Hive preceding and following理解

在了解hive開窗函數前我們來看看Hive窗口函數preceding and following是怎么回事呢.
Hive窗口函數中,有一個功能是統計當前行之前或之后指定行作為一個聚合,關鍵字是 preceding 和 following,舉例說明其使用方法.
常規的窗口函數比較簡單,這里介紹一下分組的,重點是分組排序之后的rows between用法。
關鍵是理解rows between中關鍵字含義：

關鍵字含義

preceding	往前
following	往后
current row	當前行
unbounded	開始行
unbounded preceding	表示從前面的起點
unbounded following	表示到后面的終點

案例

select country,time,charge, max(charge) over (partition by country order by time) as normal, max(charge) over (partition by country order by time rows between unbounded preceding and current row) as unb_pre_cur, max(charge) over (partition by country order by time rows between 2 preceding and 1 following) as pre2_fol1, max(charge) over (partition by country order by time rows between current row and unbounded following) as cur_unb_fol from temp

注意:默認是在分組類的當前行之前的行中計算。
rows between unbounded preceding and current row和默認的一樣
rows between 2 preceding and 1 following表示在當前行的前2行和后1行中計算
rows between current row and unbounded following表示在當前行和到最后行中計算
rows between對于avg、min、max、sum這幾個窗口函數的含義基本是一致的,注意查看當前結果

在 hive 環境中創建臨時表

create table tmp_student (name string,class tinyint,cooperator_name string,score tinyint )row format delimited fields terminated by '|';

加載測試數據
load data local inpath ‘text.txt’ into table tmp_student;
其中text.txt中內容為:

adf|3|測試公司1|45 xx|3|測試公司2|55 cfe|2|測試公司2|74 3dd|3|測試公司5|n fda|1|測試公司7|80 gds|2|測試公司9|92 ffd|1|測試公司10|95 dss|1|測試公司4|95 ddd|3|測試公司3|99 gf|3|測試公司9|99

查看是否加載成功

hive> select * from tmp_student; adf 3 測試公司1 45 xx 3 測試公司2 55 cfe 2 測試公司2 74 3dd 3 測試公司5 NULL fda 1 測試公司7 80 gds 2 測試公司9 92 ffd 1 測試公司10 95 dss 1 測試公司4 95 ddd 3 測試公司3 99 gf 3 測試公司9 99 Time taken: 1.314 seconds, Fetched: 10 row(s)

下面來了解preceding and following函數用法,執行下面sql

selectname,score,sum(score) over(order by score range between 2 preceding and 2 following) s1, -- 當前行的score值加減2的范圍內的所有行sum(score) over(order by score rows between 2 preceding and 2 following) s2, -- 當前行+前后2行,一共5行sum(score) over(order by score range between unbounded preceding and unbounded following) s3, -- 全部行,不做限制sum(score) over(order by score rows between unbounded preceding and unbounded following) s4, -- 全部行,不做限制sum(score) over(order by score) s5, -- 第一行到當前行（和當前行相同score值的所有行都會包含進去）sum(score) over(order by score rows between unbounded preceding and current row) s6, -- 第一行到當前行（和當前行相同score值的其他行不會包含進去,這是和上面的區別）sum(score) over(order by score rows between 3 preceding and current row) s7, -- 當前行+往前3行sum(score) over(order by score rows between 3 preceding and 1 following) s8, --當前行+往前3行+往后1行sum(score) over(order by score rows between current row and unbounded following) s9 --當前行+往后所有行 fromtmp.tmp_student order by score;

注意:
當ORDER BY后面缺少窗口從句條件，窗口規范默認是 RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW.
當ORDER BY和窗口從句都缺失, 窗口規范默認是 ROW BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING.
rows是物理窗口，是哪一行就是哪一行，與當前行的值（order by key的key的值）無關，只與排序后的行號相關，就是我們常規理解的那樣。
range是邏輯窗口，與當前行的值有關（order by key的key的值）,在key上操作range范圍。
得到相關結果如下
通過上面的練習我們主要是對preceding and following有了一個比較全面的理解,所謂開窗函數其實就相當于flink中的滾動窗口,統計分析都是基于這個滾動窗口內完成的,所有的聚合計算統計都需要先根據range或者row確定窗口內的數據,然后就很容易得到正確的計算結果,在確定行數的過程中需要根據range和row確定是邏輯范圍還是物理范圍,最終都可以看作是第N行到第M行內數據的聚合統計.
Flink窗口說明

