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hoping someone can try to help me decrypt some index behavior. I'm working on enabling some simple contains type lookups on various user-data columns (~varchar < 255) and trying to understand the index behavior, as well as as maybe get some insight whether there is a better approach overall (maybe full text? -- though I imagine we likely really need a broader search application at some point, but moving to that is time prohibitive for our app at the moment)

Anyhoo, in my case, we primarily lookup all users from this table starting with the category/type tuple (due to some single-table inheritance w/ Rails)

Example table & indexes, using Postgres11:

CREATE TABLE people (
    id SERIAL,
    email character varying(255) not null,
    first_name character varying(255) not null,
    last_name character varying(255) not null,
    user_category integer not null,
    user_type character varying(255) not null
);

-- Dummy Data
INSERT INTO people (email, first_name, last_name, user_category, user_type)
SELECT
  concat(md5(random()::text), '@not-real-email-plz.com'),
  md5(random()::text), 
  md5(random()::text), 
  ceil(random() * 3), 
  ('{Grunt,Peon,Ogre}'::text[])[ceil(random()*3)]
FROM
  (SELECT * FROM generate_series(1,1000000) AS id) AS x;

-- Standard, existing lookup
CREATE INDEX index_people_category_type ON people USING btree (user_category, user_type);

-- taken from https://niallburkley.com/blog/index-columns-for-like-in-postgres/
CREATE INDEX idx_people_gin_user_category_and_user_type_and_full_name 
ON people
USING GIN(user_category, user_type, (first_name || ' ' || last_name) gin_trgm_ops);    

-- first name
CREATE INDEX idx_people_gin_user_category_and_user_type_and_first_name 
ON people
USING GIN(user_category, user_type, first_name gin_trgm_ops);

-- last name
CREATE INDEX idx_people_gin_user_category_and_user_type_and_last_name 
ON people
USING GIN(user_category, user_type, last_name gin_trgm_ops);

-- email
CREATE INDEX idx_people_gin_user_category_and_user_type_and_email 
ON people
USING GIN(user_category, user_type, email gin_trgm_ops);

-- non-composite email (had for testing and raised more questions)
CREATE INDEX idx_people_gin_email 
ON people
USING GIN(email gin_trgm_ops);

I've read that order doesn't matter in GIN indexes, so I guess my first question is whether or not it is also possible to make one index including multiple columns that would kick in for any combination of their usage? My guess is no, since the indexes definitely vary by size but was unsure of the implication of the ordering detail.

Anyway, onto what I've observed!

One of the first things I noticed, is that it seems like the GIN index immediately supplants the first b-tree index when simply looking up by category and type

EXPLAIN ANALYZE VERBOSE

SELECT DISTINCT id
FROM people
WHERE user_category = 2
  AND (user_type != 'Ogre')

Results:

Unique  (cost=52220.05..53334.71 rows=222932 width=4) (actual time=251.070..339.769 rows=222408 loops=1)
  Output: id
  ->  Sort  (cost=52220.05..52777.38 rows=222932 width=4) (actual time=251.069..285.652 rows=222408 loops=1)
        Output: id
        Sort Key: people.id
        Sort Method: external merge  Disk: 3064kB
        ->  Bitmap Heap Scan on public.people  (cost=3070.23..29368.23 rows=222932 width=4) (actual time=35.156..198.549 rows=222408 loops=1)
              Output: id
              Recheck Cond: (people.user_category = 2)
              Filter: ((people.user_type)::text <> 'Ogre'::text)
              Rows Removed by Filter: 111278
              Heap Blocks: exact=21277
              ->  Bitmap Index Scan on idx_people_gin_user_category_and_user_type_and_email  (cost=0.00..3014.50 rows=334733 width=0) (actual time=32.017..32.017 rows=333686 loops=1)
                    Index Cond: (people.user_category = 2)
Planning Time: 0.293 ms
Execution Time: 359.247 ms

Is the original b-tree totally redundant at this point? I expected it might still be picked by the planner if only those two columns were used if the b-tree was faster for those data types but that seems like it is not the case.

