Eric Hanson created HIVE-4548:
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Summary: Speed up vectorized LIKE filter for special cases abc%,
%abc and %abc%
Key: HIVE-4548
URL: https://issues.apache.org/jira/browse/HIVE-4548
Project: Hive
Issue Type: Sub-task
Affects Versions: vectorization-branch
Reporter: Eric Hanson
Assignee: Teddy Choi
Priority: Minor
Fix For: vectorization-branch
Speed up vectorized LIKE filter evaluation for abc%, %abc, and %abc% pattern
special cases (here, abc is just a place holder for some fixed string).
Problem: The current vectorized LIKE implementation always calls the standard
LIKE function code in UDFLike.java. But this is pretty expensive. It calls
multiple functions and allocates at least one new object per call. Probably 80%
of uses of LIKE are for the simple patterns abc%, %abc, and %abc%. These can
be implemented much more efficiently.
Start by speeding up the case for
Column LIKE "abc%"
The goal would be to minimize expense in the inner loop. Don't use new() in the
inner loop, and write a static function that checks the prefix of the string
matches the like pattern as efficiently as possible, operating directly on the
byte array holding UTF-8-encoded string data, and avoiding unnecessary
additional function calls and if/else logic. Call that in the inner loop.
If feasible, consider using a template-driven approach, with an instance of the
template expanded for each of the three cases. Start doing the abc% (prefix
match) by hand, then consider templatizing for the other two cases.
The code is in the "vectorization" branch of the main hive repo.
Start by checking in the constructor for FilterStringColLikeStringScalar.java
if the pattern is one of the simple special cases. If so, record that, and have
the evaluate() method call a special-case function for each case, i.e. the
general case, and each of the 3 special cases. All the dynamic decision-making
would be done once per vector, not once per element.
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