Hive 高级应用及分析窗口函数

Hive高级应用
一、支持复杂数据类型
array，map，struct
支持对应复杂数据类型的遍历和查询

二、支持视图

三、函数
3.一、丰富的内置函数
3.二、支持自定义Java处理类，以jar文件的方式添加至Hive，定义临时函数关联处理类，对数据进行自定义处理
3.三、Json数据的解析和操做get_json_object,json_tuple
3.三、经过Transform在HQL中调用自定义脚本如Python
3.四、分析窗口函数
a.sum,avg,min,max窗口内聚合分析
over (partition by col1 order by col2 rows between unbounded[n] preceding and current row[n following])
若是不指定ROWS BETWEEN,默认为从起点到当前行;
若是不指定ORDER BY，则将分组内全部值累加;
关键是理解ROWS BETWEEN含义,也叫作WINDOW子句：
PRECEDING：往前
FOLLOWING：日后
CURRENT ROW：当前行
UNBOUNDED：起点，
UNBOUNDED PRECEDING 表示从前面的起点，
UNBOUNDED FOLLOWING：表示到后面的终点
b.Ntile,row_number,ran,dense_ran
NTILE(n) 用于将分组数据按照顺序切分红n片，返回当前切片值
ROW_NUMBER() 从1开始，按照顺序，生成分组内记录的序列，无重复
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335
DENSE_RANK() 生成数据项在分组中的排名，排名相等会在名次中不会留下空位，334
c.cume_dist,percent_rank
CUME_DIST ：小于等于当前值的行数/分组内总行数
PERCENT_RANK ：分组内当前行的RANK值-1/分组内总行数-1
d.lag,lead,first_value,last_value
LAG(col,n,DEFAULT) 用于统计窗口内往上第n行值
LEAD(col,n,DEFAULT) 用于统计窗口内往下第n行值
first_value(col1) over (partition by col2 order by col3)取分组内排序后，截止到当前行，第一个值
last_value(col1) over (partition by col2 order by col3)取分组内排序后，截止到当前行，最后一个值
e.grouping sets,grouping_id,cube,rollup 经常使用于OLAP
grouping sets,grouping_id 将GROUP BY分组字段各个进行聚合，最终结果合并一块
cube 将GROUP BY分组字段全部组合的聚合
rollup 将GROUP BY分组字段层级组合的聚合

grouping sets (group by columns list):column list 不一样组合
grouping__id:给不一样集合编号
eg:
select month,day,count(distinct cookieid) as uv,grouping__id
from cookie5
group by month,day
grouping sets (month,day[,month,day])
order by grouping__id;

cube: with cube 根据group by的维度的全部组合进行聚合
eg:
select month,day,count(distinct cookieid) as uv,grouping__id
from cookie5
group by month,day
with cube
order by grouping__id; 

rollup: with rollup 根据group by的维度顺序逐层组合聚合
eg:
select month,day,count(distinct cookieid) as uv,grouping__id
from cookie5
group by month,day
with rollup
order by grouping__id; 

lag(column,n,default):统计窗口内取前n行值，窗口内错行显示
lead(column,n,default):窗口内取后n行值，窗口内错行显示
eg:
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 front_1_time, 
  LEAD(createtime,2,'2018-12-24 00:00:00') over (partition by cookieid order by createtime) as behind_2_time 
from  cookie4;

first_value(column):窗口内，排序第一个值（倒排序即最后一个值）
last_value(column):窗口内排序截至当前行的最后一个值，即该列值
select  cookieid, createtime, url, 
  row_number() over (partition by cookieid order by createtime) as rn, 
  first_value(url) over (partition by cookieid order by createtime) as first1,
  first_value(url) over (partition by cookieid order by createtime desc) as last1,
  last_value(url) over (partition by cookieid order by createtime) as last2 
from cookie4;

CUME_DIST():小于等于当前值的行数/分组内总行数
PERCENT_RANK():分组内当前行的RANK值-1/分组内总行数-1
eg:
select  dept, userid, sal,
  cume_dist() over (order by sal) as rn1,
  cume_dist() over (partition by dept order by sal) as rn2
from cookie3;
select  dept, userid, sal,
  percent_rank() over (order by sal) as rn1, --分组内
  rank() over (order by sal) as rn11, --分组内的rank值
  sum(1) over (partition by null) as rn12, --分组内总行数
  percent_rank() over (partition by dept order by sal) as rn2,
  rank() over (partition by dept order by sal) as rn21,
  sum(1) over (partition by dept) as rn22 
from cookie3;

ntile(n):将窗口内的数据切成n片，窗口内分块
row_number():从1开始窗口内记录的序列
rank():窗口内记录的排名，335
dense_rank():窗口内记录的排名，334
eg:
select cookieid, createtime, pv,
  ntile(2) over (partition by cookieid order by createtime) as rn1,
  row_number() over (partition by cookieid order by pv desc) as rn2,
  rank() over (partition by cookieid order by pv desc) as rn3,
  dense_rank() over (partition by cookieid order by pv desc) as rn4
from  cookie2 
order by cookieid,createtime;

sum|avg|min|max(column) over(partition by col1 order by col2 rows between n|unbounded preceding current row and n|unbounded following current row):窗口内记录的聚合，自由定义窗口聚合范围
eg:
select cookieid,createtime,pv, 
   sum(pv) over (partition by cookieid order by createtime rows between unbounded preceding and current row) as pv1, -- 默认为从起点到当前行
   avg(pv) over (partition by cookieid order by createtime) as pv2,                                                  -- 从起点到当前行
   max(pv) over (partition by cookieid) as pv3,                                                                      -- 分组内全部行
   min(pv) over (partition by cookieid order by createtime rows between 3 preceding and current row) as pv4,         -- 当前行+往前3行
   sum(pv) over (partition by cookieid order by createtime rows between 3 preceding and 1 following) as pv5,         -- 当前行+往前3行+日后1行
   avg(pv) over (partition by cookieid order by createtime rows between current row and unbounded following) as pv6  -- 当前行+日后全部行
from cookie1;

四、特殊分隔符处理，regexserde正则表达式解析，自定义inputformat处理

lateral view explode

lateral view侧视图用于和split、explode等UDTF一块儿使用的，能将一行数据拆分红多行数据，在此基础上能够对拆分的数据进行聚合，lateral view首先为原始表的每行调用UDTF，UDTF会把一行拆分红一行或者多行，lateral view在把结果组合，产生一个支持别名表的虚拟表。
lateral clause 至关于一个虚拟表，与原表explode_lateral_view笛卡尔积关联。
explode不能写在别的函数内