第八章_函数【窗口函数】
1. 开窗函数的作用
说明 : 开窗函数就是 根据指定的开窗规则 为表的每条记录,标记状态,不会增加或者减少表的记录数
2. 语法
分析函数、聚合函数 over(partition by 列名 order by 列名 rows between 开始位置 and 结束位置)
注意 : over(partition by x order by y) = over(distribute by x sort by y)
3. 分区 : partition by 列名
说明 : 按照指定字段分区,可以为多个字段,相当于 group by
不指定分区时,操作的是全表
4. 排序 : order by 列名 [asc|desc]
说明 : 按照指定字段,进行排序(默认为asc)
如果指定分区,则是分区内排序,不指定分区,则是全表排序
聚合函数 + 开窗范围 + order by = 累计求值
5. 开窗范围(window specification) : rows between 开始位置 and 结束位置
说明 : 计算的范围
注意 : 1. 不指定时,默认范围: 第一行到当前行
2. 开窗范围 语法能在 聚合函数(count、sum、min、max、avg)中使用
位置参数 :
current row:当前行
n preceding:往前 n 行数据
n following:往后 n 行数据
unbounded:起点，
unbounded preceding 表示该窗口最前面的行（起点）
unbounded following 表示该窗口最后面的行（终点)
示例 :
rows between unbounded preceding and current row -- 表示从起点到当前行 rows between 2 preceding and 1 following -- 表示往前2行到往后1行 rows between 2 preceding and current row -- 表示往前2行到当前行 rows between current row and unbounded following -- 表示当前行到终点 rows between unbounded preceding and 1 preceding -- 首行到当前行的前一行
rows between unbounded preceding and unbounded following -- 首行到终点
with t1 as (
select 'huawei' as channel ,'2021-08-01' as credit_date,5 as score
union all
select 'huawei' as channel ,'2021-08-01' as credit_date,5 as score
union all
select 'huawei' as channel ,'2021-08-03' as credit_date,12 as score
union all
select 'huawei' as channel ,'2021-08-04' as credit_date,25 as score
union all
select 'huawei' as channel ,'2021-08-02' as credit_date,1 as score
union all
select 'huawei' as channel ,'2021-08-07' as credit_date,7 as score
union all
select 'huawei' as channel ,'2021-08-08' as credit_date,10 as score
union all
select 'huawei' as channel ,'2021-08-06' as credit_date,33 as score
union all
select 'vivo' as channel ,'2021-09-01' as credit_date,1 as score
union all
select 'vivo' as channel ,'2021-09-02' as credit_date,2 as score
union all
select 'vivo' as channel ,'2021-09-04' as credit_date,5 as score
union all
select 'vivo' as channel ,'2021-09-09' as credit_date,9 as score
union all
select 'vivo' as channel ,'2021-09-07' as credit_date,77 as score
union all
select 'vivo' as channel ,'2021-09-08' as credit_date,10 as score
union all
select 'vivo' as channel ,'2021-09-11' as credit_date,3 as score
)
select *
,sum(t1.score) over(partition by t1.channel
ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
) as sum1 -- 开窗范围:首行~当前行(默认参数为此参数)
,sum(t1.score) over(partition by t1.channel
ROWS BETWEEN CURRENT ROW AND UNBOUNDED FOLLOWING
) as sum2 -- 开窗范围:当前行~末尾行
from t1
;
channel credit_date score sum1 sum2
huawei 2021-08-01 5 5 98
huawei 2021-08-06 33 38 93
huawei 2021-08-08 10 48 60
huawei 2021-08-07 7 55 50
huawei 2021-08-02 1 56 43
huawei 2021-08-04 25 81 42
huawei 2021-08-03 12 93 17
huawei 2021-08-01 5 98 5
vivo 2021-09-08 10 10 107
vivo 2021-09-07 77 87 97
vivo 2021-09-09 9 96 20
vivo 2021-09-04 5 101 11
vivo 2021-09-02 2 103 6
vivo 2021-09-01 1 104 4
vivo 2021-09-11 3 107 3
6. 聚合函数
count、sum、min、max、avg
7. 分析函数
rank : 跳跃排序 当排序字段值相同时, 自增序号重复,且会跳过下一个序号 示例(1,2,3,4,4,6)
row_number : 连续排序 当排序字段值相同时, 自增序号不会重复 示例(1,2,3,4,5,6)
dense_rank : 密集排序 当排序字段值相同时, 自增序号重复,且不会跳过下一个序号 示例(1,2,3,4,4,5)
cume_dist : <= 当前值的记录数据 / 分组内总记录数
percent_rank : 分组内当前行的RANK值-1/分组内总行数-1
ntile(x) : 将窗口内数据进行切片(根据序号),并返回该记录的切片编号
当切片不均时,默认增加第一个切片的分布
说明 :根据row_number,将数据划分为x等份,x为ntile(x)
