postgres-full-text-search
Postgres full text search options (tsearch, trigram) examples.
Create DB
>> CREATE DATABASE ftdb;
To feed db with an example dataset (dataset.txt, 100k rows, 15 words each one) I used python init_db.py script.
Full text search using simple ilike
>> EXPLAIN ANALYZE
SELECT text, language
FROM public.document
WHERE
text ilike '%field%'
AND text ilike '%window%'
AND text ilike '%lamp%'
AND text ilike '%research%'
AND language = 'en'
LIMIT 1;
QUERY PLAN
----------------------------------------------------------------------------------------------------------------------------------------------
Limit (cost=0.00..3734.02 rows=1 width=105) (actual time=87.473..87.474 rows=0 loops=1)
-> Seq Scan on document (cost=0.00..3734.02 rows=1 width=105) (actual time=87.466..87.466 rows=0 loops=1)
Filter: ((text ~~* '%field%'::text) AND (text ~~* '%window%'::text) AND (text ~~* '%lamp%'::text) AND (text ~~* '%research%'::text))
Rows Removed by Filter: 100001
Planning Time: 2.193 ms
Execution Time: 87.500 ms
Full text search using ilike supported by trigram index
What is a trigram? See this example:
>> CREATE EXTENSION pg_trgm;
CREATE EXTENSION
>> select show_trgm('fielded');
show_trgm
-----------------------------------------
{" f"," fi",ded,"ed ",eld,fie,iel,lde}
We can improve ilike performance using trigram index, e.g. gin_trgm_ops.
>> CREATE INDEX ix_document_text_trigram ON document USING gin (text gin_trgm_ops) where language = 'en';
CREATE INDEX
>> EXPLAIN ANALYZE SELECT text, language
FROM public.document
WHERE
text ilike '%field%'
AND text ilike '%window%'
AND text ilike '%lamp%'
AND text ilike '%research%'
AND language = 'en'
LIMIT 1;
QUERY PLAN
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Limit (cost=176.00..180.02 rows=1 width=105) (actual time=1.473..1.474 rows=0 loops=1)
-> Bitmap Heap Scan on document (cost=176.00..180.02 rows=1 width=105) (actual time=1.470..1.471 rows=0 loops=1)
Recheck Cond: ((text ~~* '%field%'::text) AND (text ~~* '%window%'::text) AND (text ~~* '%lamp%'::text) AND (text ~~* '%research%'::text) AND ((language)::text = 'en'::text))
-> Bitmap Index Scan on ix_document_text_trigram (cost=0.00..176.00 rows=1 width=0) (actual time=1.466..1.466 rows=0 loops=1)
Index Cond: ((text ~~* '%field%'::text) AND (text ~~* '%window%'::text) AND (text ~~* '%lamp%'::text) AND (text ~~* '%research%'::text))
Planning Time: 2.389 ms
Execution Time: 1.524 ms
Create non-default language configuration for tsearch full text search
Postgres does not provide support for many languages by default. However, you can setup the configuration quite easily. You just need additional dictionary files. Here is an example for polish language. Polish dictionary files can be downloaded from: https://github.com/judehunter/polish-tsearch.
polish.affix, polish.stop and polish.dict files should be copied to postgresql tsearch_data location, e.g. /usr/share/postgresql/13/tsearch_data.
There also must be created a configuration (see the docs) inside database:
>> DROP TEXT SEARCH DICTIONARY IF EXISTS polish_hunspell CASCADE;
CREATE TEXT SEARCH DICTIONARY polish_hunspell (
TEMPLATE = ispell,
DictFile = polish,
AffFile = polish,
StopWords = polish
);
CREATE TEXT SEARCH CONFIGURATION public.polish (
COPY = pg_catalog.english
);
ALTER TEXT SEARCH CONFIGURATION polish
ALTER MAPPING
FOR
asciiword, asciihword, hword_asciipart, word, hword, hword_part
WITH
polish_hunspell, simple;
You need these files and configuration because full text search engine uses lexeme comparing to find best matches (both query pattern and stored text are lexemized):
>> SELECT to_tsquery('english', 'fielded'), to_tsvector('english', text)
FROM document
LIMIT 1;
to_tsquery | to_tsvector
------------+----------------------------------------------------------------------------------------------------------------------------------------------------
'field' | '19':16 'bat':12 'dead':8 'degre':1 'depth':5 'field':15 'lamp':13 'men':6 'put':14 'ranch':2 'tall':4 'time':3 'underlin':11 'wast':10 'window':9
If you cannot provide dictionary files you can use full text in "simple" form (without transformation to lexeme):
>> SELECT to_tsquery('simple', 'fielded'), to_tsvector('simple', text)
FROM document
LIMIT 1;
to_tsquery | to_tsvector
------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------
'fielded' | '19':16 'bat':12 'below':7 'dead':8 'degree':1 'depth':5 'field':15 'lamp':13 'men':6 'putting':14 'ranch':2 'tall':4 'time':3 'underline':11 'waste':10 'window':9
Tsearch full text search without stored index
>> EXPLAIN ANALYZE SELECT text, language
FROM public.document
WHERE to_tsvector('english', text) @@ to_tsquery('english', 'fielded & window & lamp & depth & test ')
LIMIT 1;
QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Limit (cost=1000.00..18298.49 rows=1 width=103) (actual time=489.802..491.352 rows=0 loops=1)
