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

  1. 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;  ...

  2. 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 ...

  3. 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 ...

  4. 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 ...

  5. 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 ...

  6. 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. ...

  7. hive中的join

    建表 : jdbc:hive2://localhost:10000> create database myjoin; No rows affected (3.78 seconds) : jdbc ...

  8. 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 ...

  9. 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

  1. LeetCode——Flatten Binary Tree to Linked List

    Given a binary tree, flatten it to a linked list in-place. For example, Given 1 / \ 2 5 / \ \ 3 4 6 ...

  2. HDU2586

    最近的共同祖先反复问的问题. #include <iostream> #include <algorithm> #include <vector> #include ...

  3. openSUSE 安装

    https://lug.ustc.edu.cn/sites/opensuse-guide/installation.php 开始 1. 简介2. 改用 GNU/Linux3. 获取 openSUSE4 ...

  4. 异步编程(Async和Await)的使用

    .net4.5新特性之异步编程(Async和Await)的使用 一.简介 首先来看看.net的发展中的各个阶段的特性:NET 与C# 的每个版本发布都是有一个“主题”.即:C#1.0托管代码→C#2. ...

  5. C#函数式编程-序列

    C#函数式编程之序列 过了许久的时间,终于趁闲暇的时间来继续将函数式编程这个专辑连载下去,这段时间开头是为IOS这个新方向做准备,将OC的教程写成了SWIFT版,当然我个人是支持Xamarin,但是我 ...

  6. hibernate之使用Annotation注解搭建项目

    之前开发都是使用xml配置来开发项目,开发起来特别繁琐 大家会发现通过注解大大简化了我们开发流程,使我们从繁琐的XML配置中解放出来. 第一步:新建一个javaweb项目.并将hibernate需要的 ...

  7. IOC 在Mvc中的使用

    IOC 在Mvc中的使用 IOC,是控制反转(Inversion of Control)的英文简写, 控制反转一般分为两种类型,依赖注入(Dependency Injection)和依赖查找(Depe ...

  8. SQL点滴28—一个简单的存储过程

    原文:SQL点滴28-一个简单的存储过程 在表中写入一条数据同事要向另外一个表中写入信息,所以会使用到事务.实际使用的时候还会一次向一个表中吸入多条数据,下面的存储过程,将字符串拆分成数组然后写入到表 ...

  9. ASP.Net使用母版页窗

    背景:每一个网页的基本框架结构类似: 浏览站点的时候会发现,好多站点中.每一个网页的基本框架都是一样的,比方,最上面都是站点的标题,中间是内容.最以下是站点的版权.开发提供商等信息: watermar ...

  10. 解决Postman发送post数据但是Node.js中req.body接收不到数据的问题[已解决]

    之前编写后台接口,测试数据都是使用的Postman,相当的方便,之前也一直使用get方法,编写Node.js一直没有问题,但是由于要编写一个注册/登陆的功能,所以发送的post数据,后台的逻辑已经编写 ...