-- 7.1 排序函数示例
-- rank、dense_rank、row_number
-- 排序函数示例
-- rank、dense_rank、row_number
with t1 as (
select 'huawei' as channel ,'2021-08-01' as credit_date,5 as score
union all
select 'huawei' as channel ,'2021-08-01' as credit_date,5 as score
union all
select 'huawei' as channel ,'2021-08-03' as credit_date,12 as score
union all
select 'huawei' as channel ,'2021-08-04' as credit_date,25 as score
union all
select 'huawei' as channel ,'2021-08-02' as credit_date,1 as score
union all
select 'huawei' as channel ,'2021-08-07' as credit_date,7 as score
union all
select 'huawei' as channel ,'2021-08-08' as credit_date,10 as score
union all
select 'huawei' as channel ,'2021-08-06' as credit_date,33 as score
union all
select 'vivo' as channel ,'2021-09-01' as credit_date,1 as score
union all
select 'vivo' as channel ,'2021-09-02' as credit_date,2 as score
union all
select 'vivo' as channel ,'2021-09-04' as credit_date,5 as score
union all
select 'vivo' as channel ,'2021-09-09' as credit_date,9 as score
union all
select 'vivo' as channel ,'2021-09-07' as credit_date,77 as score
union all
select 'vivo' as channel ,'2021-09-08' as credit_date,10 as score
union all
select 'vivo' as channel ,'2021-09-11' as credit_date,3 as score
)
select *
,rank() over(partition by t1.channel order by t1.score) as rank
,dense_rank() over(partition by t1.channel order by t1.score) as dense_rank
,row_number() over(partition by t1.channel order by t1.score) as row_number
from t1;
channel credit_date score rank dense_rank row_number
huawei 2021-08-02 1 1 1 1
huawei 2021-08-01 5 2 2 2
huawei 2021-08-01 5 2 2 3
huawei 2021-08-07 7 4 3 4
huawei 2021-08-08 10 5 4 5
huawei 2021-08-03 12 6 5 6
huawei 2021-08-04 25 7 6 7
huawei 2021-08-06 33 8 7 8
vivo 2021-09-01 1 1 1 1
vivo 2021-09-02 2 2 2 2
vivo 2021-09-11 3 3 3 3
vivo 2021-09-04 5 4 4 4
vivo 2021-09-09 9 5 5 5
vivo 2021-09-08 10 6 6 6
vivo 2021-09-07 77 7 7 7
-- 7.2 统计当前记录组内占比
-- cume_dist
-- 7.2 统计当前记录组内占比
-- cume_dist
with t1 as (
select 'huawei' as channel ,'2021-08-01' as credit_date,5 as score
union all
select 'huawei' as channel ,'2021-08-01' as credit_date,5 as score
union all
select 'huawei' as channel ,'2021-08-03' as credit_date,12 as score
union all
select 'huawei' as channel ,'2021-08-04' as credit_date,25 as score
union all
select 'huawei' as channel ,'2021-08-02' as credit_date,1 as score
union all
select 'huawei' as channel ,'2021-08-07' as credit_date,7 as score
union all
select 'huawei' as channel ,'2021-08-08' as credit_date,10 as score
union all
select 'huawei' as channel ,'2021-08-06' as credit_date,33 as score
union all
select 'vivo' as channel ,'2021-09-01' as credit_date,1 as score
union all
select 'vivo' as channel ,'2021-09-02' as credit_date,2 as score
union all
select 'vivo' as channel ,'2021-09-04' as credit_date,5 as score
union all
select 'vivo' as channel ,'2021-09-09' as credit_date,9 as score
union all
select 'vivo' as channel ,'2021-09-07' as credit_date,77 as score
union all
select 'vivo' as channel ,'2021-09-08' as credit_date,10 as score
union all
select 'vivo' as channel ,'2021-09-11' as credit_date,3 as score
)
select *
,cume_dist() over(partition by t1.channel order by t1.score) as zhanbi
from t1;
channel credit_date score zhanbi
huawei 2021-08-02 1 0.125
huawei 2021-08-01 5 0.375