窗口函Windowing functions

• FIRST_VALUE(col, bool DEFAULT)

  返回分組窗口內第一行col的值,DEFAULT默認為false,如果指定為true,則跳過NULL后再取值,對于FIRST_VALUE每個分組第一行數據的FIRST_VALUE(col, bool DEFAULT) 就等于col,接下來幾行數據會參考第一行數據是否為NULL根據True/False進行取舍.

WITH tmp AS (SELECT 1 AS group_id, 'a' AS colUNION ALLSELECT 1 AS group_id, 'b' AS colUNION ALLSELECT 1 AS group_id, 'c' AS colUNION ALLSELECT 2 AS group_id, NULL AS colUNION ALLSELECT 2 AS group_id, 'e' AS col) SELECT group_id, col, FIRST_VALUE(col) OVER (PARTITION BY group_id ORDER BY col) AS col_new FROM tmp; 返回結果為: group_id col col_new 1 a a 1 b a 1 c a 2 NULL NULL 2 e NULL 如果是True WITH tmp AS (SELECT 1 AS group_id, NULL AS colUNION ALLSELECT 1 AS group_id, 'b' AS colUNION ALLSELECT 1 AS group_id, 'c' AS colUNION ALLSELECT 2 AS group_id, NULL AS colUNION ALLSELECT 2 AS group_id, 'e' AS col) SELECT group_id, col, FIRST_VALUE(col, true) OVER (PARTITION BY group_id ORDER BY col) AS col_new FROM tmp; 返回結果為： group_id col col_new 1 NULL NULL 1 b b 1 c b 2 NULL NULL 2 e e

• LAST_VALUE(col, bool DEFAULT)
  返回分組窗口內第后一行col的值,DEFAULT默認為false,如果指定為true,則跳過NULL后再取值.

WITH tmp AS (SELECT 1 AS group_id, 'a' AS colUNION ALLSELECT 1 AS group_id, NULL AS colUNION ALLSELECT 1 AS group_id, 'c' AS colUNION ALLSELECT 2 AS group_id, 'd' AS colUNION ALLSELECT 2 AS group_id, 'e' AS col) SELECT group_id, col, LAST_VALUE(col) OVER (PARTITION BY group_id ORDER BY col DESC) AS col_new FROM tmp; 返回結果為： group_id col col_new 1 c c 1 a a 1 NULL NULL 2 e e 2 d d 如果是True WITH tmp AS (SELECT 1 AS group_id, 'a' AS colUNION ALLSELECT 1 AS group_id, NULL AS colUNION ALLSELECT 1 AS group_id, 'c' AS colUNION ALLSELECT 2 AS group_id, 'd' AS colUNION ALLSELECT 2 AS group_id, 'e' AS col) SELECT group_id, col, LAST_VALUE(col, true) OVER (ORDER BY group_id,col DESC ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING) AS col_new FROM tmp; 返回結果為： group_id col col_new 1 c a 1 a a 1 NULL e 2 e d 2 d d 開窗函數不同于group by函數,開窗函數能夠把所有的記錄都顯示出來,一般select所選擇的列也都與over里面的分組和排序字段相同,這樣才能比較清楚地看到當前記錄在聚合函數中的區別和貢獻,上面兩個窗口函數我們針對最后一個案例進行說明下. 使用了開窗函數首先要確定窗口的大小,根據上面的PRECEDING和FOLLOWING講解我們可以知道在分析時候窗口大小為[前一行,當前行,后一行],那么對于第一行1 c取出last_value就是從[空值,c,a]取出集合中最后一個就是a,同理對于第二行1 a取出last_value就是從[c,a,Null]中取出最后一個Null跳過再取得到a,對于2 e從集合[Null,e,d]last_value=d*

• LEAD(col, n, DEFAULT)

返回分組窗口內往下第n行col的值,n默認為1,往下第n沒有時返回DEFAULT（DEFAULT默認為NULL）使用分組后那么分組之間就不交叉計算.

