用简单程序协助 MySQL 实现窗口函数

窗口函数是 SQL2003 标准才开始有的一系列 SQL 函数,用于应付一些复杂运算是比较方便。但是普遍使用的 MySQL 数据库对窗口函数支持得却很不好,直到最近的版本才开始有部分支持,这当然就让 MySQL 程序员很郁闷了。

实际操作中,我们可以在 MySQL 里用 SQL 拼出窗口函数功能,但是需要使用用户变量以及多个 SELECT 表达式从左到右依次计算的隐含规则。下面我们来看两个例子(为调试方便,我们直接用集算器作为测试环境)。

1、2016 年 1 月销售额排名


A

1

set @i1=0, @i2=0, @d1=null;

2

select @i1:=@i1+1 `row_number`, province, curr_sales, prev_sales,

      @i2:=if(prev_sales=curr_sales,@i2,@i1) `rank`

from (select province,  cast(@d1 as decimal(15,2)) as prev_sales,

          @d1:=sales as curr_sales

     from detail

   where yearmonth=201601

   order by sales desc

   ) t1;

3

=connect("mysql")

4

>A3.execute(A1)

5

=A3.query@x(A2)


(1)A1 中语句用于初始化用户变量;

(2)A2 中语句先对销售额排倒序,然后每一行销售额与上一行销售额比较,若相等则排名不变,否则排名等于行号;

(3)A3 连接数据库;

(4)A4 执行初始化语句;

(5)A5 执行查询语句并关闭数据库连接,返回结果。

执行后 A5 为需要的结果。

2、2016 年 1 月和 2 月销售额按月分组百分比排名


A

1

set @i1=null, @i2=0, @i3=0, @d1=null;

2

select curr_month, t1.province, curr_sales, sale_rank,

if(count>1, (sale_rank-1)/(count-1), 0) as `percent_rank`

from (select prev_month, curr_month, province,

@i2:=if(prev_month=curr_month,@i2+1,1) as `row_number`,

@i3:=if(prev_month<>curr_month, 1, if(prev_sales=curr_sales,

 @i3, @i2)) as 'sale_rank', prev_sales, curr_sales

from (select @i1 as prev_month, @i1:=yearmonth as curr_month,

       province, @d1 as prev_sales, @d1:=sales as curr_sales

from (select *

from detail

where yearmonth in (201601,201602)

order by yearmonth, sales desc

) t111

) t11

     ) t1

     join

     (select yearmonth, count(*) count

     from detail

     where yearmonth in (201601, 201602)

     group by yearmonth

     ) t2

     on t1.curr_month=t2.yearmonth;

3

=connect("mysql")

4

>A3.execute(A1)

5

=A3.query@x(A2)


(1)A1 中语句用于初始化用户变量;

(2)A2 中语句子查询 t11 求出上一行的月份和销售额,t1 再求出本月行号与排名,t2 算出每月的行数,最后 t1 与 t2 连接再利用公式 [if(本月行数>1,(当前行的本月排名 -1)/(本组行数 -1),0)] 求出百分比排号。

执行后 A5 为需要的结果。

通过上述两个例子,我们可以看到,为了实现窗口函数相应功能,SQL 语句冗长、复杂而且可读性较差。另外,这里还使用了 SELECT 表达式从左到右依次计算的隐含规则,而这在 MySQL 参考手册是不推荐使用的,如果今后不能使用这一规则,那么写出来的 SQL 语句会更加复杂。譬如不使用这条隐含规则如何能取上一行的字段值呢?各位读者可以自行脑补。

值得庆幸的是,有了集算器及其特有的 SPL 语言,我们就大可不必这么麻烦了,MySQL 只要使用最基本的 SQL 就行了,剩下的事由集算器来完成。

下面我们就来看看集算器的 SPL 语法是如何实现相应窗口函数的功能的。

1、SUM()、COUNT()、AVG()、MAX()、MIN()、VARIANCE

a)

select province, sales, sum(sales) over() `sum`,
       avg(sales) over() `avg`, max(sales) over() `max`,
       min(sales) over() `min`, count(*) over() `count`
from detail
where yearmonth=201601
order by sales;

A

1

=connect("mysql")

2

=A1.query@x("select * from detail where yearmonth=201601 order by sales desc")

3

=A2.sum(sales)

4

=A2.avg(sales)

5

=A2.max(sales)

6

=A2.min(sales)

7

=A2.count()

8

=A2.new(province, sales, A3:sum, A4:avg,A5:max,A6:min, A7:count)


(1)A3 到 A7 依次对销售额求和、求平均、求最大、求最小及求总行数;

(2)A8 构造序表,其中每一行都有本月销售额总和、平均值、最大值、最小值及总行数

执行后 A8 的结果如下:

