hive行转列

               selectt1.base,concat_ws('|', collect_set(t1.name)) namefrom(selectname,concat(constellation, "," , blood_type) basefromperson_info) t1group byt1.base;

hive列转行

select
movie,
category_name
from 
movie_info lateral view explode(category) table_tmp as category_name;

reflect调用Java自带函数

select reflect("java.lang.Math","max",col1,col2) from test_udf;
select reflect(class_name,method_name,col1,col2) from test_udf2;
select reflect("org.apache.commons.lang.math.NumberUtils","isNumber","123")

窗口函数

select 
user_id,
user_type,
sales,
--分组内所有行
sum(sales) over(partition by user_type) AS sales_1 ,
sum(sales) over(order  by user_type) AS sales_2 , 
--默认为从起点到当前行，如果sales相同，累加结果相同
sum(sales) over(partition by user_type order by sales asc) AS sales_3,
--从起点到当前行，结果与sales_3不同。 根据排序先后不同，可能结果累加不同
sum(sales) over(partition by user_type order by sales asc rows between unbounded preceding and current row) AS sales_4,
--当前行+往前3行 
sum(sales) over(partition by user_type order by sales asc rows between 3 preceding and current row) AS sales_5,
--当前行+往前3行+往后1行
sum(sales) over(partition by user_type order by sales asc rows between 3 preceding and 1 following) AS sales_6,
--当前行+往后所有行  
sum(sales) over(partition by user_type order by sales asc rows between current row and unbounded following) AS sales_7 
from 
order_detail 
order by user_type,sales,user_id;

分析函数

select user_id,user_type,sales,RANK() over (partition by user_type order by sales desc) as r,ROW_NUMBER() over (partition by user_type order by sales desc) as rn,DENSE_RANK() over (partition by user_type order by sales desc) as dr
fromorder_detail;   

cube聚合

selectuser_type,sales,count(user_id) as pv,GROUPING__ID 
from order_detail
group by user_type,sales
WITH CUBE 
ORDER BY GROUPING__ID;