StarRocks docker 环境编译,测试最新留存retention和漏斗window_funnel函数
名词解释
1.什么是留存分析？
留存分析是一种用来分析用户参与情况/活跃程度的分析模型，考查进行初始行为后的用户中，有多少人会进行后续行为。这是衡量产品对用户价值高低的重要指标。
2.什么是漏斗分析
漏斗分析主要是用于分析一个多步骤过程中每一步的转化和流失情况。
近期在做留存，漏斗等用户行为分析调研时，留存retention函数随着官网2.2 rc版本发布已经可以下载使用，然后漏斗函数window_funnel官方已经开发完成，合并到main版本，预计2.3版本会发布，现阶段通过编译，提前测试漏斗函数，以下为操作过程：
1.部署docker环境
mac环境下载docker.dmg，安装好，然后调整好docker资源（内存推荐8G以上）：
可以编译好后，把docker资源再调整回来，之间就因为docker资源不够，编译不过，各种被kill，报错：
be部分编译到一半，然后停止了，再拉起编译，报错：
此时需要 sh build.sh --be --clean
note:可以通过sh build.sh --help查看compile支持的选项，fe和be模块可以分开编译；
2.docker pull starrocks main编译环境
可以参考docs/Build_in_docker.md at main · StarRocks/docs · GitHub
docker pull starrocks/dev-env:main
docker run -it --name starrocks2.3 -d starrocks/dev-env:main进入到编译环境：
docker exec -it starrocks-compile-env-container-id /bin/bash3.编译
参考docs/Build_in_docker.md at main · StarRocks/docs · GitHub
git clone https://github.com/StarRocks/starrocks.git
cd starrocks
sh build.sh整个编译过程大概40-60mins
[100%] Built target starrocks_be
real 39m46.800s
user 168m35.045s
sys 10m22.659s编译好后，生成的starrocks版本会在output目录，配置fe，be配置，启动服务，添加backend节点；
查看builtin functions：
MySQL [(none)]> select current_version();
+-------------------+
| current_version() |
+-------------------+
| UNKNOWN ec27d13 |
+-------------------+
1 row in set (2.05 sec)
MySQL [(none)]>
MySQL [(none)]> create database gong;
Query OK, 0 rows affected (0.00 sec)
MySQL [(none)]> use gong;
Database changed
MySQL [gong]> show builtin functions from gong like '%retention%';
+---------------+
| Function Name |
+---------------+
| retention |
+---------------+
1 row in set (0.02 sec)
MySQL [gong]> show builtin functions from gong like '%window%';
+---------------+
| Function Name |
+---------------+
| window_funnel |
+---------------+
1 row in set (0.00 sec)4.测试
4.1 留存函数retention
准备数据：
mysql> select * from retention_test;
+------+---------------------+
| uid | date |
+------+---------------------+
| 4 | 2020-01-01 00:00:00 |
| 0 | 2020-01-01 00:00:00 |
| 1 | 2020-01-01 00:00:00 |
| 2 | 2020-01-01 00:00:00 |
| 3 | 2020-01-01 00:00:00 |
| 0 | 2020-01-02 00:00:00 |
| 1 | 2020-01-02 00:00:00 |
| 2 | 2020-01-02 00:00:00 |
| 3 | 2020-01-02 00:00:00 |
| 4 | 2020-01-02 00:00:00 |
| 5 | 2020-01-02 00:00:00 |
| 6 | 2020-01-02 00:00:00 |
| 7 | 2020-01-02 00:00:00 |
| 8 | 2020-01-02 00:00:00 |
| 9 | 2020-01-02 00:00:00 |
| 0 | 2020-01-03 00:00:00 |
| 1 | 2020-01-03 00:00:00 |
| 2 | 2020-01-03 00:00:00 |
| 3 | 2020-01-03 00:00:00 |
| 4 | 2020-01-03 00:00:00 |
| 5 | 2020-01-03 00:00:00 |
| 6 | 2020-01-03 00:00:00 |
| 7 | 2020-01-03 00:00:00 |
| 8 | 2020-01-03 00:00:00 |
| 9 | 2020-01-03 00:00:00 |
| 10 | 2020-01-03 00:00:00 |
| 11 | 2020-01-03 00:00:00 |
| 12 | 2020-01-03 00:00:00 |
| 13 | 2020-01-03 00:00:00 |
| 14 | 2020-01-03 00:00:00 |
+------+---------------------+
30 rows in set (0.01 sec)让我们考虑使用 retention 功能的一个例子 ，以确定网站流量。
按唯一ID uid 对用户进行分组，使用 retention 功能。
mysql> select uid,
retention([date='2020-01-01',date='2020-01-02',date='2020-01-03'])
from retention_test where date in('2020-01-01','2020-01-02','2020-01-03')
group by uid order by uid;
+------+----------------------------------------------------------------------------+
| uid | retention([date = '2020-01-01', date = '2020-01-02', date = '2020-01-03']) |
+------+----------------------------------------------------------------------------+
| 0 | [1,1,1] |
| 1 | [1,1,1] |
| 2 | [1,1,1] |
| 3 | [1,1,1] |
| 4 | [1,1,1] |
| 5 | [0,0,0] |
| 6 | [0,0,0] |
| 7 | [0,0,0] |
| 8 | [0,0,0] |
| 9 | [0,0,0] |
| 10 | [0,0,0] |
| 11 | [0,0,0] |
| 12 | [0,0,0] |
| 13 | [0,0,0] |
| 14 | [0,0,0] |
+------+----------------------------------------------------------------------------+
