I want to use pg_trgm for auto-suggest functionality. I created a Materialized View with the information that I need, with the relevant columns being (keywords text, rank int). keywords is the column from which I build the tri-grams, and rank is some popularity factor so that popular results will show up higher than less popular results given the same tri-gram distance.
I want to return results in the order of [distance], [distance_word], [rank]. The input comes from the user and is not known in advance. My query is as follows:
SELECT title
,id
,(input <-> keywords) AS distance
,(input <<-> keywords) AS distance_word
,rank
FROM (VALUES (cast('red pill' AS text))) consts(input)
,mv_autosuggest
ORDER BY 3, 4, 5
LIMIT 20;
This gives me pretty good results, but it takes too long and is not likely to scale well.
I have created two indexes but neither seem to be used:
CREATE INDEX mv_autosuggest_keywords_tgrm_gist ON staging.mv_autosuggest USING gist (keywords gist_trgm_ops);
CREATE INDEX mv_autosuggest_keywords_tgrm_gin ON staging.mv_autosuggest USING gin (keywords gin_trgm_ops);
This is the result of explain analyze:
QUERY PLAN |
-------------------------------------------------------------------------------------------------------------------------------------------|
Limit (cost=356.41..356.46 rows=20 width=51) (actual time=163.132..163.135 rows=20 loops=1) |
-> Sort (cost=356.41..372.96 rows=6619 width=51) (actual time=163.130..163.131 rows=20 loops=1) |
Sort Key: (('red pill'::text <-> mv_autosuggest.keywords)), (('red pill'::text <<-> mv_autosuggest.keywords)), mv_autosuggest.rank |
Sort Method: top-N heapsort Memory: 28kB |
-> Seq Scan on mv_autosuggest (cost=0.00..180.29 rows=6619 width=51) (actual time=0.263..161.289 rows=6619 loops=1) |
Planning time: 0.139 ms |
Execution time: 163.174 ms |
How can I improve the performance here?
Thank you,
