窗口函数与分析函数
在sql中有一类函数叫做聚合函数，例如sum(),avg(),max()，这类函数可以将多行数据按照规则聚集为一行，一般来说聚集后的行数是要少于聚集前的行数的。但是有时想要既显示聚集前的数据，又要显示聚集后的数据，这时我们引入了窗口函数。窗口函数又叫OLAP函数/分析函数，窗口函数兼具分组和排序功能
窗口函数最重要的关键字是partition by和order by
具体语法如下：over (partition by xxx order by xxx)
sum、avg、min、max
准备数据
建表语句
create table test_1(cookieid string,createtime string,pv int) row format delimited fields terminated by ',';
加载数据
load data local inpath '/root/hivedata/test_1.dat' into table test_1;
cookie1,2020-04-10,1
cookie1,2020-04-11,5
cookie1,2020-04-12,7
cookie1,2020-04-13,3
cookie1,2020-04-14,2
cookie1,2020-04-15,4
cookie1,2020-04-16,4开启智能本地模式
set hive.exec.mode.local.auto = true;SUM函数和窗口函数的配合使用：结果和order by 相关，默认为升序
pv1：分组内从起点到当前行的pv累加
select cookieid,createtime,pv,sum(pv) over (partition by cookieid order by createtime) as pv1 from test_1;pv2：同pv1
select cookieid,createtime,pv,sum(pv) over (partition by cookieid order by createtime rows between unbounded preceding and current row) as pv2 from test_1;pv3：分组内cookieid1所有的pv累加
select cookieid,createtime,pv,sum(pv) over(partittion by cookieid) as pv3 from test_1;pv4：分组内当前行+往前3行
select cookieid,createtime,pv,sum(pv) over (partition by cookieid order by createtime rows between 3 preceding and current row) as pv4 from test_1;pv5：分组内当前行+往前3行+往后1行
select cookieid,createtime,pv,sum(pv) over(partition by cookieid order by createtime rows between 3 preceding and 1 following) as pv5 from test_1;pv6：分组内当前行+往后所有行
select cookieid,createtime,pv,sum(pv) over (partition by cookieid order by createtime rows between current row and unbounded following) as pv6 from test_1;如果不指定rows between,默认为从起点到当前行
如果不指定order by 则将分组内所有值累加
关键是理解rows between含义，也叫作window子句
preceding：往前
following：往后
current row：当前
unbounded：起点
unbounded preceding：表示从前面的起点
unbounded following：表示到后面的终点
AVG,MIN,MAX,和SUM的用法一样
row_number、rank、dense_rank、ntile
准备数据
cookie1,2020-04-10,1
cookie1,2020-04-11,5
cookie1,2020-04-12,7
cookie1,2020-04-13,3
cookie1,2020-04-14,2
cookie1,2020-04-15,4
cookie1,2020-04-16,4
cookie2,2020-04-10,2
cookie2,2020-04-11,3
cookie2,2020-04-12,5
cookie2,2020-04-13,6
cookie2,2020-04-14,3
cookie2,2020-04-15,9
cookie2,2020-04-16,7
create table test_2(cookieid string,createtime string,pv int) row format delimited fields terminated by ',' stored as testfile;
加载数据
load data local inpath '/root/hivedata/test_2.dat' into table test_2;- ROW_NUMBER()使用
row_number()从1开始，按照顺序，生成分组内记录的序列
select cookieid,createtime,pv,row_number() over(partition by cookieid order by pv desc) as rn from test_2;- RANK和DENSE_RANK使用
RANK()生成数据项在分组中的排名，排名相等会在名次中留下空位 1224
DENSE_RANK()生成数据项在分组中的排名，排名相等不会留下空位12234
select cookieid,createtime,pv,rank() over (partition by cookieid order by pv desc) as rn1,
dense_rank() over (partition by cookieid order by pv desc) as rn2,
row_number() over (partition by cookidid order by pv desc) as rn3 from test_2 where cookieid = 'cookie1';- NTILE
有时会有这样的需求：如果数据排序后分为三部分，业务人员只关心其中一部分，如何将这中间的三分之一数据拿出来呢？NTILE可以满足
ntile可以看成是：把有序的数据集合平均分配到指定的数量（num）个桶中，将桶号分配给每一行。如果不能平均分配，则优先分配较小编号的桶，并且每个桶中能放的行数最多差1
然后可以根据桶号，选取前或后n分之几的数据。数据就回完整展示出来，知识给相应的数据打标签，具体要取几分之几的数据，需要再嵌套一层根据标签取出
select cookieid,createtime,pv,
ntile(2) over (partition by cookieid order by createtime )as rn1,
ntile(3) over (partition by cookieid order by createtime ) as rn2,
ntile(4) over(order by createtime) as rn3
from test_2 order by cookieid ,createtime;其他窗口函数
lag、lead、first_value、last_value
- LAG
LAG(col,n,DEFAULT) 用于统计窗口内往上第几行值
第一个参数为列名，第二个参数为往上第n行（可选，默认为1），第三个参数为默认值（当往上第n行为NULL时候，取默认值，如不指定，则为null
select cookieid,createtime,url, row_number() over (partition by cookieid order by createtime) as rn,