Next, I had noticed was that our existing queries were depending on lower() and seemed to completely ignore the GIN indexes, or rather it seemed to use whichever was the last one created even if that column was not used in the query:

EXPLAIN ANALYZE VERBOSE

SELECT DISTINCT id
FROM people
WHERE user_category = 2
  AND (user_type != 'Ogre')
  AND (LOWER(last_name) LIKE LOWER('%a62%'))

Results (comparing last_name but using email index):

HashAggregate  (cost=28997.16..29086.33 rows=8917 width=4) (actual time=175.204..175.554 rows=1677 loops=1)
  Output: id
  Group Key: people.id
  ->  Gather  (cost=4016.73..28974.87 rows=8917 width=4) (actual time=39.947..181.936 rows=1677 loops=1)
        Output: id
        Workers Planned: 2
        Workers Launched: 2
        ->  Parallel Bitmap Heap Scan on public.people  (cost=3016.73..27083.17 rows=3715 width=4) (actual time=22.037..156.233 rows=559 loops=3)
              Output: id
              Recheck Cond: (people.user_category = 2)
              Filter: (((people.user_type)::text <> 'Ogre'::text) AND (lower((people.last_name)::text) ~~ '%a62%'::text))
              Rows Removed by Filter: 110670
              Heap Blocks: exact=7011
              Worker 0: actual time=13.573..147.844 rows=527 loops=1
              Worker 1: actual time=13.138..147.867 rows=584 loops=1
              ->  Bitmap Index Scan on idx_people_gin_user_category_and_user_type_and_email  (cost=0.00..3014.50 rows=334733 width=0) (actual time=35.445..35.445 rows=333686 loops=1)
                    Index Cond: (people.user_category = 2)
Planning Time: 7.546 ms
Execution Time: 189.186 ms

Whereas switching to ILIKE

EXPLAIN ANALYZE VERBOSE

SELECT DISTINCT id
FROM people
WHERE user_category = 2
  AND (user_type != 'Ogre')
  AND (last_name ILIKE '%A62%')

Results are way faster, and using the expected index. What is it about the lower() call that seems to make the planner skip a beat?

Unique  (cost=161.51..161.62 rows=22 width=4) (actual time=27.144..27.570 rows=1677 loops=1)
  Output: id
  ->  Sort  (cost=161.51..161.56 rows=22 width=4) (actual time=27.137..27.256 rows=1677 loops=1)
        Output: id
        Sort Key: people.id
        Sort Method: quicksort  Memory: 127kB
        ->  Bitmap Heap Scan on public.people  (cost=32.34..161.02 rows=22 width=4) (actual time=16.470..26.798 rows=1677 loops=1)
              Output: id
              Recheck Cond: ((people.user_category = 2) AND ((people.last_name)::text ~~* '%A62%'::text))
              Filter: ((people.user_type)::text <> 'Ogre'::text)
              Rows Removed by Filter: 766
              Heap Blocks: exact=2291
              ->  Bitmap Index Scan on idx_people_gin_user_category_and_user_type_and_last_name  (cost=0.00..32.33 rows=33 width=0) (actual time=16.058..16.058 rows=2443 loops=1)
                    Index Cond: ((people.user_category = 2) AND ((people.last_name)::text ~~* '%A62%'::text))
Planning Time: 10.577 ms
Execution Time: 27.746 ms

Next, adding another field into things...

EXPLAIN ANALYZE VERBOSE

SELECT DISTINCT id
FROM people
WHERE user_category = 2
  AND (user_type != 'Ogre')
  AND (last_name ILIKE '%A62%')
  AND (first_name ILIKE '%EAD%')

Is still pretty speedy overall

Unique  (cost=161.11..161.11 rows=1 width=4) (actual time=10.854..10.860 rows=12 loops=1)
  Output: id
  ->  Sort  (cost=161.11..161.11 rows=1 width=4) (actual time=10.853..10.854 rows=12 loops=1)
        Output: id
        Sort Key: people.id
        Sort Method: quicksort  Memory: 25kB
        ->  Bitmap Heap Scan on public.people  (cost=32.33..161.10 rows=1 width=4) (actual time=3.895..10.831 rows=12 loops=1)
              Output: id
              Recheck Cond: ((people.user_category = 2) AND ((people.last_name)::text ~~* '%A62%'::text))
              Filter: (((people.user_type)::text <> 'Ogre'::text) AND ((people.first_name)::text ~~* '%EAD%'::text))
              Rows Removed by Filter: 2431
              Heap Blocks: exact=2291
              ->  Bitmap Index Scan on idx_people_gin_user_category_and_user_type_and_last_name  (cost=0.00..32.33 rows=33 width=0) (actual time=3.173..3.173 rows=2443 loops=1)
                    Index Cond: ((people.user_category = 2) AND ((people.last_name)::text ~~* '%A62%'::text))
Planning Time: 0.257 ms
Execution Time: 10.897 ms

Yet going back to that extra, non-tuple index created at the bottom and filtering on email seems to utilize another index as part of things?