15 rows in set (0.03 sec)计算每天的现场访问总数：
mysql> SELECT sum(r[1]) AS r1,sum(r[2]) AS r2,sum(r[3]) AS r3
FROM (
SELECT uid,
retention([date = '2020-01-01', date = '2020-01-02', date = '2020-01-03']) As r
FROM retention_test WHERE date IN ('2020-01-01', '2020-01-02', '2020-01-03')
GROUP BY uid
) as rr;
+------+------+------+
| r1 | r2 | r3 |
+------+------+------+
| 5 | 5 | 5 |
+------+------+------+
1 row in set (0.39 sec)条件:
r1-2020-01-01期间访问该网站的独立访问者数量（cond1条件）。
r2-在2020-01-01和2020-01-02之间的特定时间段内访问该网站的唯一访问者的数量 (cond1和cond2条件）。
r3-在2020-01-01和2020-01-03之间的特定时间段内访问该网站的唯一访问者的数量 (cond1和cond3条件）。
4.2 window funnel函数测试：
window_funnel已经merge到主分支,还没正式release，参考：https://github.com/StarRocks/starrocks/pull/5542
4.2.1 Use case(datetime)
mysql> select * from action;
+------+------------+---------------------+
| uid | event_type | time |
+------+------------+---------------------+
| 1 | 浏览 | 2020-01-02 11:00:00 |
| 1 | 点击 | 2020-01-02 11:10:00 |
| 1 | 下单 | 2020-01-02 11:20:00 |
| 1 | 支付 | 2020-01-02 11:30:00 |
| 1 | 浏览 | 2020-01-02 11:00:00 |
| 2 | 下单 | 2020-01-02 11:00:00 |
| 2 | 支付 | 2020-01-02 11:10:00 |
| 3 | 浏览 | 2020-01-02 11:20:00 |
| 3 | 点击 | 2020-01-02 12:00:00 |
| 4 | 浏览 | 2020-01-02 11:50:00 |
| 4 | 点击 | 2020-01-02 12:00:00 |
| 5 | 浏览 | 2020-01-02 11:50:00 |
| 5 | 点击 | 2020-01-02 12:00:00 |
| 5 | 下单 | 2020-01-02 11:10:00 |
| 6 | 浏览 | 2020-01-02 11:50:00 |
| 6 | 点击 | 2020-01-02 12:00:00 |
| 6 | 下单 | 2020-01-02 12:10:00 |
+------+------------+---------------------+
17 rows in set (0.01 sec)
mysql> select uid, window_funnel(6000, time, 0, [event_type="浏览", event_type="点击",event_type="下单", event_type="支付"]) as level from action group by uid order by uid;
+------+-------+
| uid | level |
+------+-------+
| 1 | 4 |
| 2 | 0 |
| 3 | 2 |
| 4 | 2 |
| 5 | 2 |
| 6 | 3 |
+------+-------+
6 rows in set (0.04 sec)算出到了每个level的人数：
MySQL [gong]> select
-> level,
-> count(uid) as res
-> from
-> (
-> select
-> uid,
-> window_funnel(
-> 6000, time, 0, [event_type = "浏览",
-> event_type = "点击", event_type = "下单",
-> event_type = "支付" ]
-> ) as level
-> from
-> action
-> group by
-> uid
-> order by
-> uid
-> ) as t
-> group by
-> t.level;
+-------+------+
| level | res |
+-------+------+
| 3 | 1 |
| 2 | 3 |
| 4 | 1 |
| 0 | 1 |
+-------+------+
4 rows in set (0.02 sec)再结合开窗函数算出每个level留存的人数：
MySQL [gong]> with tmp as (
-> select
-> level,
-> count(uid) as res
-> from
-> (
-> select
-> uid,
-> window_funnel(
-> 6000, time, 0, [event_type = "浏览",
-> event_type = "点击", event_type = "下单",
-> event_type = "支付" ]
-> ) as level
-> from
-> action
-> group by
-> uid
-> order by
-> uid
-> ) as t
-> group by
-> t.level
-> )
-> select
-> tmp.level,
-> SUM(tmp.res) OVER(
-> ORDER BY
-> tmp.level ROWS BETWEEN CURRENT ROW
-> AND UNBOUNDED FOLLOWING
-> ) AS retention
-> from
-> tmp
-> order by
-> tmp.level;
+-------+-----------+
| level | retention |
+-------+-----------+
| 0 | 6 |
| 2 | 5 |
| 3 | 2 |
| 4 | 1 |
+-------+-----------+
4 rows in set (0.01 sec)有了每个level的留存人数后，就很容易算出漏斗转化了；
4.2.2 Use case(date)
mysql> select * from action9;
+------+------------+------------+
| uid | event_type | time |
+------+------------+------------+
| 1 | 浏览 | 2020-01-02 |
| 1 | 点击 | 2020-01-03 |
| 1 | 下单 | 2020-01-04 |
| 1 | 支付 | 2020-01-05 |
| 2 | 浏览 | 2020-01-02 |
| 2 | 点击 | 2020-01-06 |
+------+------------+------------+
6 rows in set (0.01 sec)
mysql> select uid, window_funnel(3, time, 0, [event_type="浏览", event_type="点击", event_type="下单", event_type="支付"]) as level from action9 group by uid order by uid;
+------+-------+
| uid | level |
+------+-------+
| 1 | 4 |
| 2 | 1 |
+------+-------+
2 rows in set (0.02 sec)