案例 MovieNow

一家虚构的在线影视租赁公司 MovieNow，它拥有电影信息（类型和主演）以及（用户信息，用户评分）。它需要决定：投资于哪些新电影？

BI（商业智能目标）

运营决策的数据
- 根据演员的流行度，决定投资哪一部电影。
- 根据上月的收入，辅助决定，短期的投资预算
策略决策的数据
- 在哪国更受欢迎，用来决定市场扩张
- 长线的投资及收入

KPI（关键绩效指标）

租赁总收入
用户满意度：所有电影的平均评分
活跃用户总数：用户参与度

OLAP（联机分析处理）

联机分析处理（英语：Online analytical processing），简称OLAP (/ˈoʊlæp/)，是计算机技术中快速解决多维分析问题（英语：multi-dimensional analytical）（MDA）的一种方法。^[1] OLAP是更广泛的商业智能范畴的一部分，它还包括关系数据库、报告编写和数据挖掘。^[2] OLAP的典型应用包括销售业务报告（英语：business reporting）、市场营销、管理报告、业务流程管理（BPM）^[3]、预算和预测、财务报表以及类似领域，新的应用正在出现，如农业。^[4] 术语“OLAP”是对传统数据库术语“联机事务处理”（OLTP）稍微修改而成的。^[5]

下面主要是上卷（roll-up）和切片（slicing）的例子。

上卷：汇集所有的电影租赁数据、用户评分，查询该租赁网站用户偏好的趋势。
切片：根据不同维度，国家、性别对演员的评分进行筛选查看。

ROLLUP运算符

SELECT 
    c.country, 
    m.genre, 
    AVG(r.rating) AS avg_rating, 
    COUNT(*) AS num_rating
FROM renting AS r
LEFT JOIN movies AS m
ON m.movie_id = r.movie_id
LEFT JOIN customers AS c
ON r.customer_id = c.customer_id
GROUP BY ROLLUP(c.country, m.genre)
ORDER BY c.country, m.genre;

按照租赁用户所属国家、电影的类型来查看平均评分及评分数目。

相比与 GOURP BY ， ROLLUP 包含了所有电影的平均评分，不同国家不限类型电影的平均评分，不同类型电影的平均评分。

CUBE运算符

SELECT 
    c.country, 
    m.genre, 
    AVG(r.rating) AS avg_rating 
FROM renting AS r
LEFT JOIN movies AS m
ON m.movie_id = r.movie_id
LEFT JOIN customers AS c
ON r.customer_id = c.customer_id
GROUP BY CUBE(country, genre);

按照租赁用户所属国家、电影的类型来查看平均评分及评分数目。

ROLLUP 结果是 88条结果，CUBE 得到 96 条结果，多出来的结果，是因为 CUBE 生成所选列的所有合计组合。而 ROLLUP 是生成所选列的层级聚合结果。

ROLLUP (YEAR, MONTH, DAY)

With a ROLLUP, it will have the following outputs:

YEAR, MONTH, DAY
YEAR, MONTH
YEAR
()
With CUBE, it will have the following:

YEAR, MONTH, DAY
YEAR, MONTH
YEAR, DAY
YEAR
MONTH, DAY
MONTH
DAY
()

GROUPING SET运算符

SELECT 
    nationality, 
    gender, 
    COUNT(*) 
FROM actors
GROUP BY GROUPING SETS ((nationality), (gender), ());

GROUP SET 最灵活

可以返回不同的 GROUP BY 检索的集合

完整BI例子

问题: 投资哪些新电影？需要考虑，新电影比旧电影更贵。

用户对新旧电影的评分差异？
不同国家用户之间，对新旧电影的评分，有差异吗？

表联结(Join data)

联结三张表，分别是 renting/customers/movies

SELECT *
FROM renting AS r
LEFT JOIN customers AS c
ON c.customer_id = r.customer_id
LEFT JOIN movies AS m
ON m.movie_id = r.movie_id;

选择相关记录(Select relevant records)

SELECT *
FROM renting AS r
LEFT JOIN customers AS c
ON c.customer_id = r.customer_id
LEFT JOIN movies AS m
ON m.movie_id = r.movie_id
WHERE r.movie_id IN (
SELECT movie_id
FROM renting
GROUP BY movie_id
HAVING COUNT(rating) >= 4)
AND r.date_renting >= '2018-04-01';

聚集数据（Aggregation）

SELECT c.country,
m.year_of_release,
COUNT(*) AS n_rentals,
COUNT(DISTINCT r.movie_id) AS n_movies,
AVG(rating) AS avg_rating
FROM renting AS r
LEFT JOIN customers AS c
ON c.customer_id = r.customer_id
LEFT JOIN movies AS m
ON m.movie_id = r.movie_id
WHERE r.movie_id IN (
SELECT movie_id
FROM renting
GROUP BY movie_id
HAVING COUNT(rating) >= 4)
AND r.date_renting >= '2018-04-01'
GROUP BY ROLLUP (m.year_of_release, c.country)
ORDER BY c.country, m.year_of_release;

按照不同的年份、国家来查看租赁次数、电影总数和平均评分

SELECT genre,
       AVG(rating) AS avg_rating,
       COUNT(rating) AS n_rating,
       COUNT(*) AS n_rentals,     
       COUNT(DISTINCT m.movie_id) AS n_movies 
FROM renting AS r
LEFT JOIN movies AS m
ON m.movie_id = r.movie_id
WHERE r.movie_id IN ( 
    SELECT movie_id
    FROM renting
    GROUP BY movie_id
    HAVING COUNT(rating) >= 3 )
AND r.date_renting >= '2018-01-01'
GROUP BY genre
ORDER BY avg_rating DESC;

筛选条件是 2018 年以后的租赁记录，评分超过 3 次的电影。
根据类型 genre 分组，按照平均评分降序排序

SELECT a.nationality,
       a.gender,
       AVG(r.rating) AS avg_rating,
       COUNT(r.rating) AS n_rating,
       COUNT(*) AS n_rentals,
       COUNT(DISTINCT a.actor_id) AS n_actors
FROM renting AS r
LEFT JOIN actsin AS ai
ON ai.movie_id = r.movie_id
LEFT JOIN actors AS a
ON ai.actor_id = a.actor_id
WHERE r.movie_id IN ( 
    SELECT movie_id
    FROM renting
    GROUP BY movie_id
    HAVING COUNT(rating) >= 4)
AND r.date_renting >= '2018-04-01'
GROUP BY CUBE(nationality, gender);

哪些国家/性别的演员更受欢迎？
根据国籍、性别，查看演员参演的电影之中，所获得的平均评分、租赁字数、演员数目等等。

参考资料：

DataCamp
SQL中ROLLUP、CUBE的用法和区别
understanding-the-differences-between-cube-and-rollup

SQL-KPI的线上分析处理(OLAP)