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:
$INTNAME
Reduce Output Operator
key expressions:
expr: _col3
type: string
sort order: +
Map-reduce partition columns:
expr: _col3
type: string
tag: 0
value expressions:
expr: _col4
type: int
e
TableScan
alias: e
Reduce Output Operator
key expressions:
expr: k2
type: string
sort order: +
Map-reduce partition columns:
expr: k2
type: string
tag: 1
Reduce Operator Tree:
Join Operator
condition map:
Left Outer Join0 to 1
condition expressions:
0 {VALUE._col10}
1
handleSkewJoin: false
outputColumnNames: _col10
Select Operator
expressions:
expr: _col10
type: int
outputColumnNames: _col0
File Output Operator
compressed: true
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
常规来讲,这个SQL非常简单,a表是主表，与其他表左外关联用到了k1和k2两个关联键，使用两个MapReduce作业完全可以搞定。但是这个SQL的执行计划却给出了3个作业：（Stage-0用做数据的最终展示，该作业可以忽略不计）第1个作业（Stage-5）是a表与b表关联；第2个作业（Stage-1）是第1个作业的中间结果再与c、d、f三表关联；第3个作业（Stage-2）是第2个作业的中间结果再与e表关联。
相关阅读：