PostgreSQL – Search for Partial Matches in Website Domain Names

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 on merchant 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_trgminstalled (like you obviously have).

For your first request:

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.

I suggest this query using the similarity operator %:

-- SELECT set_limit(0.2)  -- Adjust similarity operator only if needed

SELECT *
FROM   products
WHERE  to_tsvector('english', product) @@ to_tsquery('bag')
AND    merchant % 'walmart'
ORDER  BY merchant <-> 'walmart'
--    LIMIT  n; -- possibly limit to top n results

Again, you can choose between GiST and GIN, but this time GiST carries a decisive advantage:

This can be implemented quite efficiently by GiST indexes, but not by GIN indexes. It will usually beat the first formulation when only a small number of the closest matches is wanted.

Therefore, I suggest this index:

CREATE INDEX prod_merchant_trgm_idx ON products USING gist (merchant gist_trgm_ops);

As for your second request:

Can I have both GIN and GIST index working for me?

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:

To combine multiple indexes, the system scans each needed index and prepares a bitmap in memory giving the locations of table rows that are reported as matching that index's conditions. The bitmaps are then ANDed and ORed together as needed by the query. Finally, the actual table rows are visited and returned. The table rows are visited in physical order, because that is how the bitmap is laid out; this means that any ordering of the original indexes is lost, and so a separate sort step will be needed if the query has an ORDER BY clause. For this reason, and because each additional index scan adds extra time, the planner will sometimes choose to use a simple index scan even though additional indexes are available that could have been used as well.

As a part of my work I maintain a fairly large PostgreSQL database (around 120gb on disk, several multi-million-row tables) and have collected a few tricks on how to speed up the queries. First some comments on your assumptions:

1. Yes, order is important, but it's only the first one that is really different, the rest are second class indexes.
2. I'm not sure it will always use both, my guess is that the query planner will use #1, then do something clever with the rest.
3. I have no experience with GIST.
4. Yes, add all indexes first, see what is used the most and what gives the best performance.
5. I would sugest that you try both and measure what works best. Try rewriting the sql with different subqueries, maybe country and time in one, then join with the intersect-query. I have not noticed any performance problem with IN-clauses, as long as the IN-list is not thousands of elements long. My guess is that a few different queries specifically tuned depending on the input criteria available will give the best results.

6. I would suggest against making a 4-way index. Try creating one and then check the size, they can get really huge. In my experience, four 1-key indexes have been almost as fast as a single 4-way index. A trick that does work nicely for some specific queries are partial indexes, ie something like this:

   CREATE INDEX ON table_x (key1, key2, key3) WHERE some_x_column = 'XXXX';

I have created aliases in my .psqlrc-file with queries to help find what indexes to add or remove. Feel free to have a look at them over at GitHub: .psql

I use the :seq_scans and :bigtables a lot, and then \d table_name to get details about the table. Don't forget to reset the statistics after you've done some changes, select pg_stat_reset();