关于 HiveSQL 常见的 Left Join 误区，你知道吗-51CTO.COM

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

查看执行计划

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。