(select *, row_number() over (partition by 要分组的列名 order by 要排序的列名 desc) as ranking from 表名) as a where rangking<=N; 1. 2. 3. 4. 4.聚合函数作为窗口函数 聚合函数作为窗口函数和专用窗口函数用法相同,只需要把聚合函数写在窗口函数的位置即可,但聚合函数括号里不能为空,必须写...
定义:ROW_NUMBER()函数作用就是将SELECT查询到的数据进行排序,每一条数据加一个序号 select * ,ROW_NUMBER() OVER (PARTITION BY ClassName ORDER BY SCORE DESC) 班内排序 ,ROW_NUMBER() OVER (ORDER BY SCORE DESC) AS 总排序 from Scores ; 1. 2. 3. 4. 5. RANK() select ROW_NUMBER() OVER (...
a,row_number()over(partition by a order by b desc)asrn from testdata2 上面的语句主要分两部分 window函数部分(row_number) 窗口定义部分(over) 代码语言:javascript 复制 select a,row_number()over(partition by a order by b desc)asrn from testdata2---unresolved logical plan---'Project ['a,...
首先可以在select查询时,使用row_number()函数 其次,row_number()函数后面先跟上over关键字 然后括号中是partition by也就是根据哪个字段进行分组 其次是可以用order by进行组内排序 然后row_number()就可以给每个组内的行,一个组内行号 RowNumberWindowFunc.scala package com.UDF.row_numberFUNC import org.apach...
一、rank() over(partition by ...order by) 解释:partition by用于给结果集分组,如果没有指定那么它把整个结果集作为一个分组。二、语法:ROW_NUMBER() OVER(PARTITION BY COLUMN ORDER BY COLUMN) 解释:partition...
row_number() over(partition by gender order by age) as rowNumber, rank() over(partition by gender order by age) as ranks, dense_rank() over(partition by gender order by age) as denseRank, percent_rank() over(partition by gender order by age) as percentRank from Affairs 1 2 3 4 5...
排序函数(列) OVER(选项),这里的选项可以是ORDER BY 子句,也可以是OVER(PARTITION BY 子句 ORDER BY 子句),但不可以是 PARTITION BY 子句。 2. 准备工作 进入到SparkShell命令行 代码语言:javascript 代码运行次数:0 复制 Cloud Studio代码运行 /export/servers/spark/bin/spark-shell--master spark://node01:...
3.1 ROW_NUMBER顺序排序 row_number() over(order by score) as rownum 表示按score 升序的方式来排序,并得出排序结果的序号 spark.sql("select name,class,score,row_number()over(order by score)rankfromscores").show() PartitionBy分组 spark.sql("select name,class,score,row_number()over(partition by...
row_number() over(order by score) as rownum 表示按score 升序的方式来排序,并得出排序结果的序号 spark.sql("select name,class,score,row_number()over(order by score)rankfromscores").show() PartitionBy分组 spark.sql("select name,class,score,row_number()over(partition byclassorde...
row_number()over(partiiton by xxx order by xxx[asc/desc])# 相同用户名只取年龄最大的那一个selectunamefrom(selectuname,row_number()over(partition by age desc)rnfromA)swherern=1# 相同数据中,只取时间戳最小的3条数据selectuidfrom(selectuid,row_number()over(partition by uid order by log_...