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Posted to issues@spark.apache.org by "Matthew Porter (JIRA)" <ji...@apache.org> on 2019/05/03 17:15:00 UTC
[jira] [Commented] (SPARK-8682) Range Join for Spark SQL
[ https://issues.apache.org/jira/browse/SPARK-8682?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16832678#comment-16832678 ]
Matthew Porter commented on SPARK-8682:
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Are there any plans to improve range joins in Spark 3.0? This would be very beneficial for our company; we currently have to take certain range joins out of Spark and perform them more efficiently on a single instance with other libraries/frameworks (when the DataFrame size permits this).
> Range Join for Spark SQL
> ------------------------
>
> Key: SPARK-8682
> URL: https://issues.apache.org/jira/browse/SPARK-8682
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Reporter: Herman van Hovell
> Priority: Major
> Attachments: perf_testing.scala
>
>
> Currently Spark SQL uses a Broadcast Nested Loop join (or a filtered Cartesian Join) when it has to execute the following range query:
> {noformat}
> SELECT A.*,
> B.*
> FROM tableA A
> JOIN tableB B
> ON A.start <= B.end
> AND A.end > B.start
> {noformat}
> This is horribly inefficient. The performance of this query can be greatly improved, when one of the tables can be broadcasted, by creating a range index. A range index is basically a sorted map containing the rows of the smaller table, indexed by both the high and low keys. using this structure the complexity of the query would go from O(N * M) to O(N * 2 * LOG(M)), N = number of records in the larger table, M = number of records in the smaller (indexed) table.
> I have created a pull request for this. According to the [Spark SQL: Relational Data Processing in Spark|http://people.csail.mit.edu/matei/papers/2015/sigmod_spark_sql.pdf] paper similar work (page 11, section 7.2) has already been done by the ADAM project (cannot locate the code though).
> Any comments and/or feedback are greatly appreciated.
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