WITH tmp AS (SELECT 1 AS group_id, 'a' AS col UNION ALL SELECT 1 AS group_id, 'b' AS col UNION ALL SELECT 1 AS group_id, 'c' AS col UNION ALL SELECT 2 AS group_id, 'd' AS col UNION ALL SELECT 2 AS group_id, 'e' AS col ) SELECT group_id,col,LEAD(col) over(partition by group_id order by col) as col_new FROM tmp;

返回結果

group_id col col_new 1 a b 1 b c 1 c NULL 2 d e 2 e NULL

等同于

WITH tmp AS (SELECT 1 AS group_id, 'a' AS col UNION ALL SELECT 1 AS group_id, 'b' AS col UNION ALL SELECT 1 AS group_id, 'c' AS col UNION ALL SELECT 2 AS group_id, 'd' AS col UNION ALL SELECT 2 AS group_id, 'e' AS col ) SELECT group_id,col,LAST_VALUE(col) over(partition by group_id order by col rows between 1 FOLLOWING and 1 FOLLOWING) as col_new FROM tmp;

其中rows between 1 FOLLOWING and 1 FOLLOWING為從往后一行開始到往后一行結束=往后一行
返回結果

group_id col col_new 1 a b 1 b c 1 c NULL 2 d e 2 e NULL

使用LEAD默認值

WITH tmp AS (SELECT 1 AS group_id, 'a' AS col UNION ALL SELECT 1 AS group_id, 'b' AS col UNION ALL SELECT 1 AS group_id, 'c' AS col UNION ALL SELECT 2 AS group_id, 'd' AS col UNION ALL SELECT 2 AS group_id, 'e' AS col ) SELECT group_id,col,LEAD(col, 2, 'z') over(partition by group_id order by col) as col_new FROM tmp;

返回結果

group_id col col_new 1 a c 1 b z 1 c z 2 d z 2 e z

• LAG(col, n, DEFAULT)
  返回分組窗口內往上第n行col的值,n默認為1,往上第n沒有時返回DEFAULT（DEFAULT默認為NULL）

WITH tmp AS (SELECT 1 AS group_id, 'a' AS col UNION ALL SELECT 1 AS group_id, 'b' AS col UNION ALL SELECT 1 AS group_id, 'c' AS col UNION ALL SELECT 2 AS group_id, 'd' AS col UNION ALL SELECT 2 AS group_id, 'e' AS col ) SELECT group_id,col,LAG(col) over(partition by group_id order by col) as col_new FROM tmp;

等同于

WITH tmp AS (SELECT 1 AS group_id, 'a' AS col UNION ALL SELECT 1 AS group_id, 'b' AS col UNION ALL SELECT 1 AS group_id, 'c' AS col UNION ALL SELECT 2 AS group_id, 'd' AS col UNION ALL SELECT 2 AS group_id, 'e' AS col ) SELECT group_id,col,FIRST_VALUE(col) over(partition by group_id order by col rows BETWEEN 1 PRECEDING and 1 PRECEDING) as col_new FROM tmp;

返回結果都是

group_id col col_new 1 a NULL 1 b a 1 c b 2 d NULL 2 e d

使用默認值

WITH tmp AS (SELECT 1 AS group_id, 'a' AS col UNION ALL SELECT 1 AS group_id, 'b' AS col UNION ALL SELECT 1 AS group_id, 'c' AS col UNION ALL SELECT 2 AS group_id, 'd' AS col UNION ALL SELECT 2 AS group_id, 'e' AS col ) SELECT group_id,col,LAG(col, 2, 'zz') over(partition by group_id order by col) as col_new FROM tmp;

返回結果

group_id col col_new 1 a zz 1 b zz 1 c a 2 d zz 2 e zz

OVER詳解 The OVER clause

** FUNCTION(expr) OVER([PARTITION BY statement] [ORDER BY statement] [window clause]) ** 中括號為可選參數 FUNCTION：包括標準聚合函數(COUNT/SUM/MIN/MAX/AVG)和一些分析函數(RANK/ROW_NUMBER/DENSE_RANK等) PARTITION BY：可以由一個或者多個列組成 ORDER BY：可以由一個或者多個列組成 window clause:(ROWS | RANGE) BETWEEN (UNBOUNDED PRECEDING | num PRECEDING | CURRENT ROW) AND (UNBOUNDED PRECEDING | num PRECEDING | CURRENT ROW) 當window clause 未指定時默認為RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW,即分組內第一行至當前行作為窗口 當 window clause和ORDER BY都未指定時,默認為ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING **即分組內第一行至最后一行作為窗口.**

