Dima Zhiyanov created SPARK-6072:
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Summary: Enable hash joins for nullable columns
Key: SPARK-6072
URL: https://issues.apache.org/jira/browse/SPARK-6072
Project: Spark
Issue Type: Improvement
Components: SQL
Affects Versions: 1.2.1
Reporter: Dima Zhiyanov
Currently joins such as
A join B on A.x = B.x AND A.y <=> B.y
are evaluated as hash join on just x followed by filter on y.
This causes a skew problem (very long join) when a particular value of x has a
high cardinality even though (x, y) is evenly distributed
Can we implement is as a hash join on (X, Option(Y))? This will eliminate the
skew in this case
Imagine a join:
People as p1 join People as p2 on p1.name = p2.name and p1.address <=>
p2.address
(very small percentage of people has unknown address)
This causes a skewed join on popular names such as "Mary Brown" if we hash on
names alone, but will not cause a skew if we hash on (Name, Option(Address))
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