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关于 HiveSQL 常见的 Left Join 误区,你知道吗

2022-03-31 09:46 · 头闻号数据库

写在前面

很多时候,由于SQL逻辑复杂,加之对SQL执行逻辑理解不透彻,很容易产生一些莫名其妙的结果,这些结果看似不符合预期,殊不知这就是真实结果。本文整理了几个常见的SQL问题,我们在实际书写SQL脚本时,需要多加注意,希望本文对你有所帮助。

关于LEFT JOIN

外连接是我们书写SQL时经常使用的多表连接方式,使用起来也是十分的简单。值得注意的是,越是简单的东西,越是容易被忽略细节。通常我们都是这样理解LEFT JOIN的:

语义是满足Join on条件的直接返回,但不满足情况下,需要返回Left Outer Join的left 表所有列,同时右表的列全部填null

上述对于LEFT JOIN的理解是没有任何问题的,但是里面有一个误区:谓词下推。具体看下面的实例:

假设有如下的三张表:

--建表
create table t1(id int, value int) partitioned by (ds string);
create table t2(id int, value int) partitioned by (ds string);
create table t3(c1 int, c2 int, c3 int);
--数据装载,t1表
insert overwrite table t1 partition(ds='20220120') select '1','2022';
insert overwrite table t1 partition(ds='20220121') select '2','2022';
insert overwrite table t1 partition(ds='20220122') select '2','2022';

--数据装载,t2表
insert overwrite table t2 partition(ds='20220120') select '1','120';

当我们执行如下的SQL查询时,会返回什么数据呢?

SELECT  *
FROM t1
LEFT JOIN t2
ON t1.id = t2.id
AND t1.ds = '20220120'
;

结果1:

1 2022 20220120 1 120 20220120

结果2:

1 2022 20220120 1 120 20220120
2 2022 20220121 NULL NULL NULL
1 2022 20220122 NULL NULL NULL

相信对于很多初学者,甚至是一个有开发经验的人来说,会认为结果1是正确的返回结果。其实结果1的并不是正确的结果,真正的返回值是结果2.

是不是跟预期的结果不一致呢?很多初学者会认为上述查询SQL中AND t1.ds = '20220120'会进行谓词下推,从而得到结果2。其实,SQL本身的语义不是这样的,如果需要获取结果1的数据,正确的查询方式是下面这样:

--方式1:
SELECT *
FROM t1
LEFT OUTER JOIN t2
ON t1.id = t2.id
WHERE t1.ds = '20220120'
;
--方式2:

SELECT *
FROM (
SELECT *
FROM t1
WHERE ds = '20220120'
) t1
LEFT OUTER JOIN t2
ON t1.id = t2.id
;

细心的你看出差异了吗?重点是在WHERE t1.ds = '20220120'过滤条件上,最上面的查询方式是ON t1.ds = '20220120',所以按照LEFT JOIN的语义,如果没有过滤条件,那么左表的数据应该全部返回,右表匹配不上则补null。

执行计划

我们先来看看没有谓词下推的查询SQL的执行计划

正常LEFT JOIN

查看执行计划

EXPLAIN
SELECT *
FROM t1
LEFT JOIN t2
ON t1.id = t2.id
AND t1.ds = '20220120'
;

执行计划结果

hive> EXPLAIN
> SELECT *
> FROM t1
> LEFT JOIN t2
> ON t1.id = t2.id
> AND t1.ds = '20220120'
> ;
OK
STAGE DEPENDENCIES:
Stage-4 is a root stage
Stage-3 depends on stages: Stage-4
Stage-0 depends on stages: Stage-3

STAGE PLANS:
Stage: Stage-4
Map Reduce Local Work
Alias -> Map Local Tables:
$hdt$_1:t2
Fetch Operator
limit: -1
Alias -> Map Local Operator Tree:
$hdt$_1:t2
TableScan
alias: t2
Statistics: Num rows: 1 Data size: 5 Basic stats: COMPLETE Column stats: NONE
Select Operator
expressions: id (type: int), value (type: int), ds (type: string)
outputColumnNames: _col0, _col1, _col2
Statistics: Num rows: 1 Data size: 5 Basic stats: COMPLETE Column stats: NONE
HashTable Sink Operator
filter predicates:
0 {(_col2 = '20220120')}
1
keys:
0 _col0 (type: int)
1 _col0 (type: int)

Stage: Stage-3
Map Reduce
Map Operator Tree:
TableScan
alias: t1
Statistics: Num rows: 3 Data size: 18 Basic stats: COMPLETE Column stats: NONE
Select Operator
expressions: id (type: int), value (type: int), ds (type: string)
outputColumnNames: _col0, _col1, _col2
Statistics: Num rows: 3 Data size: 18 Basic stats: COMPLETE Column stats: NONE
Map Join Operator
condition map:
Left Outer Join0 to 1
filter predicates:
0 {(_col2 = '20220120')}
1
keys:
0 _col0 (type: int)
1 _col0 (type: int)
outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5
Statistics: Num rows: 3 Data size: 19 Basic stats: COMPLETE Column stats: NONE
File Output Operator
compressed: false
Statistics: Num rows: 3 Data size: 19 Basic stats: COMPLETE Column stats: NONE
table:
input format: org.apache.hadoop.mapred.SequenceFileInputFormat
output format: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat
serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
Local Work:
Map Reduce Local Work

Stage: Stage-0
Fetch Operator
limit: -1
Processor Tree:
ListSink

从上面的执行计划可以看出:总共有3个stage,

STAGE DEPENDENCIES: Stage-4 is a root stage Stage-3 depends on stages: Stage-4 Stage-0 depends on stages: Stage-3

