liyunzhang_intel created HIVE-17018:
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Summary: Small table can not be converted to map join in
TPC-DS/query.17 on 3TB data scale
Key: HIVE-17018
URL: https://issues.apache.org/jira/browse/HIVE-17018
Project: Hive
Issue Type: Bug
Reporter: liyunzhang_intel
Assignee: liyunzhang_intel
we use "hive.auto.convert.join.noconditionaltask.size" as the threshold. it
means the sum of size for n-1 of the tables/partitions for a n-way join is
smaller than it, it will be converted to a map join. for example, A join B join
C join D join E. Big table is A(100M), small tables are
B(10M),C(10M),D(10M),E(10M). If we set
hive.auto.convert.join.noconditionaltask.size=20M. In current code, E,D,B will
be converted to map join but C will not be converted to map join. In my
understanding, because hive.auto.convert.join.noconditionaltask.size can only
contain E and D, so C and B should not be converted to map join.
Let's explain more why E can be converted to map join.
in current code,
[SparkMapJoinOptimizer#getConnectedMapJoinSize|https://github.com/apache/hive/blob/master/ql/src/java/org/apache/hadoop/hive/ql/optimizer/spark/SparkMapJoinOptimizer.java#L364]
calculates all the mapjoins in the parent path and child path. The search
stops when encountering [UnionOperator or
ReduceOperator|https://github.com/apache/hive/blob/master/ql/src/java/org/apache/hadoop/hive/ql/optimizer/spark/SparkMapJoinOptimizer.java#L381].
Because C is not converted to map join because {{connectedMapJoinSize +
totalSize) > maxSize}} [see
code|https://github.com/apache/hive/blob/master/ql/src/java/org/apache/hadoop/hive/ql/optimizer/spark/SparkMapJoinOptimizer.java#L330].The
RS before the join of C remains. When calculating whether B will be converted
to map join, {{getConnectedMapJoinSize}} returns 0 as encountering [RS
|https://github.com/apache/hive/blob/master/ql/src/java/org/apache/hadoop/hive/ql/optimizer/spark/SparkMapJoinOptimizer.java#409]
and causes {{connectedMapJoinSize + totalSize) < maxSize}} matches.
[~xuefuz] or [~jxiang]: can you help see the problem as you are more familiar
with SparkJoinOptimizer.
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