ts as ts - INTERVAL ‘5’ SECOND – 在ts上定义watermark，ts成为事件时间列
) WITH (
‘connector.type’ = ‘kafka’, – kafka connector
‘connector.version’ = ‘universal’, – universal 支持 0.11 以上的版本
‘connector.topic’ = ‘user_behavior’, – kafka topic
‘connector.startup-mode’ = ‘earliest-offset’, – 从起始 offset 开始读取
‘connector.properties.zookeeper.connect’ = ‘192.168.50.43:2181’, – zk 地址
‘connector.properties.bootstrap.servers’ = ‘192.168.50.43:9092’, – broker 地址
‘format.type’ = ‘json’ – 数据源格式为 json
);
- 执行SELECT * FROM user_behavior;看看原始数据，如果消息正常应该和下图类似：
窗口统计
- 下面的SQL是以每十分钟为窗口，统计每个窗口内的总浏览数，TUMBLE_START返回的数据格式是timestamp，这里再调用DATE_FORMAT函数将其格式化成了字符串：
SELECT DATE_FORMAT(TUMBLE_START(ts, INTERVAL ‘10’ MINUTE), ‘yyyy-MM-dd hh:mm:ss’),
DATE_FORMAT(TUMBLE_END(ts, INTERVAL ‘10’ MINUTE), ‘yyyy-MM-dd hh:mm:ss’),
COUNT(*)
FROM user_behavior
WHERE behavior = ‘pv’
GROUP BY TUMBLE(ts, INTERVAL ‘10’ MINUTE);
- 得到数据如下所示：
数据写入ElasticSearch
-
确保elasticsearch已部署好；
-
执行以下语句即可创建es表，请按照您自己的es信息调整下面的参数：
CREATE TABLE pv_per_minute (
start_time STRING,
end_time STRING,
pv_cnt BIGINT
) WITH (
‘connector.type’ = ‘elasticsearch’, – 类型
‘connector.version’ = ‘6’, – elasticsearch版本
‘connector.hosts’ = ‘http://192.168.133.173:9200’, – elasticsearch地址
‘connector.index’ = ‘pv_per_minute’, – 索引名，相当于数据库表名
‘connector.document-type’ = ‘user_behavior’, – type，相当于数据库库名
‘connector.bulk-flush.max-actions’ = ‘1’, – 每条数据都刷新
‘format.type’ = ‘json’, – 输出数据格式json
‘update-mode’ = ‘append’
);
- 执行以下语句，就会将每分钟的pv总数写入es的pv_per_minute索引：
INSERT INTO pv_per_minute
SELECT DATE_FORMAT(TUMBLE_START(ts, INTERVAL ‘1’ MINUTE), ‘yyyy-MM-dd hh:mm:ss’) AS start_time,
DATE_FORMAT(TUMBLE_END(ts, INTERVAL ‘1’ MINUTE), ‘yyyy-MM-dd hh:mm:ss’) AS end_time,
COUNT(*) AS pv_cnt
FROM user_behavior
WHERE behavior = ‘pv’
GROUP BY TUMBLE(ts, INTERVAL ‘1’ MINUTE);
- 用es-head查看，发现数据已成功写入：
联表操作
-
当前user_behavior表的category_id表示商品类目，例如11120表示计算机书籍，61626表示牛仔裤，本次实战的数据集中，这样的类目共有五千多种；
-
如果我们将这五千多种类目分成6个大类，例如11120属于教育类，61626属于服装类，那么应该有个大类和类目的关系表；
-
这个大类和类目的关系表在MySQL创建，表名叫category_info，建表语句如下：
CREATE TABLE category_info(
id int(11) unsigned NOT NULL AUTO_INCREMENT,
parent_id bigint ,
category_id bigint ,
PRIMARY KEY ( id )
) ENGINE=InnoDB AUTO_INCREMENT=5 DEFAULT CHARSET=utf8 COLLATE=utf8_bin;
-
表category_info所有数据来自对原始数据中category_id字段的提取，并且随机将它们划分为6个大类，该表的数据请在我的GitHub下载：https://raw.githubusercontent.com/zq2599/blog_demos/master/files/category_info.sql
-
请在MySQL上建表category_info，并将上述数据全部写进去；
-
在Flink SQL Client执行以下语句创建这个维表，mysql信息请按您自己配置调整：
CREATE TABLE category_info (
parent_id BIGINT, – 商品大类
category_id BIGINT – 商品详细类目
) WITH (
‘connector.type’ = ‘jdbc’,
‘connector.url’ = ‘jdbc:mysql://192.168.50.43:3306/flinkdemo’,
‘connector.table’ = ‘category_info’,
‘connector.driver’ = ‘com.mysql.jdbc.Driver’,
‘connector.username’ = ‘root’,
‘connector.password’ = ‘123456’,
‘connector.lookup.cache.max-rows’ = ‘5000’,
‘connector.lookup.cache.ttl’ = ‘10min’
);
- 尝试联表查询：
SELECT U.user_id, U.item_id, U.behavior, C.parent_id, C.category_id
FROM user_behavior AS U LEFT JOIN category_info FOR SYSTEM_TIME AS OF U.proctime AS C
ON U.category_id = C.category_id;
- 如下图，联表查询成功，每条记录都能对应大类：
- 再试试联表统计，每个大类的总浏览量：
SELECT C.parent_id, COUNT(*) AS pv_count
FROM user_behavior AS U LEFT JOIN category
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_info FOR SYSTEM_TIME AS OF U.proctime AS C
ON U.category_id = C.category_id
WHERE behavior = ‘pv’
GROUP BY C.parent_id;
- 如下图，数据是动态更新的：
- 执行以下语句，可以在统计时将大类ID转成中文名：
SELECT CASE C.parent_id
WHEN 1 THEN ‘服饰鞋包’
WHEN 2 THEN ‘家装家饰’
WHEN 3 THEN ‘家电’
WHEN 4 THEN ‘美妆’
WHEN 5 THEN ‘母婴’
WHEN 6 THEN ‘3C数码’
ELSE ‘其他’
END AS category_name,
COUNT(*) AS pv_count