huawei 2021-08-01 5 0.375
huawei 2021-08-07 7 0.5
huawei 2021-08-08 10 0.625
huawei 2021-08-03 12 0.75
huawei 2021-08-04 25 0.875
huawei 2021-08-06 33 1.0
vivo 2021-09-01 1 0.14285714285714285
vivo 2021-09-02 2 0.2857142857142857
vivo 2021-09-11 3 0.42857142857142855
vivo 2021-09-04 5 0.5714285714285714
vivo 2021-09-09 9 0.7142857142857143
vivo 2021-09-08 10 0.8571428571428571
vivo 2021-09-07 77 1.0
-- 7.3 统计当前排序号组内占比
-- percent_rank
-- 7.3 统计当前排序号组内占比
-- percent_rank
with t1 as (
select 'huawei' as channel ,'2021-08-01' as credit_date,5 as score
union all
select 'huawei' as channel ,'2021-08-01' as credit_date,5 as score
union all
select 'huawei' as channel ,'2021-08-03' as credit_date,12 as score
union all
select 'huawei' as channel ,'2021-08-04' as credit_date,25 as score
union all
select 'huawei' as channel ,'2021-08-02' as credit_date,1 as score
union all
select 'huawei' as channel ,'2021-08-07' as credit_date,7 as score
union all
select 'huawei' as channel ,'2021-08-08' as credit_date,10 as score
union all
select 'huawei' as channel ,'2021-08-06' as credit_date,33 as score
union all
select 'vivo' as channel ,'2021-09-01' as credit_date,1 as score
union all
select 'vivo' as channel ,'2021-09-02' as credit_date,2 as score
union all
select 'vivo' as channel ,'2021-09-04' as credit_date,5 as score
union all
select 'vivo' as channel ,'2021-09-09' as credit_date,9 as score
union all
select 'vivo' as channel ,'2021-09-07' as credit_date,77 as score
union all
select 'vivo' as channel ,'2021-09-08' as credit_date,10 as score
union all
select 'vivo' as channel ,'2021-09-11' as credit_date,3 as score
)
select *
,percent_rank() over(partition by t1.channel order by t1.score) as rank_zhanbi
,rank() over(partition by t1.channel order by t1.score) as rank
from t1;
channel credit_date score rank_zhanbi rank
huawei 2021-08-02 1 0.0 1
huawei 2021-08-01 5 0.14285714285714285 2
huawei 2021-08-01 5 0.14285714285714285 2
huawei 2021-08-07 7 0.42857142857142855 4
huawei 2021-08-08 10 0.5714285714285714 5
huawei 2021-08-03 12 0.7142857142857143 6
huawei 2021-08-04 25 0.8571428571428571 7
huawei 2021-08-06 33 1.0 8
vivo 2021-09-01 1 0.0 1
vivo 2021-09-02 2 0.16666666666666666 2
vivo 2021-09-11 3 0.3333333333333333 3
vivo 2021-09-04 5 0.5 4
vivo 2021-09-09 9 0.6666666666666666 5
vivo 2021-09-08 10 0.8333333333333334 6
vivo 2021-09-07 77 1.0 7
-- 7.4 切片函数
-- ntile
-- 7.4 切片函数
-- ntile
with t1 as (
select 'huawei' as channel ,'2021-08-01' as credit_date,5 as score
union all
select 'huawei' as channel ,'2021-08-01' as credit_date,5 as score
union all
select 'huawei' as channel ,'2021-08-03' as credit_date,12 as score
union all
select 'huawei' as channel ,'2021-08-04' as credit_date,25 as score
union all
select 'huawei' as channel ,'2021-08-02' as credit_date,1 as score
union all
select 'huawei' as channel ,'2021-08-07' as credit_date,7 as score
union all
select 'huawei' as channel ,'2021-08-08' as credit_date,10 as score
union all
select 'huawei' as channel ,'2021-08-06' as credit_date,33 as score
union all
select 'vivo' as channel ,'2021-09-01' as credit_date,1 as score
union all
select 'vivo' as channel ,'2021-09-02' as credit_date,2 as score
union all
select 'vivo' as channel ,'2021-09-04' as credit_date,5 as score
union all
select 'vivo' as channel ,'2021-09-09' as credit_date,9 as score
union all
select 'vivo' as channel ,'2021-09-07' as credit_date,77 as score
union all
select 'vivo' as channel ,'2021-09-08' as credit_date,10 as score
union all
select 'vivo' as channel ,'2021-09-11' as credit_date,3 as score
)
select *
,ntile(2) over(partition by t1.channel order by t1.score) as splitNum
from t1;
channel credit_date score splitnum
huawei 2021-08-02 1 1
huawei 2021-08-01 5 1
huawei 2021-08-01 5 1
huawei 2021-08-07 7 1
huawei 2021-08-08 10 2
huawei 2021-08-03 12 2
huawei 2021-08-04 25 2
huawei 2021-08-06 33 2
vivo 2021-09-01 1 1
vivo 2021-09-02 2 1
vivo 2021-09-11 3 1
vivo 2021-09-04 5 1
vivo 2021-09-09 9 2
vivo 2021-09-08 10 2
vivo 2021-09-07 77 2
8. 窗口函数(lag,lead,first_value,last_value)
lag :
说明 : 用来访问前一行的数据(UDF)
语法 : LAG (scalar_expression [,offset] [,default]) OVER ([query_partition_clause] order_by_clause);
scalar_expression : 指定字段名称
offset : 当前行的前x行,默认为1
default : 指定 字段为空时的 默认值,默认值 为null
lead :
说明 : 用来访问后一行的数据(UDF)
语法 : LEAD (scalar_expression [,offset] [,default]) OVER ([query_partition_clause] order_by_clause);
scalar_expression : 指定字段名称
offset : 当前行的后x行,默认为1
default : 指定 字段为空时的 默认值,默认值 为null
first_value :
说明 : 获取 分组内排序第一的数据(第一行到当前行)
last_value :
说明 : 获取 分组内排序最后的数据(第一行到当前行)
-- 1. lag 示例
-- 1. lag
with t1 as (
select 'huawei' as channel ,'2021-08-01' as credit_date,5 as score
union all
select 'huawei' as channel ,'2021-08-01' as credit_date,5 as score
union all
select 'huawei' as channel ,'2021-08-03' as credit_date,12 as score
union all
select 'huawei' as channel ,'2021-08-04' as credit_date,25 as score
union all
select 'huawei' as channel ,'2021-08-02' as credit_date,1 as score
union all
select 'huawei' as channel ,'2021-08-07' as credit_date,7 as score
union all
select 'huawei' as channel ,'2021-08-08' as credit_date,10 as score
union all
select 'huawei' as channel ,'2021-08-06' as credit_date,33 as score
union all
select 'vivo' as channel ,'2021-09-01' as credit_date,1 as score
union all
select 'vivo' as channel ,'2021-09-02' as credit_date,2 as score
union all
select 'vivo' as channel ,'2021-09-04' as credit_date,5 as score
union all
select 'vivo' as channel ,'2021-09-09' as credit_date,9 as score
union all
select 'vivo' as channel ,'2021-09-07' as credit_date,77 as score
union all
select 'vivo' as channel ,'2021-09-08' as credit_date,10 as score
union all
select 'vivo' as channel ,'2021-09-11' as credit_date,3 as score
)
select *
,lag(credit_date) over(partition by t1.channel order by t1.score) as pre1_row -- 当前行的前一行
,lag(credit_date,2,'9999-99-99') over(partition by t1.channel order by t1.score) as pre2_row -- 当前行的前2行,为null时默认值为 9999-99-99
from t1;
channel credit_date score pre1_row pre2_row
huawei 2021-08-02 1 NULL 9999-99-99
huawei 2021-08-01 5 2021-08-02 9999-99-99
huawei 2021-08-01 5 2021-08-01 2021-08-02
huawei 2021-08-07 7 2021-08-01 2021-08-01
huawei 2021-08-08 10 2021-08-07 2021-08-01
huawei 2021-08-03 12 2021-08-08 2021-08-07
huawei 2021-08-04 25 2021-08-03 2021-08-08
huawei 2021-08-06 33 2021-08-04 2021-08-03
vivo 2021-09-01 1 NULL 9999-99-99
vivo 2021-09-02 2 2021-09-01 9999-99-99
vivo 2021-09-11 3 2021-09-02 2021-09-01
vivo 2021-09-04 5 2021-09-11 2021-09-02
vivo 2021-09-09 9 2021-09-04 2021-09-11
vivo 2021-09-08 10 2021-09-09 2021-09-04
