Details
-
Improvement
-
Status: Resolved
-
Major
-
Resolution: Incomplete
-
None
-
None
Description
Currently Spark SQL uses a Broadcast Nested Loop join (or a filtered Cartesian Join) when it has to execute the following range query:
SELECT A.*, B.* FROM tableA A JOIN tableB B ON A.start <= B.end AND A.end > B.start
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 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.