Flink SQL结合Kafka、Elasticsearch、Kibana实时分析电商用户行为

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使用Flink SQL结合Kafka、Elasticsearch、Kibana实时分析电商用户行为 (Use flink sql to combine kafka, elasticsearch and kibana, real-time analysis of e-commerce user behavior.)

Flink与其它实时计算工具区别之一是向用户提供了更多抽象易用的API，比如读写各类程序的connector接口、Table API和SQL，从数据加载、计算、一直到输出，所有操作都可以使用SQL完成，大大减少了开发量和维护成本，本文将通过实时分析电商用户行为数据介绍flink sql的使用，分析的内容如下：

分析每10分钟累计在线用户数；
分析每小时购买量；
分析top浏览商品类目(浏览的商品归属于那个类目)；

1 最终实时分析kibana展现效果

2 流程和版本信息

kafka --> flink --> es -->kibana

数据采集存储到kafka，通过flink消费kafka数据，实时计算，结果存储到es，最后通过kibana展现。

版本信息

flink 1.12.1、kafka_2.13-2.7.0、elasticsearch 7.10.1、kibana 7.10.1

3 数据结构

电商用户行为分析共涉及3个表，商品类目信息表、商品类目信息表、用户行为信息表，其中用户行为信息表共5个列：用户ID、商品ID、商品类目ID、行为类型、时间戳；

4 kafka数据

./kafka-console-consumer.sh --topic user_behavior --bootstrap-server kafka:9092 --from-beginning --max-messages 5

1,2268318,2520377,pv,1511544070

1,2333346,2520771,pv,1511561733

数据来源于淘宝开放的用户行为数据UserBehavior，数据格式为csv，以逗号分隔；

2 使用Flink SQL建表读取kafka数据

现在数据已经存储在kafka，进入flink sql client，

创建消费kafka数据表；

CREATE TABLE user_behavior (

    user_id BIGINT,

    item_id BIGINT,

    category_id BIGINT,

    behavior STRING,

    app_time BIGINT,

    ts AS TO_TIMESTAMP(FROM_UNIXTIME(app_time, 'yyyy-MM-dd HH:mm:ss')),

    proctime AS PROCTIME(),

    WATERMARK FOR ts AS ts - INTERVAL '5' SECOND

) WITH (

    'connector' = 'kafka',  --使用kafka connector

    'topic' = 'user_behavior',  --kafka topic

    'scan.startup.mode' = 'earliest-offset',  --从topic最开始处开始消费

    'properties.bootstrap.servers'='localhost:9092',  --kafka broker地址

    'properties.group.id' = 'test-group03',

    'format' = 'csv',  --存储在kafka的数据格式为csv

    'csv.field-delimiter'=',' --数据分隔符

);

WATERMARK 定义处理混乱次序的事件时间属性，每5秒触发一次window
PROCTIME 是内置函数，产生一个虚拟的Processing Time列，偶尔会用到
WITH 里定义kafka连接信息和属性
由于事件时间格式为bigint,在sql中将其转为timestamp

3 分析场景

3.1 场景1：分析每10分钟累计在线用户数

最终的分析结果数据会写入es，首先创建es index和写入es的表；

CREATE TABLE cumulative_uv (

    date_str STRING,

    time_str STRING,

    uv BIGINT,

    PRIMARY KEY (date_str, time_str) NOT ENFORCED

) WITH (

    'connector' = 'elasticsearch-7',

    'hosts' = 'http://localhost:9200',

    'index' = 'cumulative_uv'

);

WITH 里面定义es连接信息和属性

分析每10分钟在线用户数只需要知道日期(date_str)、时间(time_str)、数量(uv)即可；上面已经定义了消费kafka数据的表 user_behavior，现在查询该表，并将数据写入es；

INSERT INTO

    cumulative_uv

SELECT

    date_str, MAX(time_str), COUNT(DISTINCT user_id) as uv

FROM (

  SELECT

    DATE_FORMAT(ts, 'yyyy-MM-dd') as date_str,

    SUBSTR(DATE_FORMAT(ts, 'HH:mm'),1,4) || '0' as time_str,

    user_id

  FROM user_behavior)

GROUP BY

    date_str;

由于分析跨度为每10分钟，在sql 内层查询中使用 SUBSTR 截取事件小时和分钟字符，拼凑成每10分钟的数据，比如: 12:10，12:20。提交sql后，flink会将sql以流作业方式按照设定的WATERMARK和窗口提交到集群运行；

现在查询kibina可以看到数据已经实时写入.

