线上一个查询简化如下：
Selectdt,count(distinct c1) , count(distinct case when c2>0 and c1=0 then c1 end),count(distinct case when c2>0 and c1>0 then c1 end) from t where dtbetween ‘20131108’ and ‘20131110’ group by dt;
一个让人头痛的multi-distinct问题，为什么说很头痛，看看执行计划就清楚了：
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ABSTRACTSYNTAX TREE:
(TOK_QUERY (TOK_FROM (TOK_TABREF (TOK_TABNAMEt))) (TOK_INSERT (TOK_DESTINATION (TOK_DIR TOK_TMP_FILE)) (TOK_SELECT(TOK_SELEXPR (TOK_TABLE_OR_COL dt)) (TOK_SELEXPR (TOK_FUNCTIONDI count(TOK_TABLE_OR_COL c1))) (TOK_SELEXPR (TOK_FUNCTIONDI count (TOK_FUNCTION when(and (> (TOK_TABLE_OR_COL c2)
0
) (= (TOK_TABLE_OR_COL c1)
0
))(TOK_TABLE_OR_COL c1)))) (TOK_SELEXPR (TOK_FUNCTIONDI count (TOK_FUNCTION when(and (> (TOK_TABLE_OR_COL c2)
0
) (> (TOK_TABLE_OR_COL c1)
0
))(TOK_TABLE_OR_COL c1))))) (TOK_WHERE (TOK_FUNCTION between KW_FALSE(TOK_TABLE_OR_COL dt)
'20131108'
'20131110'
)) (TOK_GROUPBY (TOK_TABLE_OR_COLdt))))
STAGEDEPENDENCIES:
Stage-
1
is a root stage
Stage-
0
is a root stage
STAGEPLANS:
Stage: Stage-
1
Map Reduce
Alias -> Map Operator Tree:
t
TableScan
alias: t
Filter Operator
predicate:
expr: dt BETWEEN
'20131108'
AND
'20131110'
type: Boolean
//通过select operator做投影
Select Operator
expressions:
expr: dt
type: string
expr: c1
type:
int
expr: c2
type:
int
outputColumnNames: dt, c1, c2
//在MAP端进行简单的聚合，雷区1：假设有N个distinct，MAP处理数据有M条，那么这部处理后的输出是N*M条数据，因为MAP会对dt,keys[i]做聚合操作，所以尽量在MAP端过滤尽可能多的数据
Group By Operator
aggregations:
expr: count(DISTINCTc1)
expr: count(DISTINCTCASE WHEN (((c2 >
0
) and (c1 =
0
))) THEN (c1) END)
expr: count(DISTINCTCASE WHEN (((c2 >
0
) and (c1 >
0
))) THEN (c1) END)
bucketGroup:
false
keys:
expr: dt
type: string
expr: c1
type:
int
expr: CASE WHEN (((c2>
0
) and (c1 =
0
))) THEN (c1) END
type:
int
expr: CASE WHEN (((c2>
0
) and (c1 >
0
))) THEN (c1) END
type:
int
mode: hash
outputColumnNames: _col0,_col1, _col2, _col3, _col4, _col5, _col6
//雷区2：在做Reduce Sink时是根据partition cplumns进行HASH的方式，那么对于按date分区的表来说一天的所有数据被放大N倍传输到Reducer进行运算，导致性能长尾或者OOME.
Reduce Output Operator
key expressions:
expr: _col0
type: string
expr: _col1
type:
int
expr: _col2
type:
int
expr: _col3
type:
int
sort order: ++++
Map-reduce partitioncolumns:
expr: _col0
type: string
tag: -
1
value expressions:
expr: _col4
type: bigint
expr: _col5
type: bigint
expr: _col6
type: bigint
Reduce Operator Tree:
Group By Operator
aggregations:
expr: count(DISTINCTKEY._col1:
0
._col0)
expr: count(DISTINCTKEY._col1:
1
._col0)
expr: count(DISTINCTKEY._col1:
2
._col0)
bucketGroup:
false
keys:
expr: KEY._col0
type: string
mode: mergepartial
outputColumnNames: _col0, _col1,_col2, _col3
Select Operator
expressions:
expr: _col0
type: string
expr: _col1
type: bigint
expr: _col2
type: bigint
expr: _col3
type: bigint
outputColumnNames: _col0, _col1,_col2, _col3
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
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虽说Hive里有参数：hive.groupby.skewindata（具体见：http://boylook.blog.51cto.com/7934327/1316432）不过当设置该参数时只支持single-distinct（https://issues.apache.org/jira/browse/HIVE-2416）因此在这种场景下是没办法设置的.但是这个参数还是有一定启发的就是把SQL化归到这种single-distinct:通过union all(注意不能直接Union All而是需要嵌套进子查询，否则会报异常：Toplevel UNION is not supported currently; use a subquery for the UNION).
查看执行计划（省去非关键部分）：
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STAGE DEPENDENCIES:
Stage-
1
is a root stage
Stage-
2
depends on stages:Stage-
1
, Stage-
3
, Stage-
4
Stage-
3
is a root stage
Stage-
4
is a root stage
Stage-
0
is a root stage
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可以看到每个single-distinct都是独立的stage，因此可以设置上面的参数，这里既然每个stage是独立的那么是不是可以设置hive.exec.parallel，hive.exec.parallel.thread.number这两个参数来以资源换时间呢？故事总是残忍的，这里还有一个Bug(https://issues.apache.org/jira/browse/HIVE-4436), 因此在Hive0.12 release前是没办法的，这就叫有钱没地方花.
另外也有通过unionall+sum的解决方法，感兴趣的同学也可以尝试一下.
如果不做100%精确计算的话可以通过bucket sample的方式可以更快的解决：）
本文转自MIKE老毕 51CTO博客，原文链接：http://blog.51cto.com/boylook/1322536，如需转载请自行联系原作者