其中stage4是map任务读取t2表,将t2表加载成HashTable,用于map端join。t2表数据量为1行。

Select Operator expressions: id (type: int), value (type: int), ds (type: string) outputColumnNames: _col0, _col1, _col2 Statistics: Num rows: 1 Data size: 5 Basic stats: COMPLETE Column stats: NONE HashTable Sink Operator

stage3是map任务读取t1表数据并执行map端join。t1表数量为3行,可见并没有进行过滤操作。

 Map Operator Tree:
TableScan
alias: t1
Statistics: Num rows: 3 Data size: 18 Basic stats: COMPLETE Column stats: NONE
Select Operator
expressions: id (type: int), value (type: int), ds (type: string)
outputColumnNames: _col0, _col1, _col2
Statistics: Num rows: 3 Data size: 18 Basic stats: COMPLETE Column stats: NONE

Stage-0进行结果输出,最终并未执行过滤操作。

Stage: Stage-0 Fetch Operator limit: -1 Processor Tree: ListSink

谓词下推的LEFT JOIN

EXPLAIN
SELECT *
FROM t1
LEFT OUTER JOIN t2
ON t1.id = t2.id
WHERE t1.ds = '20220120'
;

执行计划结果

STAGE DEPENDENCIES:
Stage-4 is a root stage
Stage-3 depends on stages: Stage-4
Stage-0 depends on stages: Stage-3

STAGE PLANS:
Stage: Stage-4
Map Reduce Local Work
Alias -> Map Local Tables:
$hdt$_1:t2
Fetch Operator
limit: -1
Alias -> Map Local Operator Tree:
$hdt$_1:t2
TableScan
alias: t2
Statistics: Num rows: 1 Data size: 5 Basic stats: COMPLETE Column stats: NONE
Select Operator
expressions: id (type: int), value (type: int), ds (type: string)
outputColumnNames: _col0, _col1, _col2
Statistics: Num rows: 1 Data size: 5 Basic stats: COMPLETE Column stats: NONE
HashTable Sink Operator
keys:
0 _col0 (type: int)
1 _col0 (type: int)

Stage: Stage-3
Map Reduce
Map Operator Tree:
TableScan
alias: t1
Statistics: Num rows: 1 Data size: 6 Basic stats: COMPLETE Column stats: NONE
Select Operator
expressions: id (type: int), value (type: int)
outputColumnNames: _col0, _col1
Statistics: Num rows: 1 Data size: 6 Basic stats: COMPLETE Column stats: NONE
Map Join Operator
condition map:
Left Outer Join0 to 1
keys:
0 _col0 (type: int)
1 _col0 (type: int)
outputColumnNames: _col0, _col1, _col3, _col4, _col5
Statistics: Num rows: 1 Data size: 6 Basic stats: COMPLETE Column stats: NONE
Select Operator
expressions: _col0 (type: int), _col1 (type: int), '20220120' (type: string), _col3 (type: int), _col4 (type: int), _col5 (type: string)
outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5
Statistics: Num rows: 1 Data size: 6 Basic stats: COMPLETE Column stats: NONE
File Output Operator
compressed: false
Statistics: Num rows: 1 Data size: 6 Basic stats: COMPLETE Column stats: NONE
table:
input format: org.apache.hadoop.mapred.SequenceFileInputFormat
output format: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat
serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
Local Work:
Map Reduce Local Work

Stage: Stage-0
Fetch Operator
limit: -1
Processor Tree:
ListSink

从上面的执行计划可以看出:总共有3个stage,

STAGE DEPENDENCIES: Stage-4 is a root stage Stage-3 depends on stages: Stage-4 Stage-0 depends on stages: Stage-3

其中stage4是map任务读取t2表,将t2表加载成HashTable,用于map端join。t2表数据量为1行。

 TableScan
alias: t2
Statistics: Num rows: 1 Data size: 5 Basic stats: COMPLETE Column stats: NONE
Select Operator
expressions: id (type: int), value (type: int), ds (type: string)
outputColumnNames: _col0, _col1, _col2
Statistics: Num rows: 1 Data size: 5 Basic stats: COMPLETE Column stats: NONE
HashTable Sink Operator

stage3是map任务读取t1表数据并执行map端join。t1表数量为1行,执行了过滤操作。

TableScan
alias: t1
Statistics: Num rows: 1 Data size: 6 Basic stats: COMPLETE Column stats: NONE
Select Operator
expressions: id (type: int), value (type: int)
outputColumnNames: _col0, _col1
Statistics: Num rows: 1 Data size: 6 Basic stats: COMPLETE Column stats: NONE
Map Join Operator
condition map:
Left Outer Join0 to 1
keys:
0 _col0 (type: int)
1 _col0 (type: int)
outputColumnNames: _col0, _col1, _col3, _col4, _col5
Statistics: Num rows: 1 Data size: 6 Basic stats: COMPLETE Column stats: NONE

Stage-0进行结果输出,最终并未执行过操作。

Stage: Stage-0 Fetch Operator limit: -1 Processor Tree: ListSink

总结本文主要结合具体的使用示例,对HiveSQL的LEFT JOIN操作进行了详细解释。主要包括两种比较常见的LEFT JOIN方式,一种是正常的LEFT JOIN,也就是只包含ON条件,这种情况没有过滤操作,即左表的数据会全部返回。另一种方式是有谓词下推,即关联的时候使用了WHERE条件,这个时候会会对数据进行过滤。所以在写SQL的时候,尤其需要注意这些细节问题,以免出现意想不到的错误结果。


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