这个例子很常规,毫无挑战性,只是小练一把,下面开始玩真的。

b)

select yearmonth,province,sales,
       sum(sales) over (partition by yearmonth) `sum`,
       avg(sales) over (partition by yearmonth) `avg`,
       max(sales) over (partition by yearmonth) `max`,
       min(sales) over (partition by yearmonth) `min`,
       count(*) over (partition by yearmonth) `count`
from detail
where yearmonth in (201601,201602) and sales>49500
order by yearmonth, sales desc;

A

1

=connect("mysql")

2

=A1.query@x("select * from detail where yearmonth in (201601,201602) and sales>49500 order by yearmonth,sales desc")

3

=A2.groups(yearmonth;sum(sales):sum,avg(sales):avg,max(sales):max,min(sales):min, count(1):count)

4

=A2.switch(yearmonth,A3)

5

=A4.new(yearmonth.yearmonth:yearmonth,province,sales,yearmonth.sum:sum, yearmonth.avg:avg,yearmonth.max:max,yearmonth.min:min,yearmonth.count:count)


(1)A2 中按月份分组并对销售额求和、求平均、求最大、求最小及每组行数;

(2)A4 按月份将 A2 中 yearmonth 字段值转换成 A3 中相同月份的记录

执行后 A5 的结果如下。

2、VARIANCE()、STD()

a)

select province, sales, variance(sales) over() `variance`, std(sales) over() `std`
from detail where yearmonth=201601;

A

1

=connect("mysql")

2

=A1.query("select * from detail where yearmonth=201601")

3

=A2.variance(sales)

4

=sqrt(A3)

5

=A2.new(province,sales,A3:variance,A4:std)


(1)A3 对销售额求方差。

(2)A4 对 A3 求平方根即为标准差

执行后 A5 的结果如下。

b)

select yearmonth, province, sales,
       variance(sales) over(partition by yearmonth) `variance`,
       std(sales) over(partition by yearmonth) `std`
from detail
where yearmonth in (201601, 201602);

A

1

=connect("mysql")

2

=A1.query@x("select * from detail where yearmonth in (201601,201602) order by yearmonth")

3

=A2.group(yearmonth)

4

=A3.new(yearmonth:m,~.variance(sales):v, sqrt(v):v2)

5

=A2.switch(yearmonth, A4:m)

6

=A5.new(yearmonth.m:yearmonth, province, sales, yearmonth.v:variance, yearmonth.v2:std)


(1)A3 按月份分组

(2)A4 求每月销售额的方差

执行后 A6 的结果如下:

3、ROW_NUMBER()、RANK()、DENSE_RANK()、PERCENT_RANK()

a)

select province, sales, row_number() over(order by sales desc) `row_number`,
       rank() over (order by sales desc) `rank`,
       dense_rank() over (order by sales desc) `dense_rank`,
       percent_rank() over (order by sales desc) `percent_rank`
from detail
where yearmonth=201601;

A

1

=connect("mysql")

2

=A1.query("select * from detail where yearmonth=201601")

3

=A2.sort(sales:-1)

4

=A2.count()

5

=A3.new(province,sales,#:row_number,rank(sales):rank,ranki(sales):dense_rank, if(A4>1,(rank-1)/(A4-1),0):percent_rank)


(1)A5 中 #表示当前行在 A3 中的序号

(2) 百分比排名的公式 =if(行数 >1,( 排名 -1)/(行数 -1))

执行后 A5 的结果如下:

b)

select province, sales,
       row_number() over(partition by yearmonth order by sales desc)
              `row_number`,
       rank() over (partition by yearmonth order by sales desc) `rank`,
       dense_rank() over (partition by yearmonth order by sales desc)
              `dense_rank`,
       percent_rank() over (partition by yearmonth order by sales desc)
              `percent_rank`
from detail
where yearmonth in (201601,201602);

A

1

=connect("mysql")

2

=A1.query("select * from detail where yearmonth in (201601,201602)")

3

=A2.sort(yearmonth,sales:-1)

4

=A2.groups(yearmonth:m;count(1):count)

5

=A2.switch(yearmonth,A4:m)

6

=A3.new(yearmonth,province,sales,seq(yearmonth):row_number,rank(sales;yearmonth):rank, ranki(sales;yearmonth):dense_rank, if(yearmonth.count>1, (rank-1)/(yearmonth.count-1),0):percent_rank)


执行后 A6 的结果如下:

4、NTILE()

a)

select province, sales, ntile(3) over() `ntile`
from detail
where yearmonth=201601;

A

1

=connect("mysql")

2

=A1.query@x("select * from detail where yearmonth=201601")

3

= 桶数 =3

4

=A2.count()

5

=A2.new(province,sales,z(#, 桶数,A4):ntile)


(1)A3 里指明桶数为 3

(2)A5 中 z(i, 桶数, 总行数) 计算第 i 行所在桶号

执行后 A9 的结果如下:

b)

select yearmonth, province, sales, ntile(3) over(partition by yearmonth) 
       `ntile`
from detail
where yearmonth=201601 or( yearmonth=201602 and province!='上海');

