HIve高级函数

sum与over实现定制sum

               //查询当前行和下两行score总分的数据
               select name, score, sum(score)over(order by name rows between current row and 2 following) from window_test;

               //查询当前行和上两行score总分的数据
               select name, score, sum(score)over(order by name rows between 2 preceding and current row) from window_test;

               //查询当前行和以下行score总分的数据
               select name, score, sum(score)over(order by name rows between current row and unbounded following) from window_test;

               //查询当前行和以上行score总分的数据
               select name, score, sum(score)over(order by name rows between unbounded preceding and current row) from window_test;

over排序

               //排序函数 每一个name分组后从1开始排序，相同的数据列序号相同，后一个加一
               select name, score, rank()over(partition by name order by score) from window_test;

               //排序函数 每一个name分组后从1开始排序，相同的数据列序号相同，后一个不加一
               select name, score, dense_rank()over(partition by name order by score) from window_test;

               //排序函数 每一个name分组后从1开始排序，相同的数据列序号不相同
               select name, score, row_number()over(partition by name order by score) from window_test;

组内最大值,最小值提取

               //分组内排正序取第一个score
               select name, score, first_value(score)over(partition by name order by score) from window_test;

               //分组内排正序取最后一个score
               select name, score, last_value(score)over(partition by name order by score range between unbounded preceding and unbounded following) from window_test;

               //函数的结果是错误的,必须要前面一大段
               //select name, score, last_value(score)over(partition by name order by score) from window_test;

数据动态等级划分

               //根据分数将数据分为三个区域

               select name, score, ntile(3)over(order by score) from window_test;

上一行,下一行值提取

               //下两行的score提过来,没有填充NULL
               select name, score, lead(score,2)over(partition by name order by score) from window_test;               

               //上两行的score提过来,没有填充NULL
               select name, score, lag(score,2)over(partition by name order by score) from window_test;

占比函数

               //小于当前值SCORE的人数占总人数的占比（name分组）
               select name, score, cume_dist()over(partition by name order by score) from window_test;

 行列转

               //行转列
               select name,concat_ws(‘,‘,collect_list(score)) as score_value from window_test group by name;

               //列转行
               select name,score,score_value from window_test lateral view explode(split(score,‘,‘))num as score_value;