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From "Dima Zhiyanov (JIRA)" <j...@apache.org>
Subject [jira] [Created] (SPARK-6072) Enable hash joins for nullable columns
Date Sat, 28 Feb 2015 00:54:04 GMT
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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