A

1

=connect("mysql")

2

=A1.query@x("select * from detail where yearmonth=201601 or (yearmonth=201602 and province!=' 上海 ') order by yearmonth" )

3

= 桶数 =3

4

=A2.group(yearmonth:m;~.count():count)

5

=A2.switch(yearmonth,A4:m)

6

=A5.new(yearmonth.m:yearmonth,province,sales, z(seq(yearmonth), 桶数, yearmonth.count):ntile)


执行后 A6 的结果如下:

5、FIRST_VALUE()、LAST_VALUE()、NTH_VALUE()、LAG()、LEAD()

a)

select province,sales,
       first_value(sales) over(partition by yearmonth) `first_value`,
       last_value(sales) over(partition by yearmonth) `last_value`,
       nth_value(sales, 5) over(partition by yearmonth) `nth_value`,
       lag(sales, 2) over(partition by yearmonth) `lag`,
       lead(sales, 3) over(partition by yearmonth) `lead`
from detail
where yearmonth=201601;

A

1

=connect("mysql")

2

=A1.query@x("select * from detail where yearmonth=201601")

3

=A2.new(province, sales, A2.m(1).sales:first_value,A2.m(-1).sales:last_value, A2.m(5).sales:nth_value, ~[-2].sales:lag,~[3].sales:lead)


(1)Am(i) 取 A2 中第 i 条记录,越界返回 null,负数则从后往前数第 abs(i) 条记录,不能使用 A2(i),因为 A2(i) 越界会报错

执行后 A3 的结果如下:

b)

select yearmonth,province,sales,
       first_value(sales) over(partition by yearmonth) `first_value`,
       last_value(sales) over(partition by yearmonth) `last_value`,
       nth_value(sales, 5) over(partition by yearmonth) `nth_value`,
       lag(sales, 2) over(partition by yearmonth) `lag`,
       lead(sales, 3) over(partition by yearmonth) `lead`
from detail
where yearmonth=201601 or (yearmonth=201602 and sales>50000);

A

1

=connect("mysql")

2

=A1.query@x("select * from detail where yearmonth=201601 or (yearmonth=201602 and sales>50000) order by yearmonth")

3

=A2.group(yearmonth:m;~.count():count,~.m(1).sales:first_value, ~.m(-1).sales:last_value,~.m(5).sales:nth_value)

4

=A2.switch(yearmonth, A3:m)

5

=A2.new(yearmonth.m:yearmonth, province, sales, yearmonth.first_value:first_value,yearmonth.last_value:last_value,yearmonth.nth_value:nth_value, (seq=seq(yearmonth),if(seq>2,~[-2].sales,null)):lag,if(yearmonth.count-seq>=3,~[3].sales,null):lead)


(1)A5 中,seq(yearmonth) 尽可能不要在 if 函数中使用,因为 seq 函数是在对 A2 中记录循环过程中累加的,导致 seq 函数少执行 1 次就少累加 1。

(2)A5 中,前面的表达式用 seq=seq(yearmonth) 对变量 seq 赋值,这样后续表达式就可以引用变量 seq。

执行后 A5 的结果如下:

6、CUME_DIST()

a)

select province,sales, cume_dist() over(order by sales) `cume_dist`
from detail
where yearmonth=201601;

A

1

=connect("mysql")

2

=A1.query@x("select * from detail where yearmonth=201601 order by sales desc")

3

=A2.count()

4

=A2.new(province,sales,(A3-rank(sales)+1)/A3:cume_dist)

5

=A4.rvs()


(1)CUME_DIST()over (order by sales) 求销售额从小到大的累积概率分布,公式为 (小于等于当前销售额的行数 / 总行数)

(2) 小于等于当前销售额的行数 = 总行数 - 当前销售额从大到小的排名 +1

(3)A2 必须按销售额从大到小排序

(4)A5 数据倒排

执行后 A5 的结果如下:

b)

select yearmonth, province,sales,
       cume_dist() over(partition by yearmonth order by sales) `cume_dist`
from detail
where yearmonth in (201601,201602);

A

1

=connect("mysql")

2

=A1.query@x("select * from detail where yearmonth in (201601,201602) order by yearmonth desc,sales desc")

3

=A2.groups(yearmonth:m;count(1):count)

4

=A2.switch(yearmonth,A3:m)

5

=A2.new(yearmonth.m:yearmonth,province,sales,(yearmonth.count-rank(sales;yearmonth)+1)/yearmonth.count:cume_dist)

6

=A5.rvs()


(1) 对应于最后的倒排,A2 中按月份从大到小排序

执行后 A6 的结果如下:

看完十多个例子,有没有觉得集算器代码实现 so easy?!而且,由于集算器可以对单元格进行分步计算,我们可以按照自然的思路逐步查看查询结果,从而更加简便、直观地完善整个查询脚本。赶紧用起来吧,你会发现更多又方便又强大的功能!

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