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From "Petar Zecevic (JIRA)" <j...@apache.org>
Subject [jira] [Created] (SPARK-24020) Sort-merge join inner range optimization
Date Wed, 18 Apr 2018 23:49:00 GMT
Petar Zecevic created SPARK-24020:
-------------------------------------

             Summary: Sort-merge join inner range optimization
                 Key: SPARK-24020
                 URL: https://issues.apache.org/jira/browse/SPARK-24020
             Project: Spark
          Issue Type: Improvement
          Components: SQL
    Affects Versions: 2.3.0
            Reporter: Petar Zecevic


The problem we are solving is the case where you have two big tables partitioned by X column,
but also sorted by Y column (within partitions) and you need to calculate an expensive function
on the joined rows. During a sort-merge join, Spark will do cross-joins of all rows that have
the same X values and calculate the function's value on all of them. If the two tables have
a large number of rows per X, this can result in a huge number of calculations.

We hereby propose an optimization that would allow you to reduce the number of matching rows
per X using a range condition on Y columns of the two tables. Something like:

... WHERE t1.X = t2.X AND t1.Y BETWEEN t2.Y - d AND t2.Y + d

The way SMJ is currently implemented, these extra conditions have no influence on the number
of rows (per X) being checked because these extra conditions are put in the same block with
the function being calculated.

Here we propose a change to the sort-merge join so that, when these extra conditions are specified,
a queue is used instead of the ExternalAppendOnlyUnsafeRowArray class. This queue would then
used as a moving window across the values from the right relation as the left row changes.
You could call this a combination of an equi-join and a theta join (we call it "sort-merge
inner range join").

Potential use-cases for this are joins based on spatial or temporal distance calculations.

The optimization should be triggered automatically when an equi-join expression is present
AND lower and upper range conditions on a secondary column are specified. If the tables aren't
sorted by both columns, appropriate sorts should be added.

To limit the impact of this change we also propose adding a new parameter (tentatively named
"spark.sql.join.smj.useInnerRangeOptimization") which could be used to switch off the optimization
entirely.

 



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