hive left outer join的问题
最近BA用户反馈有两句看似很像的语句返回的结果数不一样,比较奇怪,怀疑是不是Hive的Bug
Query 1 返回结果数6071
select count(distinct reviewid) as dis_reviewcnt
from
(select a.reviewid
from bi.dpods_dp_reviewreport a
left outer join bi.dpods_dp_reviewlog b
on a.reviewid=b.reviewid and b.hp_statdate='2013-07-24'
where to_date(a.feedadddate) >= '2013-07-01' and a.hp_statdate='2013-07-24'
) a
Query 2 返回结果数6443
select count(distinct reviewid) as dis_reviewcnt
from
(select a.reviewid
from bi.dpods_dp_reviewreport a
left outer join bi.dpods_dp_reviewlog b
on a.reviewid=b.reviewid and b.hp_statdate='2013-07-24' and a.hp_statdate='2013-07-24'
where to_date(a.feedadddate) >= '2013-07-01'
) a
第二条query比第一条多了372条数据,而且在子查询的左表中并不存在
两条语句唯一的区别是dpods_dp_reviewreport的分区过滤条件(hp_statdate是partition column)一个在where后面,另一个在on后面
粗看感觉出来的数据应该是一样的,但是玄机其实就在where和on的区别。
where 后面跟的是过滤条件,query 1 中的a.hp_statdate='2013-07-24', 在table scan之前就会Partition Pruner 过滤分区,所以只有'2013-07-24'下的数据会和dpods_dp_reviewlog进行join。
而query 2中会读入所有partition下的数据,再和dpods_dp_reviewlog join,并且根据join的关联条件只有a.hp_statdate='2013-07-24'的时候才会真正执行join,其余情况下又由于是left outer join, join不上右面会留NULL,query 2中其实是取出了所有的reviewid,所以会和query 1 结果不一样
可以做一个实验,query2去掉on后面的a.hp_statdate='2013-07-24',其余不动,执行语句,出来的distinct reviewcnt 也是 6443
select count(distinct reviewid) as dis_reviewcnt
from
(select a.reviewid
from bi.dpods_dp_reviewreport a
left outer join bi.dpods_dp_reviewlog b
on a.reviewid=b.reviewid and b.hp_statdate='2013-07-24'
where to_date(a.feedadddate) >= '2013-07-01'
) a
query 1的query plan
ABSTRACT SYNTAX TREE:
(TOK_QUERY (TOK_FROM (TOK_SUBQUERY (TOK_QUERY (TOK_FROM (TOK_LEFTOUTERJOIN (TOK_TABREF (TOK_TABNAME bi dpods_dp_reviewreport) a) (TOK_TABREF (TOK_TABNAME bi dpods_dp_reviewlog) b) (and (= (. (TOK_TABLE_OR_COL a) reviewid) (. (TOK_TABLE_OR_COL b) reviewid)) (= (. (TOK_TABLE_OR_COL b) hp_statdate) '2013-07-24')))) (TOK_INSERT (TOK_DESTINATION (TOK_DIR TOK_TMP_FILE)) (TOK_SELECT (TOK_SELEXPR (. (TOK_TABLE_OR_COL a) reviewid))) (TOK_WHERE (and (>= (TOK_FUNCTION to_date (. (TOK_TABLE_OR_COL a) feedadddate)) '2013-07-01') (= (. (TOK_TABLE_OR_COL a) hp_statdate) '2013-07-24'))))) a)) (TOK_INSERT (TOK_DESTINATION (TOK_DIR TOK_TMP_FILE)) (TOK_SELECT (TOK_SELEXPR (TOK_FUNCTIONDI count (TOK_TABLE_OR_COL reviewid)) dis_reviewcnt)))) STAGE DEPENDENCIES:
Stage-5 is a root stage , consists of Stage-1
Stage-1
Stage-2 depends on stages: Stage-1
Stage-0 is a root stage STAGE PLANS:
Stage: Stage-5
Conditional Operator Stage: Stage-1
Map Reduce
Alias -> Map Operator Tree:
a:a
TableScan
alias: a
Filter Operator
predicate:
expr: (to_date(feedadddate) >= '2013-07-01')
type: boolean
Reduce Output Operator
key expressions:
expr: reviewid
type: int
sort order: +
Map-reduce partition columns:
expr: reviewid
type: int
tag: 0
value expressions:
expr: feedadddate
type: string
expr: reviewid
type: int
expr: hp_statdate
type: string
a:b
TableScan
alias: b
Reduce Output Operator
key expressions:
expr: reviewid
type: int
sort order: +
Map-reduce partition columns:
expr: reviewid
type: int
tag: 1
Reduce Operator Tree:
Join Operator
condition map:
Left Outer Join0 to 1
condition expressions:
0 {VALUE._col5} {VALUE._col8} {VALUE._col17}
1
handleSkewJoin: false
outputColumnNames: _col5, _col8, _col17
Select Operator
expressions:
expr: _col8
type: int
outputColumnNames: _col0
Select Operator
expressions:
expr: _col0
type: int
outputColumnNames: _col0
Group By Operator
aggregations:
expr: count(DISTINCT _col0)
bucketGroup: false
keys:
expr: _col0
type: int
mode: hash
outputColumnNames: _col0, _col1
File Output Operator
compressed: true
GlobalTableId: 0
table:
input format: org.apache.hadoop.mapred.SequenceFileInputFormat
output format: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat Stage: Stage-2
Map Reduce
Alias -> Map Operator Tree:
hdfs://10.2.6.102/tmp/hive-hadoop/hive_2013-07-26_18-10-59_408_7272696604651905662/-mr-10002