標準聚合函數

COUNT(expr) OVER() 返回窗口內行數 WITH tmp AS (SELECT 1 AS group_id, 'a' AS col UNION ALL SELECT 1 AS group_id, 'b' AS col UNION ALL SELECT 1 AS group_id, 'c' AS col UNION ALL SELECT 2 AS group_id, 'e' AS col UNION ALL SELECT 2 AS group_id, 'e' AS col ) SELECT group_id,col,count(col) over(partition by group_id) as cnt1,count(col) over(partition by group_id order by col) as cnt2,count(col) over(partition by group_id order by col rows between CURRENT ROW and UNBOUNDED following) as cnt3,count(distinct col) over(partition by group_id order by col rows between CURRENT ROW and UNBOUNDED following) as cnt4 FROM tmp; 返回結果為 group_id col cnt1 cnt2 cnt3 cnt4 1 a 3 1 3 3 1 b 3 2 2 2 1 c 3 3 1 1 2 e 2 2 2 1 2 e 2 2 1 1SUM(expr) OVER() 返回窗口內求和值 WITH tmp AS (SELECT 1 AS group_id, 1 AS col UNION ALL SELECT 1 AS group_id, 2 AS col UNION ALL SELECT 1 AS group_id, 3 AS col UNION ALL SELECT 2 AS group_id, 4 AS col UNION ALL SELECT 2 AS group_id, 4 AS col ) SELECT group_id,col,SUM(col) over(partition by group_id) as sum1,SUM(col) over(partition by group_id order by col) as sum2,SUM(col) over(partition by group_id order by col rows between CURRENT ROW and UNBOUNDED following) as sum3,SUM(distinct col) over(partition by group_id order by col rows between CURRENT ROW and UNBOUNDED following) as sum4 FROM tmp; 返回結果為 group_id col sum1 sum2 sum3 sum4 1 1 6 1 6 6 1 2 6 3 5 5 1 3 6 6 3 3 2 4 8 8 8 4 2 4 8 8 4 4MIN(expr) OVER() 返回窗口內最小值 WITH tmp AS (SELECT 1 AS group_id, 1 AS col UNION ALL SELECT 1 AS group_id, 2 AS col UNION ALL SELECT 1 AS group_id, 3 AS col UNION ALL SELECT 2 AS group_id, 4 AS col UNION ALL SELECT 2 AS group_id, 5 AS col ) SELECT group_id,col,MIN(col) over(partition by group_id) as min1,MIN(col) over(partition by group_id order by col) as min2,MIN(col) over(partition by group_id order by col rows between CURRENT ROW and UNBOUNDED following) as min3 FROM tmp; group_id col min1 min2 min3 1 1 1 1 1 1 2 1 1 2 1 3 1 1 3 2 4 4 4 4 2 5 4 4 5MAX(expr) OVER() 返回窗口內最大值 WITH tmp AS (SELECT 1 AS group_id, 1 AS col UNION ALL SELECT 1 AS group_id, 2 AS col UNION ALL SELECT 1 AS group_id, 3 AS col UNION ALL SELECT 2 AS group_id, 4 AS col UNION ALL SELECT 2 AS group_id, 5 AS col ) SELECT group_id,col,MAX(col) over(partition by group_id) as max1,MAX(col) over(partition by group_id order by col) as max2,MAX(col) over(partition by group_id order by col rows between CURRENT ROW and UNBOUNDED following) as max3 FROM tmp; 返回結果為 group_id col max1 max2 max3 1 1 3 1 3 1 2 3 2 3 1 3 3 3 3 2 4 5 4 5 2 5 5 5 5AVG(expr) OVER() 返回窗口內平均值 WITH tmp AS (SELECT 1 AS group_id, 1 AS col UNION ALL SELECT 1 AS group_id, 2 AS col UNION ALL SELECT 1 AS group_id, 3 AS col UNION ALL SELECT 2 AS group_id, 4 AS col UNION ALL SELECT 2 AS