EXPLAIN ANALYZE VERBOSE

SELECT DISTINCT id
FROM people
WHERE user_category = 2
  AND (user_type != 'Ogre')
  AND (last_name ILIKE '%A62%')
  AND (email ILIKE '%0F9%')

Has a different path:

Unique  (cost=140.37..140.38 rows=1 width=4) (actual time=4.180..4.184 rows=7 loops=1)
  Output: id
  ->  Sort  (cost=140.37..140.38 rows=1 width=4) (actual time=4.180..4.180 rows=7 loops=1)
        Output: id
        Sort Key: people.id
        Sort Method: quicksort  Memory: 25kB
        ->  Bitmap Heap Scan on public.people  (cost=136.34..140.36 rows=1 width=4) (actual time=4.145..4.174 rows=7 loops=1)
              Output: id
              Recheck Cond: ((people.user_category = 2) AND ((people.last_name)::text ~~* '%A62%'::text) AND ((people.email)::text ~~* '%0F9%'::text))
              Filter: ((people.user_type)::text <> 'Ogre'::text)
              Rows Removed by Filter: 4
              Heap Blocks: exact=11
              ->  BitmapAnd  (cost=136.34..136.34 rows=1 width=0) (actual time=4.125..4.125 rows=0 loops=1)
                    ->  Bitmap Index Scan on idx_people_gin_user_category_and_user_type_and_last_name  (cost=0.00..32.33 rows=33 width=0) (actual time=3.089..3.089 rows=2443 loops=1)
                          Index Cond: ((people.user_category = 2) AND ((people.last_name)::text ~~* '%A62%'::text))
                    ->  Bitmap Index Scan on idx_people_gin_email  (cost=0.00..103.76 rows=10101 width=0) (actual time=0.879..0.879 rows=7138 loops=1)
                          Index Cond: ((people.email)::text ~~* '%0F9%'::text)
Planning Time: 0.291 ms
Execution Time: 4.217 ms

The cost seems negligibly lower, but wondering what this means for a fairly dynamic amount of columns that could be filtered by? Would it be ideal to make a non-tuple index for all fields too?

Sorry for the length, have spun my wheels for a while trying to figure through it all, but any insight would be pretty awesome (and it seems like there aren't a ton of GIN indexes like this, though maybe I am missing something more fundamental overall)

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  • 1
    You are providing good and clean information. But there are just too many questions at once. Please try to isolate one question at a time.
    – Erwin Brandstetter
    Nov 29, 2019 at 1:37
  • Just a quick side note thanking you for your examples. It finally made it clear that generate_series() can function as a pure iterator. In your example, you generate 1-1M numbers, but don't use them...you only use the series itself as a way of injecting sample records. Probably obvious to most, but I've been struggling with this very point since last week.
    – Morris de Oryx
    Nov 29, 2019 at 11:12

1 Answer 1

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Is the original b-tree totally redundant at this point? I expected it might still be picked by the planner if only those two columns were used if the b-tree was faster for those data types but that seems like it is not the case.

Not totally. The btree index can be used for ordering (although for 3 distinct values in each column, its not clear how much call there would be for that). In my hands the b-tree index actually is faster, but not by much. I expected it to be faster by more. Even if I change the != test to = (which is where b-tree shines), the b-tree index is still only slightly faster. The advantage btree has in multicolumn equality testing was mostly cancelled by the GIN's advantage in compressing the storage of tid-lists, which comes in handy when each of 3 values shows up 333,333 times. Don't expect this to carry over to other situations.

Results are way faster, and using the expected index. What is it about the lower() call that seems to make the planner skip a beat?

You have to build the index on the things you want to search. If you had built the index on (user_category, user_type, lower(last_name) gin_trgm_ops); then it would use it. PostgreSQL just knows that lower() takes text and spits out text. It doesn't know that lower(a) LIKE lower(b) implies that a ILIKE b.

The cost seems negligibly lower, but wondering what this means for a fairly dynamic amount of columns that could be filtered by? Would it be ideal to make a non-tuple index for all fields too?

I don't know what you mean by a non-tuple index. When you make an N-column GIN index, it is about the same thing as making N single-column GIN indexes. The planner can combine individual indexes with BitmapAnd and BitmapOr, and GIN can combine multiple columns of a muli-column index within in much the same way, but without the transparency.

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