lag(createtime,1,'1970-01-01 00:00:00') over (partition by cookieid order by createtime) as last_1_time,
lag(createtime,2) over (partition by cookieid order by createtime) as last_2_time from test_4;last_1_time: 指定了往上第1行的值，default为'1970-01-01 00:00:00'
cookie1第一行，往上1行为NULL,因此取默认值 1970-01-01 00:00:00
cookie1第三行，往上1行值为第二行值，2015-04-10 10:00:02
cookie1第六行，往上1行值为第五行值，2015-04-10 10:50:01
last_2_time: 指定了往上第2行的值，为指定默认值
cookie1第一行，往上2行为NULL
cookie1第二行，往上2行为NULL
cookie1第四行，往上2行为第二行值，2015-04-10 10:00:02
cookie1第七行，往上2行为第五行值，2015-04-10 10:50:01
- LEAD
与LAG相反LEAD(col,n,DEFAULT)用于统计窗口内往下第n行值
第一个参数为列名，第二个参数为往下第n行（可选，默认为1），第三个参数为默认值（当往下第n行为Null时，取默认值，如不指定则为null
- FIRST_VALUE
取分组内排序后，截止到当前行，第一个值
- LAST_VALUE
取分组内排序后，截止到当前行，最后一个值
cume_dist，percent_rank
这两个序列分析函数不是很常用，
注意：序列函数不支持WINDOW子句
数据准备
d1,user1,1000
d1,user2,2000
d1,user3,3000
d2,user4,4000
d2,user5,5000
create external table test_t3 (dept STRING,userid string,sal int) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' stored as textfile;
加载数据：load data local inpath '/root/hivedata/test_t3.dat' into table test_t3;- cume_dist
CUME_DIST：小于等于当前值的行数/分组内总行数 order默认升序
比如：统计小于等于当前薪水的人数所占总人数的比例
select dept,userid,sal, CUME_DIST() OVER(ORDER BY sal) AS rn1, CUME_DIST() OVER(PARTITION BY dept ORDER BY sal) AS rn2 FROM test_t3;rn1: 没有partition,所有数据均为1组，总行数为5，
第一行：小于等于1000的行数为1，因此，1/5=0.2
第三行：小于等于3000的行数为3，因此，3/5=0.6
rn2: 按照部门分组，dpet=d1的行数为3,
第二行：小于等于2000的行数为2，因此，2/3=0.6666666666666666
- PERCENT_RANK
percent_rank：分组内当前行的RANK值-1/分组内总行数-1
grouping sets,grouping_id,cube,rollup
这几个分析函数通常用于OLAP中，不能累加，而且需要根据不同维度上钻和下钻的指标统计。比如分小时、天、月的UV数
数据准备
2020-03,2020-03-10,cookie1
2020-03,2020-03-10,cookie5
2020-03,2020-03-12,cookie7
2020-04,2020-04-12,cookie3
2020-04,2020-04-13,cookie2
2020-04,2020-04-13,cookie4
2020-04,2020-04-16,cookie4
2020-03,2020-03-10,cookie2
2020-03,2020-03-10,cookie3
2020-04,2020-04-12,cookie5
2020-04,2020-04-13,cookie6
2020-04,2020-04-15,cookie3
2020-04,2020-04-15,cookie2
2020-04,2020-04-16,cookie1
CREATE TABLE test_t5 (month STRING,day STRING, cookieid STRING ) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' stored as textfile;
加载数据：load data local inpath '/root/hivedata/test_t5.dat' into table test_t5;- GROUPING SETS
grouping sets是一种将多个group by逻辑写在一个sql语句中的便利写法
等价于将不同维度的GROUP BY结果进行union all
GROUPING_ID 表示结果属于哪一个分组集合
SELECT month,day,COUNT(DISTINCT cookieid) AS uv,GROUPING_ID FROM test_t5 GROUP BY month,day GROUPING SETS (month,day) ORDER BY GROUPINGID;grouping_id表示这一组结果属于哪个分组集合，根据grouping sets中的分组条件month，day，1是代表month，2是代表day
等价于
SELECT month,NULL,COUNT(DISTINCT cookieid) AS uv,1 AS GROUPING_ID FROM test_t5 GROUP BY month UNION ALL SELECT NULL as month,day,COUNT(DISTINCT cookieid) AS uv,2 AS GROUPING_ID FROM test_t5 GROUP BY day;- CUBE
根据GROUP BY的维度的所有组合进行聚合
SELECT month,day,COUNT(DISTINCT cookieid) AS uv,GROUPING_ID FROM test_t5 GROUP BY month,day WITH CUBE ORDER BY GROUPING_ID;等价于
SELECT NULL,NULL,COUNT(DISTINCT cookieid) AS uv,0 AS GROUPING_ID FROM test_t5UNION ALL SELECT month,NULL,COUNT(DISTINCT cookieid) AS uv,1 AS GROUPING_ID FROM test_t5 GROUP BY month UNION ALL SELECT NULL,day,COUNT(DISTINCT cookieid) AS uv,2 AS GROUPING_ID FROM test_t5 GROUP BY dayUNION ALL SELECT month,day,COUNT(DISTINCT cookieid) AS uv,3 AS GROUPING_ID FROM test_t5 GROUP BY month,day;- ROLLUP
是CUBE的子集，以最左侧的维度为主，从该维度进行层级聚合
比如，以month维度进行层级聚合：SELECT month,day,COUNT(DISTINCT cookieid) AS uv,GROUPING_IDFROM test_t5 GROUP BY month,dayWITH ROLLUP ORDER BY GROUPING_ID;
--把month和day调换顺序，则以day维度进行层级聚合：
SELECT day,month,COUNT(DISTINCT cookieid) AS uv,GROUPING_IDFROM test_t5 GROUP BY day,month WITH ROLLUP ORDER BY GROUPING_ID;（这里，根据天和月进行聚合，和根据天聚合结果一样，因为有父子关系，如果是其他维度组合的话，就会不一样）