vivo 2021-09-07 77 2021-09-08 2021-09-09
-- 2. lead 示例
-- 2. lead 示例
with t1 as (
select 'huawei' as channel ,'2021-08-01' as credit_date,5 as score
union all
select 'huawei' as channel ,'2021-08-01' as credit_date,5 as score
union all
select 'huawei' as channel ,'2021-08-03' as credit_date,12 as score
union all
select 'huawei' as channel ,'2021-08-04' as credit_date,25 as score
union all
select 'huawei' as channel ,'2021-08-02' as credit_date,1 as score
union all
select 'huawei' as channel ,'2021-08-07' as credit_date,7 as score
union all
select 'huawei' as channel ,'2021-08-08' as credit_date,10 as score
union all
select 'huawei' as channel ,'2021-08-06' as credit_date,33 as score
union all
select 'vivo' as channel ,'2021-09-01' as credit_date,1 as score
union all
select 'vivo' as channel ,'2021-09-02' as credit_date,2 as score
union all
select 'vivo' as channel ,'2021-09-04' as credit_date,5 as score
union all
select 'vivo' as channel ,'2021-09-09' as credit_date,9 as score
union all
select 'vivo' as channel ,'2021-09-07' as credit_date,77 as score
union all
select 'vivo' as channel ,'2021-09-08' as credit_date,10 as score
union all
select 'vivo' as channel ,'2021-09-11' as credit_date,3 as score
)
select *
,lead(credit_date) over(partition by t1.channel order by t1.score) as next1_row -- 当前行的前一行
,lead(credit_date,2,'9999-99-99') over(partition by t1.channel order by t1.score) as next2_row -- 当前行的前2行,为null时默认值为 9999-99-99
from t1;
channel credit_date score next1_row next2_row
huawei 2021-08-02 1 2021-08-01 2021-08-01
huawei 2021-08-01 5 2021-08-01 2021-08-07
huawei 2021-08-01 5 2021-08-07 2021-08-08
huawei 2021-08-07 7 2021-08-08 2021-08-03
huawei 2021-08-08 10 2021-08-03 2021-08-04
huawei 2021-08-03 12 2021-08-04 2021-08-06
huawei 2021-08-04 25 2021-08-06 9999-99-99
huawei 2021-08-06 33 NULL 9999-99-99
vivo 2021-09-01 1 2021-09-02 2021-09-11
vivo 2021-09-02 2 2021-09-11 2021-09-04
vivo 2021-09-11 3 2021-09-04 2021-09-09
vivo 2021-09-04 5 2021-09-09 2021-09-08
vivo 2021-09-09 9 2021-09-08 2021-09-07
vivo 2021-09-08 10 2021-09-07 9999-99-99
vivo 2021-09-07 77 NULL 9999-99-99
-- 3. first_value、last_value 示例
-- 3. first_value、last_value 示例
with t1 as (
select 'huawei' as channel ,'2021-08-01' as credit_date,5 as score
union all
select 'huawei' as channel ,'2021-08-01' as credit_date,5 as score
union all
select 'huawei' as channel ,'2021-08-03' as credit_date,12 as score
union all
select 'huawei' as channel ,'2021-08-04' as credit_date,25 as score
union all
select 'huawei' as channel ,'2021-08-02' as credit_date,1 as score
union all
select 'huawei' as channel ,'2021-08-07' as credit_date,7 as score
union all
select 'huawei' as channel ,'2021-08-08' as credit_date,10 as score
union all
select 'huawei' as channel ,'2021-08-06' as credit_date,33 as score
union all
select 'vivo' as channel ,'2021-09-01' as credit_date,1 as score
union all
select 'vivo' as channel ,'2021-09-02' as credit_date,2 as score
union all
select 'vivo' as channel ,'2021-09-04' as credit_date,5 as score
union all
select 'vivo' as channel ,'2021-09-09' as credit_date,9 as score
union all
select 'vivo' as channel ,'2021-09-07' as credit_date,77 as score
union all
select 'vivo' as channel ,'2021-09-08' as credit_date,10 as score
union all