Reduce Output Operator
key expressions:
expr: _col0
type: int
sort order: +
tag: -1
value expressions:
expr: _col1
type: bigint
Reduce Operator Tree:
Group By Operator
aggregations:
expr: count(DISTINCT KEY._col0:0._col0)
bucketGroup: false
mode: mergepartial
outputColumnNames: _col0
Select Operator
expressions:
expr: _col0
type: bigint
outputColumnNames: _col0
File Output Operator
compressed: false
GlobalTableId: 0
table:
input format: org.apache.hadoop.mapred.TextInputFormat
output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat Stage: Stage-0
Fetch Operator
limit: -1
Query 2的query plan
ABSTRACT SYNTAX TREE:
(TOK_QUERY (TOK_FROM (TOK_SUBQUERY (TOK_QUERY (TOK_FROM (TOK_LEFTOUTERJOIN (TOK_TABREF (TOK_TABNAME bi dpods_dp_reviewreport) a) (TOK_TABREF (TOK_TABNAME bi dpods_dp_reviewlog) b) (and (and (= (. (TOK_TABLE_OR_COL a) reviewid) (. (TOK_TABLE_OR_COL b) reviewid)) (= (. (TOK_TABLE_OR_COL b) hp_statdate) '2013-07-24')) (= (. (TOK_TABLE_OR_COL a) hp_statdate) '2013-07-24')))) (TOK_INSERT (TOK_DESTINATION (TOK_DIR TOK_TMP_FILE)) (TOK_SELECT (TOK_SELEXPR (. (TOK_TABLE_OR_COL a) reviewid))) (TOK_WHERE (>= (TOK_FUNCTION to_date (. (TOK_TABLE_OR_COL a) feedadddate)) '2013-07-01')))) a)) (TOK_INSERT (TOK_DESTINATION (TOK_DIR TOK_TMP_FILE)) (TOK_SELECT (TOK_SELEXPR (TOK_FUNCTIONDI count (TOK_TABLE_OR_COL reviewid)) dis_reviewcnt)))) STAGE DEPENDENCIES:
Stage-5 is a root stage , consists of Stage-1
Stage-1
Stage-2 depends on stages: Stage-1
Stage-0 is a root stage STAGE PLANS:
Stage: Stage-5
Conditional Operator Stage: Stage-1
Map Reduce
Alias -> Map Operator Tree:
a:a
TableScan
alias: a
Filter Operator
predicate:
expr: (to_date(feedadddate) >= '2013-07-01')
type: boolean
Reduce Output Operator
key expressions:
expr: reviewid
type: int
sort order: +
Map-reduce partition columns:
expr: reviewid
type: int
tag: 0
value expressions:
expr: feedadddate
type: string
expr: reviewid
type: int
expr: hp_statdate
type: string
a:b
TableScan
alias: b
Reduce Output Operator
key expressions:
expr: reviewid
type: int
sort order: +
Map-reduce partition columns:
expr: reviewid
type: int
tag: 1
Reduce Operator Tree:
Join Operator
condition map:
Left Outer Join0 to 1
condition expressions:
0 {VALUE._col5} {VALUE._col8}
1
filter predicates:
0 {(VALUE._col17 = '2013-07-24')}
1
handleSkewJoin: false
outputColumnNames: _col5, _col8
Select Operator
expressions:
expr: _col8
type: int
outputColumnNames: _col0
Select Operator
expressions:
expr: _col0
type: int
outputColumnNames: _col0
Group By Operator
aggregations:
expr: count(DISTINCT _col0)
bucketGroup: false
keys:
expr: _col0
type: int
mode: hash
outputColumnNames: _col0, _col1
File Output Operator
compressed: true
GlobalTableId: 0
table:
input format: org.apache.hadoop.mapred.SequenceFileInputFormat
output format: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat Stage: Stage-2
Map Reduce
Alias -> Map Operator Tree:
hdfs://10.2.6.102/tmp/hive-hadoop/hive_2013-07-26_18-13-32_879_3623450294049807419/-mr-10002
Reduce Output Operator
key expressions:
expr: _col0
type: int
sort order: +
tag: -1
value expressions:
expr: _col1
type: bigint
Reduce Operator Tree:
Group By Operator
aggregations:
expr: count(DISTINCT KEY._col0:0._col0)
bucketGroup: false
mode: mergepartial
outputColumnNames: _col0
Select Operator
expressions:
expr: _col0
type: bigint
outputColumnNames: _col0
File Output Operator
compressed: false
GlobalTableId: 0
table:
input format: org.apache.hadoop.mapred.TextInputFormat
output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat Stage: Stage-0
Fetch Operator
limit: -1
参考:
http://blog.sina.com.cn/s/blog_6ff05a2c01010oxp.html
hive left outer join的问题的更多相关文章
- HIVE中join、semi join、outer join举例详解
转自 http://www.cnblogs.com/xd502djj/archive/2013/01/18/2866662.html 举例子: hive> select * from zz0; ...