group_id, 4 AS col ) SELECT group_id,col,AVG(col) over(partition by group_id) as avg1,AVG(col) over(partition by group_id order by col) as avg2,AVG(col) over(partition by group_id order by col rows between CURRENT ROW and UNBOUNDED following) as avg3,AVG(distinct col) over(partition by group_id order by col rows between CURRENT ROW and UNBOUNDED following) as avg4 FROM tmp; 返回結果為 |group_id|col|avg1|avg2|avg3|avg4| |1|1|2.0|1.0|2.0|2.0| |1|2|2.0|1.5|2.5|2.5| |1|3|2.0|2.0|3.0|3.0| |2|4|4.0|4.0|4.0|4.0| |2|4|4.0|4.0|4.0|4.0|分析函數 Analytics functions RANK() OVER() 返回分組內排名（不支持自定義窗口） WITH tmp AS (SELECT 1 AS group_id, 1 AS col UNION ALL SELECT 1 AS group_id, 3 AS col UNION ALL SELECT 1 AS group_id, 3 AS col UNION ALL SELECT 2 AS group_id, 4 AS col UNION ALL SELECT 2 AS group_id, 5 AS col ) SELECT group_id,col,RANK() over(partition by group_id order by col desc) as r FROM tmp; 返回結果為 |group_id|col|r| |1|3|1| |1|3|1| |1|1|3| |2|5|1| |2|4|2|ROW_NUMBER() OVER() 返回分組內行號（不支持自定義窗口） WITH tmp AS (SELECT 1 AS group_id, 1 AS col UNION ALL SELECT 1 AS group_id, 3 AS col UNION ALL SELECT 1 AS group_id, 3 AS col UNION ALL SELECT 2 AS group_id, 4 AS col UNION ALL SELECT 2 AS group_id, 5 AS col ) SELECT group_id,col,ROW_NUMBER() over(partition by group_id order by col desc) as r FROM tmp; 返回結果為 |group_id|col|r| |1|3|1| |1|3|2| |1|1|3| |2|5|1| |2|4|2|DENSE_RANK() OVER() 返回分組內排名（排名相等不會留下空位,不支持自定義窗口） WITH tmp AS (SELECT 1 AS group_id, 1 AS col UNION ALL SELECT 1 AS group_id, 3 AS col UNION ALL SELECT 1 AS group_id, 3 AS col UNION ALL SELECT 2 AS group_id, 4 AS col UNION ALL SELECT 2 AS group_id, 5 AS col ) SELECT group_id,col,DENSE_RANK() over(partition by group_id order by col desc) as r FROM tmp; 返回結果為 |group_id|col|r| |1|3|1| |1|3|1| |1|1|2| |2|5|1| |2|4|2|CUME_DIST() OVER() 返回分組內累計分布值,即分組內小于(或者大于)等于當前值行數/分組內總行數 WITH tmp AS (SELECT 1 AS group_id, 1 AS col UNION ALL SELECT 1 AS group_id, 3 AS col UNION ALL SELECT 1 AS group_id, 3 AS col UNION ALL SELECT 2 AS group_id, 4 AS col UNION ALL SELECT 2 AS group_id, 5 AS col ) SELECT group_id,col,CUME_DIST() over(partition by group_id order by col asc) as d1,CUME_DIST() over(partition by group_id order by col desc) as d2 FROM tmp;返回結果為 |group_id|col|d1|d2| |1|3|1.0|0.6666666666666666| |1|3|1.0|0.6666666666666666| |1|1|0.3333333333333333|1.0| |2|5|1.0|0.5| |2|4|0.5|1.0|PERCENT_RANK() OVER() 返回百分比排序值,即分組內當前行的RANK值-1/分組內總行數-1 WITH tmp AS (SELECT 1 AS group_id, 1 AS col UNION ALL SELECT 1 AS group_id, 3 AS col UNION ALL SELECT 1 AS group_id, 3 AS col UNION ALL SELECT 2 AS group_id, 4 AS col UNION ALL SELECT 2 AS group_id, 5 AS col ) SELECT group_id,col,RANK() over(partition by group_id order by col asc) as r1,PERCENT_RANK() over(partition by group_id order by col asc) as