select 'vivo' as channel ,'2021-09-11' as credit_date,3 as score
)
select *
,first_value(credit_date) over(partition by t1.channel order by t1.score) as first_value
,last_value(credit_date) over(partition by t1.channel order by t1.score) as last_value
,row_number() over(partition by t1.channel order by t1.score) as rank
from t1;
channel credit_date score first_value last_value rank
huawei 2021-08-02 1 2021-08-02 2021-08-02 1
huawei 2021-08-01 5 2021-08-02 2021-08-01 2
huawei 2021-08-01 5 2021-08-02 2021-08-01 3
huawei 2021-08-07 7 2021-08-02 2021-08-07 4
huawei 2021-08-08 10 2021-08-02 2021-08-08 5
huawei 2021-08-03 12 2021-08-02 2021-08-03 6
huawei 2021-08-04 25 2021-08-02 2021-08-04 7
huawei 2021-08-06 33 2021-08-02 2021-08-06 8
vivo 2021-09-01 1 2021-09-01 2021-09-01 1
vivo 2021-09-02 2 2021-09-01 2021-09-02 2
vivo 2021-09-11 3 2021-09-01 2021-09-11 3
vivo 2021-09-04 5 2021-09-01 2021-09-04 4
vivo 2021-09-09 9 2021-09-01 2021-09-09 5
vivo 2021-09-08 10 2021-09-01 2021-09-08 6
vivo 2021-09-07 77 2021-09-01 2021-09-07 7
9. window clause (窗口子句)
说明: 将窗口部分单独提出来,放入表的后面使用,多个函数 可以共享一个窗口
-- window clause 示例
-- window clause 示例
with t1 as (
select 'huawei' as channel ,'2021-08-01' as credit_date,5 as score
union all
select 'huawei' as channel ,'2021-08-01' as credit_date,5 as score
union all
select 'huawei' as channel ,'2021-08-03' as credit_date,12 as score
union all
select 'huawei' as channel ,'2021-08-04' as credit_date,25 as score
union all
select 'huawei' as channel ,'2021-08-02' as credit_date,1 as score
union all
select 'huawei' as channel ,'2021-08-07' as credit_date,7 as score
union all
select 'huawei' as channel ,'2021-08-08' as credit_date,10 as score
union all
select 'huawei' as channel ,'2021-08-06' as credit_date,33 as score
union all
select 'vivo' as channel ,'2021-09-01' as credit_date,1 as score
union all
select 'vivo' as channel ,'2021-09-02' as credit_date,2 as score
union all
select 'vivo' as channel ,'2021-09-04' as credit_date,5 as score
union all
select 'vivo' as channel ,'2021-09-09' as credit_date,9 as score
union all
select 'vivo' as channel ,'2021-09-07' as credit_date,77 as score
union all
select 'vivo' as channel ,'2021-09-08' as credit_date,10 as score
union all
select 'vivo' as channel ,'2021-09-11' as credit_date,3 as score
)
select *
,count(credit_date) over w1 as cnt
,sum(score) over w1 as sum
from t1
window w1 as (partition by t1.channel order by t1.score)
;
channel credit_date score cnt sum
huawei 2021-08-02 1 1 1
huawei 2021-08-01 5 3 11
huawei 2021-08-01 5 3 11
huawei 2021-08-07 7 4 18
huawei 2021-08-08 10 5 28
huawei 2021-08-03 12 6 40
huawei 2021-08-04 25 7 65
huawei 2021-08-06 33 8 98
vivo 2021-09-01 1 1 1
vivo 2021-09-02 2 2 3
vivo 2021-09-11 3 3 6
vivo 2021-09-04 5 4 11
vivo 2021-09-09 9 5 20
vivo 2021-09-08 10 6 30
vivo 2021-09-07 77 7 107
10. 参考链接
官网 : https://cwiki.apache.org/confluence/display/Hive/LanguageManual+WindowingAndAnalytics
案例1 : http://lxw1234.com/archives/tag/hive-window-functions
案例2 ：https://blog.csdn.net/a934079371/article/details/108301730?utm_medium=distribute.pc_relevant.none-task-blog-2%7Edefault%7ECTRLIST%7Edefault-3.control&depth_1-utm_source=distribute.pc_relevant.none-task-blog-2%7Edefault%7ECTRLIST%7Edefault-3.control
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