- hive中left join、left outer join和left semi join的区别
先说结论,再举例子. hive中,left join与left outer join等价. left semi join与left outer join的区别:left semi join相当 ...
- HIVE中join、semi join、outer join
补充说明 left outer join where is not null与left semi join的联系与区别:两者均可实现exists in操作,不同的是,前者允许右表的字段在select或 ...
- hive 包含操作(left semi join)(left outer join = in)迪卡尔积
目前hive不支持 in或not in 中包含查询子句的语法,所以只能通过left join实现. 假设有一个登陆表login(当天登陆记录,只有一个uid),和一个用户注册表regusers(当天注 ...
- hive regex insert join group cli
1.insert Insert时,from子句既能够放在select子句后,也能够放在insert子句前,以下两句是等价的 hive> FROM invites a INSERT OVERWRI ...
- 一起学Hive——总结各种Join连接的用法
Hive支持常用的SQL join语句,例如内连接.左外连接.右外连接以及HiVe独有的map端连接.其中map端连接是用于优化Hive连接查询的一个重要技巧. 在介绍各种连接之前,先准备好表和数据. ...
- hive中的join
建表 : jdbc:hive2://localhost:10000> create database myjoin; No rows affected (3.78 seconds) : jdbc ...
- Oracle Partition Outer Join 稠化报表
partition outer join实现将稀疏数据转为稠密数据,举例: with t as (select deptno, job, sum(sal) sum_sal from emp group ...
- SQL Server 2008 R2——使用FULL OUTER JOIN实现多表信息汇总
=================================版权声明================================= 版权声明:原创文章 谢绝转载 请通过右侧公告中的“联系邮 ...
随机推荐
- javascript权威指南(2)
JavaScript预定义了一系列全局变量和函数,在自定义变量和函数式要避免使用这些预定义的名称: arguments encodeURI Infinity Number RegExp Arra ...
- 认识bash这个shell
我们通过shell将我们输入的命令与内核通信,好让内核可以控制硬件来正确无误地工作bash是我们Linux默认的shell 用户界面(Shell,application)--------核心(Kern ...
- vector查找元素
转自:http://hi.baidu.com/chain2008/blog/item/821744585e12c5c89c8204e8.html 今天又忘了怎么在vector中查找某一个值..唉..每 ...
- jQuery+PHP掷色子抽奖
原文 jQuery+PHP掷色子抽奖 本文以大富翁游戏为背景,综合运用jQuery和PHP知识,设计出以掷色子点数来达成抽奖的效果,当然抽奖概率是可控的,开发者可以将本实例稍作修改即可运用到网站中的抽 ...
- OR导致笛卡尔积
近期监控数据库,发现以下语句跑得很慢,原来运行计划走了导致笛卡尔积,来看以下语句: SQL> explain plan for 2 SELECT COUNT(*) 3 FROM "GD ...
- 初探Django Admin(一)
前面的文章记录了django项目的一些操作,插入数据部分是手动在shell中操作的,如果能有一个图形界面来管理我们的数据,那该多好~ Django已经想到大家会需要这个功能,通过简单的配置,就能使用d ...
- VS2015安装
VS2015安装 Secondary Installer Setup Failed求解决方案 看到微软最近的一系列变化,着实让我等兴奋不已.VS2015下载地址就不说了.先来记录一下微软的几个变化吧. ...
- C#将XML转换成JSON转换XML
原文:C#将XML转换成JSON转换XML using System; using System.Collections.Generic; using System.Linq; using Syste ...
- ASP.NET MVC + EF 利用存储过程读取大数据
ASP.NET MVC + EF 利用存储过程读取大数据,1亿数据测试很OK 看到本文的标题,相信你会忍不住进来看看! 没错,本文要讲的就是这个重量级的东西,这个不仅仅支持单表查询,更能支持连接查询, ...
- Mysql 嵌套游标添以及任意位置声明变量的方法
在写存储过程的时候,会遇到某个游标的筛选条件来自于 先前语句运行的结果,比较常见的方式是 再写一个存储过程,通过调用来完成 动态参数的配置, 或者使用 动态sql的功能,而这两种方式都不能很好的解决这 ...