Chapter6 SQL

Sequential Query Language

基本解题思路

Metrics, what does the result look like
需要什么数据
怎么获取数据
有了数据怎么计算（avg，count）

面试

电话面试：“2张表格” 借助草稿纸，口头说
share doc，copy paste问题，规定时间内写SELECT FROM 实时看写语句的顺序、停顿的位置...
On-Site：白板

强推postgre SQL （相比较Oracle SQL/MySQL... ）

非关系型数据库 HIVE Query也和SQL差不多

Cheatsheet

执行顺序 ROM-->WHERE-->GROUP BY-->HAVING-->SELECT-->ORDER BY

Group By 1 可以当做一种简写，指的group by select语句中的第一列同理，ORDER BY 2 也是一种简写
HAVING是在select之后才操作的，所以Having里有Select里用到的aggregation function
UNION，连接的是两个SELECT语句，需要SELECT的column名字一样，数量也一样，默认的是unique values，所以假如有两个row一模一样，它会合并掉；如果想保留，用UNION ALL

SELECT FROM 
    CASE WHEN THEN
        WHEN THEN
        ELSE END
        AS 
FROM AS
INNER JOIN AS
ON
WHERE IN       / WHERE BETWEEN AND
GROUP BY
ORDER BY

UNION, INTERSECT, EXCEPT

SELECT AS
FROM
UNION
SELECT
FROM
ORDER BY

subqueries:

SELECT
FROM
WHERE LIKE
    (SELECT
    FROM
    WHERE )

SELECT
FROM
WHERE 
    AND (SELECT AVG()
    FROM )

SELECT DISTINCT
    (SELECT COUNT (*)
    FROM
    WHERE) AS 
FROM

Window Function, row-number() over (partition by X order by Y) 这里容易犯的错误是partition by后面写很多column

SELECT category, product, sales
FROM (
    SELECT category, product, 
    row_number() OVER (PARTITION BY category, order by sales DESC) as row_num
    )
WHERE row_num<=2

Window Function, rank() over (partition by X oder by Y) row_number, rank, dense_rank的意义都是排序，但是区别是row_number给出单纯的行号，两种rank都给出排序后的号，两个相同数值给一样的rank；区别是rank的5、6名是同一个数字，则第七名标7，可是dense rank的第七名会标6，顺着之前的数字来。

Window Function, sum() over () 得到的是cumulative sum

Case When除了最上面的基本用法之外，更常用的是结合aggregation function使用。如果多个Case是同样的条件，那么只执行第一个条件。 +aggregation_function 比如：

SUM(CASE WHEN ... THEN ... ELSE ... END) AS ...
COUNT(DISTINCT(CASE WHEN ... THEN... ELSE ... END)) AS ...

# Eg. count total impressions and how many 'top' clicks for all campaigns, all impressions

SELECT ads_campaign, 
        COUNT(impression_time) as impressions, 
        COUNT(CASE WHEN click_time is not null then click_time END) as top_clicks
FROM table
Group by 1

另外window function还可以用来算一堆数据中的median，因为median具有一个特性：它的row_number的编号处在正中间，那么就可以通过从小到大排、从大到小排。如果是奇数，那么这两种排序后相等的那个就是中间的了，如果是偶数，会有两个index，分别是对方的+-1；不管如何，都可以对这部分匹配的。

row_number over (partition by group by) as row_num WHERE row_num=INT(COUNT(*)/2)

最后SQL也可以用来generate distribution或者bucketization。

t_pages

page_id

product_id

t_users

user_id

page_id

timestamp

# Q1: count unique users for product view for a given time
SELECT a.page_id, COUNT(DISTINCT b.user_id) as unique_users
FROM t_pages a
LEFT JOIN t_users b 
ON a.page_id=b.page_id
WHERE cast(timestamp as date) between AND 
#cast用来做数值类型的转换

# Q2：Product view distribution. eg. for product A, 10000 viewers saw it once, 22 twice

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