I have recently started working on PostgreSQL and I have around 12M rows to handle in which I want to apply Full Text Search. I don't have any prior experience in handling such databases. I have tried to optimize the query but I doubt that it has been fully optimized.
Right now I'm using GIST Index as I read that updates are slower in GIN Index and my database will be updated regularly.
I need to focus on only two columns of my database right now merchant varchar(80)
and product varchar(400)
.
I need to find the product using FTS and also I'm trying to get the product even if the merchant is misspelled.
I ran some queries on a sample Database of about 30K rows to get the following results:
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First I ran the basic FTS query to analyse the results.
explain analyze select count(*) from products where to_tsvector('english', product) @@ to_tsquery('hat');
Aggregate (cost=2027.27..2027.28 rows=1 width=0) (actual time=349.032..349.032 rows=1 loops=1) -> Seq Scan on products (cost=0.00..2026.90 rows=147 width=0) (actual time=43.322..348.961 rows=307 loops=1) Filter: (to_tsvector((product)::text) @@ to_tsquery('hat'::text)) Total runtime: 349.140 ms
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Then I created the GIST index and ran the same query to see the improvement. The results were quite good. At least for me.
create index product_gist on products using gist(to_tsvector('english', product));
Aggregate (cost=447.17..447.18 rows=1 width=0) (actual time=12.911..12.911 rows=1 loops=1) -> Bitmap Heap Scan on products (cost=9.40..446.80 rows=147 width=0) (actual time=2.256..12.776 rows=307 loops=1) Recheck Cond: (to_tsvector('english'::regconfig, (product)::text) @@ to_tsquery('hat'::text)) -> Bitmap Index Scan on pn (cost=0.00..9.37 rows=147 width=0) (actual time=2.111..2.111 rows=307 loops=1) Index Cond: (to_tsvector('english'::regconfig, (product)::text) @@ to_tsquery('hat'::text)) Total runtime: 13.051 ms
I also tested a GIN Index and the result was astonishing. Total Runtime: 0.583ms
But I can't use GIN Index, so lets get back to GIST Index.
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Now, I'm using pg_trgm module in addition to find the similarity between two words (using it for misspelled merchant).
create index merchant_trgm on products using gist(merchant gist_trgm_ops); select count(*) from products where to_tsvector('english', product) @@ to_tsquery('hat') AND similarity(merchant,'fashion') > 0.2;
Aggregate (cost=447.64..447.65 rows=1 width=0) (actual time=14.644..14.645 rows=1 loops=1) -> Bitmap Heap Scan on products (cost=9.38..447.51 rows=49 width=0) (actual time=2.187..14.635 rows=12 loops=1) Recheck Cond: (to_tsvector('english'::regconfig, (product)::text) @@ to_tsquery('hat'::text)) Filter: (similarity((merchant)::text, 'fashion'::text) > 0.2::double precision) -> Bitmap Index Scan on product_gist (cost=0.00..9.37 rows=147 width=0) (actual time=2.055..2.055 rows=307 loops=1) Index Cond: (to_tsvector('english'::regconfig, (product)::text) @@ to_tsquery('hat'::text)) Total runtime: 14.705 ms
When I run these queries on my database with 12M rows. Obviously it takes more time. Can anyone help me to further reduce the total runtime.
Some more questions in my mind right now:
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How can I search for a query like 'WALMART BAGS' which will first return me product BAG with merchant WALMART and then BAGS from other merchants.
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Can I have both GIN and GIST index working for me?
Edit:
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I also ran this query last night and got the following results. I already have the GIST index created and I've checked it is being called. Still the performance is not up to my expectations.
select count(*) from products where (setweight(to_tsvector('english', merchant || ' ' || product), 'A') || setweight(to_tsvector('english', product), 'B') || setweight(to_tsvector('english', merchant), 'C')) @@ to_tsquery('hat') AND similarity(merchant,'fashion') > 0.2;
Aggregate (cost=450.97..450.98 rows=1 width=0) (actual time=18.228..18.228 rows=1 loops=1) -> Bitmap Heap Scan on products (cost=9.40..450.84 rows=49 width=0) (actual time=2.399..18.220 rows=12 loops=1) Recheck Cond: (((setweight(to_tsvector('english'::regconfig, (((merchant)::text || ' '::text) || (product)::text)), 'A'::"char") || setweight(to_tsvector('english'::regconfig, (product)::text), 'B'::"char")) || setweight(to_tsvector('english'::regconfig, (merchant)::text), 'C'::"char")) @@ to_tsquery('hat'::text)) Filter: (similarity((merchant)::text, 'fashion'::text) > 0.2::double precision) -> Bitmap Index Scan on products_weighted_index (cost=0.00..9.39 rows=147 width=0) (actual time=2.206..2.206 rows=307 loops=1) Index Cond: (((setweight(to_tsvector('english'::regconfig, (((merchant)::text || ' '::text) || (product)::text)), 'A'::"char") || setweight(to_tsvector('english'::regconfig, (product)::text), 'B'::"char")) || setweight(to_tsvector('english'::regconfig, (merchant)::text), 'C'::"char")) @@ to_tsquery('hat'::text)) Total runtime: 18.289 ms (7 rows)
Best Answer
Assessment
In your last query, the bitmap index scan looking for 'hat' produces 307 hits.
Postgres then runs a bitmap heap scan to filter merchants similar enough (
similarity(...) > 0.2
), producing 12 rows. Your test is with 30K rows, so your real life query will produce around 300 times as many hits, 90k / 3.5k for the test case at hand. An additional index onmerchant
will help.Advice
I suggest you create an additional trigram index for the similarity search. Be sure to read the chapter in the manual about trigram index support. We need the additional module
pg_trgm
installed (like you obviously have).For your first request:
I suggest this query using the similarity operator
%
:Again, you can choose between GiST and GIN, but this time GiST carries a decisive advantage:
Therefore, I suggest this index:
As for your second request:
Yes, you can. It would hardly make sense to have both types for the same (combination of) column(s), but Postgres can combine GiST and GIN indices very well in the same query. I quote the excellent manual yet again, on Combining Multiple Indexes: