整理两个row_number() over()的例子_rownumber over partition by用两次-优快云博客

本文链接：https://blog.youkuaiyun.com/redhat1986/article/details/84760559

本文提供两个row_number() over()的使用示例。首先展示了一个查询每个学期每门功课最高分的情景，接着介绍了如何在分组学期的基础上，筛选出语文成绩90分以上且对应用户数学科目记录的场景。涉及到的数据操作包括建表、数据加载、分组和过滤。

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准备数据如下

1|zhangzhan|shuxue|80|2015
2|lisi     |yuwen |92|2016
3|wangwu   |huaxue|70|2017
4|zhangsan |yuwen |80|2015
5|zhangsan |huaxue|90|2015
6|lisi     |shuxue|85|2015
7|wangwu   |yuwen |99|2016
8|zhangsan |huaxue|80|2017
9|wangwu   |yuwen |90|2016

建表语句


create   table IF NOT EXISTS course_score
( 
id                       string, 
userid                string,
course               string ,
score                 int ,
term                  string
)

ROW FORMAT DELIMITED FIELDS TERMINATED BY '|'
stored as textfile
;

加载数据并查看原始数据

hive> load data local inpath './s.txt' overwrite into table course_score;
Loading data to table default.course_score
Table default.course_score stats: [numFiles=1, totalSize=243]
OK
Time taken: 0.463 seconds
hive> select * from course_score;
OK
1       zhangzhan       shuxue  80      2015
2       lisi            yuwen   92      2016
3       wangwu          huaxue  70      2017
4       zhangsan        yuwen   80      2015
5       zhangsan        huaxue  90      2015
6       lisi            shuxue  85      2015
7       wangwu          yuwen   99      2016
8       zhangsan        huaxue  80      2017
9       wangwu          yuwen   90      2016
Time taken: 0.193 seconds, Fetched: 9 row(s)

每个学期每门功课分数

    >  select  * ,row_number() over(partition by course,term order by score desc) rn  from course_score ;
Query ID = hdfs_20181203130505_05b33809-9aff-4cc6-abd6-bf4ee34dd54b
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
  set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
  set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
  set mapreduce.job.reduces=<number>
Kill Command = /opt/cloudera/parcels/CDH-5.14.2-1.cdh5.14.2.p0.3/lib/hadoop/bin/hadoop job  -kill job_1541413124518_0075
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
2018-12-03 13:05:38,842 Stage-1 map = 0%,  reduce = 0%
2018-12-03 13:05:44,037 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.98 sec
2018-1