3.2 场景2：分析每小时购买量

创建es index和写入es的表；

CREATE TABLE buy_cnt_per_hour (

    hour_of_day BIGINT,

    buy_cnt BIGINT

) WITH (

    'connector' = 'elasticsearch-7',

    'hosts' = 'http://localhost:9200',

    'index' = 'buy_cnt_per_hour'

);

查询 user_behavior 表，将数据写入es；

INSERT INTO

    buy_cnt_per_hour

SELECT

    HOUR(TUMBLE_START(ts, INTERVAL '1' HOUR)), COUNT(*)

FROM

    user_behavior

WHERE

    behavior='buy'

GROUP BY

    TUMBLE(ts, INTERVAL '1' HOUR);

HOUR 为内置函数，从一个 TIMESTAMP 列中提取出一天中第几个小时的值
TUMBLE 为窗口函数，按设定的时间切窗

首先通过(behavior='buy') 过滤出购买数据，再通过窗口函数(TUMBLE)按一小时切窗，统计出每小时共有多少"buy"的用户行为。

3.3 场景3：分析top浏览商品类目

由于kafka数据存储的是商品id，商品id对应的商品类目名称存储在mysql数据库，需先创建连接mysql的数据表；

CREATE TABLE category (

    category_id BIGINT,

    category_name STRING

) WITH (

    'connector' = 'jdbc',

    'url' = 'jdbc:mysql://localhost:3306/test',

    'table-name' = 'category',

    'username' = 'sywu',

    'password' = 'sywu',

    'lookup.cache.max-rows' = '5000',

    'lookup.cache.ttl' = '10min'

);

为了后续查询方便，创建kafka数据表和mysql数据表关联视图；

CREATE VIEW rich_user_behavior

AS

SELECT

    U.user_id, U.item_id, U.behavior, case when C.category_name is null then 'other' else C.category_name end as category_name

FROM

    user_behavior AS U LEFT JOIN category FOR SYSTEM_TIME AS OF U.proctime AS C

ON

    U.category_id = C.category_id;

现在 kafka 数据表和 mysql数据表通过视图表 rich_user_behavior 关联在一起；分析top浏览商品类目只需要知道商品类目名和浏览数即可，所以在此创建一张包含商品类目名和浏览数的表；

CREATE TABLE top_category (

    category_name STRING PRIMARY KEY NOT ENFORCED,

    buy_cnt BIGINT

) WITH (

    'connector' = 'elasticsearch-7',

    'hosts' = 'http://localhost:9200',

    'index' = 'top_category'

);

查询视图表 rich_user_behavior表，过滤分组统计数据；

INSERT INTO

    top_category

SELECT

    category_name, COUNT(*) buy_cnt

FROM

    rich_user_behavior

WHERE

    behavior='buy'

GROUP BY

    category_name;

到此3个分析需求实现，作业正常实时运行。

4 总结

通过Flink 提供的Table API和SQL，以及处理数据的窗口、读写各类程序的connector接口和函数，使用上面的SQL DML操作，flink即实现了用户行为数据的实时分析需求；从开发角度看，代码量和开发难度大大降低；从维护角度看，维护成本也大大降低。

参考文献

https://ci.apache.org/projects/flink/flink-docs-release-1.12/dev/table/ - Table API & SQL
https://ci.apache.org/projects/flink/flink-docs-release-1.11/dev/table/sql/queries.html#group-windows - flink group windows
https://tianchi.aliyun.com/dataset/dataDetail?dataId=649 - taobao UserBehavior