p1,RANK() over(partition by group_id order by col desc) as r2,PERCENT_RANK() over(partition by group_id order by col desc) as p2 FROM tmp;返回結果為 |group_id|col|r1|p1|r2|p2| |1|3|2|0.5|1|0.0| |1|3|2|0.5|1|0.0| |1|1|1|0.0|3|1.0| |2|5|2|1.0|1|0.0| |2|4|1|0.0|2|1.0|NTILE(INTEGER x) OVER() 返回分區編號（將有序分區劃分為x個組,稱為bucket,并為分區中的每一行分配一個bucket編號） WITH tmp AS (SELECT 1 AS group_id, 1 AS col UNION ALL SELECT 1 AS group_id, 3 AS col UNION ALL SELECT 1 AS group_id, 3 AS col UNION ALL SELECT 1 AS group_id, 3 AS col UNION ALL SELECT 2 AS group_id, 4 AS col UNION ALL SELECT 2 AS group_id, 5 AS col ) SELECT group_id,col,NTILE(2) over(partition by group_id order by col asc) as bucket_id FROM tmp;返回結果為 |group_id|col|bucket_id| |1|1|1| |1|3|1| |1|3|2| |1|3|2| |2|4|1| |2|5|2| OVER子句也支持聚合函數 Hive 2.1.0及之后版本,OVER子句也支持聚合函數,如： WITH tmp AS (SELECT 1 AS group_id, 1 AS col UNION ALL SELECT 1 AS group_id, 3 AS col UNION ALL SELECT 1 AS group_id, 3 AS col UNION ALL SELECT 2 AS group_id, 4 AS col UNION ALL SELECT 2 AS group_id, 5 AS col ) SELECT group_id,RANK() over(order by sum(col) desc) as r FROM tmp group by group_id; 結果為 |group_id|r| |2|1| |1|2|window clause 的另一種寫法 將window子句寫在from后面,在over后使用別名進行引用,如下: WITH tmp AS (SELECT 1 AS group_id, 1 AS col UNION ALL SELECT 1 AS group_id, 2 AS col UNION ALL SELECT 1 AS group_id, 3 AS col UNION ALL SELECT 2 AS group_id, 4 AS col UNION ALL SELECT 2 AS group_id, 4 AS col ) SELECT group_id,col,AVG(col) over w1 as avg1,AVG(distinct col) over(partition by group_id order by col rows between CURRENT ROW and UNBOUNDED following) as avg2 FROM tmp WINDOW w1 AS (partition by group_id order by col rows between CURRENT ROW and UNBOUNDED following);結果為 |group_id|col|avg1|avg2| |1|1|2.0|2.0| |1|2|2.5|2.5| |1|3|3.0|3.0| |2|4|4.0|4.0| |2|4|4.0|4.0|WITH tmp AS (SELECT 1 AS group_id, 1 AS col UNION ALL SELECT 1 AS group_id, 2 AS col UNION ALL SELECT 1 AS group_id, 3 AS col UNION ALL SELECT 2 AS group_id, 4 AS col UNION ALL SELECT 2 AS group_id, 4 AS col ) SELECT group_id,col,AVG(col) over w1 as avg1,AVG(distinct col) over w2 as avg2 FROM tmp WINDOW w1 AS (partition by group_id order by col rows between CURRENT ROW and UNBOUNDED following), w2 AS (partition by group_id order by col rows between CURRENT ROW and UNBOUNDED following);結果為 |group_id|col|avg1|avg2| |1|1|2.0|2.0| |1|2|2.5|2.5| |1|3|3.0|3.0| |2|4|4.0|4.0| |2|4|4.0|4.0|

本文完.
Any suggestions and criticisms will be sincerely welcomed.
資料

https://blog.csdn.net/happyrocking/article/details/105369558
https://docs.aws.amazon.com/redshift/latest/dg/redshift
https://www.jianshu.com/p/3f3cf58472ca
https://www.cnblogs.com/hyunbar/p/13524855.html
https://blog.csdn.net/weixin_42307036/article/details/112381387

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