-> Gather (cost=1000.00..18298.49 rows=1 width=103) (actual time=489.800..491.349 rows=0 loops=1)
Workers Planned: 1
Workers Launched: 1
-> Parallel Seq Scan on document (cost=0.00..17298.39 rows=1 width=103) (actual time=486.644..486.644 rows=0 loops=2)
Filter: (((language)::text = 'en'::text) AND (to_tsvector('english'::regconfig, text) @@ '''field'' & ''window'' & ''lamp'' & ''depth'' & ''test'''::tsquery))
Rows Removed by Filter: 50000
Planning Time: 0.272 ms
Execution Time: 491.376 ms
(9 rows)
Tsearch full text search with stored partial index ('en')
>> CREATE INDEX ix_en_document_tsvector_text ON public.document USING gin (to_tsvector('english'::regconfig, text)) WHERE language = 'en';
CREATED INDEX
>> EXPLAIN ANALYZE SELECT text, language
FROM public.document
WHERE to_tsvector('english', text) @@ to_tsquery('english', 'fielded & window & lamp & depth & test ')
LIMIT 1;
QUERY PLAN
----------------------------------------------------------------------------------------------------------------------------------------
Limit (cost=1000.00..18151.43 rows=1 width=103) (actual time=487.120..488.569 rows=0 loops=1)
-> Gather (cost=1000.00..18151.43 rows=1 width=103) (actual time=487.117..488.567 rows=0 loops=1)
Workers Planned: 1
Workers Launched: 1
-> Parallel Seq Scan on document (cost=0.00..17151.33 rows=1 width=103) (actual time=484.418..484.419 rows=0 loops=2)
Filter: (to_tsvector('english'::regconfig, text) @@ '''field'' & ''window'' & ''lamp'' & ''depth'' & ''test'''::tsquery)
Rows Removed by Filter: 50000
Planning Time: 0.193 ms
Execution Time: 488.596 ms
No difference? Index has not been used... Why is it not working? Ohh, looks to the partial index docs:
However, keep in mind that the predicate must match the conditions used in the queries that are supposed to benefit from the index. To be precise, a partial index can be used in a query only if the system can recognize that the WHERE condition of the query mathematically implies the predicate of the index. PostgreSQL does not have a sophisticated theorem prover that can recognize mathematically equivalent expressions that are written in different forms. (Not only is such a general theorem prover extremely difficult to create, it would probably be too slow to be of any real use.) The system can recognize simple inequality implications, for example "x < 1" implies "x < 2"; otherwise the predicate condition must exactly match part of the query's WHERE condition or the index will not be recognized as usable. Matching takes place at query planning time, not at run time. As a result, parameterized query clauses do not work with a partial index.
We have to add to query a condition that was used to create partial index: document.language = 'en':
>> EXPLAIN ANALYZE SELECT text, language
FROM public.document
WHERE
to_tsvector('english', text) @@ to_tsquery('english', 'fielded & window & lamp & depth & test ')
AND language = 'en'
LIMIT 1; QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Limit (cost=64.00..68.27 rows=1 width=103) (actual time=0.546..0.548 rows=0 loops=1)
-> Bitmap Heap Scan on document (cost=64.00..68.27 rows=1 width=103) (actual time=0.544..0.545 rows=0 loops=1)
Recheck Cond: ((to_tsvector('english'::regconfig, text) @@ '''field'' & ''window'' & ''lamp'' & ''depth'' & ''test'''::tsquery) AND ((language)::text = 'en'::text))
-> Bitmap Index Scan on ix_en_document_tsvector_text (cost=0.00..64.00 rows=1 width=0) (actual time=0.540..0.540 rows=0 loops=1)
Index Cond: (to_tsvector('english'::regconfig, text) @@ '''field'' & ''window'' & ''lamp'' & ''depth'' & ''test'''::tsquery)
Planning Time: 0.244 ms
Execution Time: 0.590 ms
Tsearch full text search with partial words
>> EXPLAIN ANALYZE SELECT text, language
FROM public.document
WHERE
to_tsvector('english', text) @@ to_tsquery('english', 'fielded & window & l:*')
AND language = 'en'
LIMIT 1;
QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------------------------
Bitmap Heap Scan on document (cost=168.00..172.27 rows=1 width=102) (actual time=5.207..5.210 rows=4 loops=1)
Recheck Cond: ((to_tsvector('english'::regconfig, text) @@ '''field'' & ''window'' & ''l'':*'::tsquery) AND ((language)::text = 'en'::text))
Heap Blocks: exact=4
-> Bitmap Index Scan on ix_en_document_tsvector_text (cost=0.00..168.00 rows=1 width=0) (actual time=5.202..5.202 rows=4 loops=1)
Index Cond: (to_tsvector('english'::regconfig, text) @@ '''field'' & ''window'' & ''l'':*'::tsquery)
Planning Time: 0.240 ms
Execution Time: 5.240 ms
>> SELECT id, text
FROM public.document
WHERE
to_tsvector('english', text) @@ to_tsquery('english', 'fielded & window & l:*')
AND language = 'en'
LIMIT 20;
id | text
-------+-----------------------------------------------------------------------------------------------------------
1 | degree ranch time tall depth men below dead window waste underline bat lamp putting field +
20152 | Law pony follow memory star whatever window sets oxygen longer word whom glass field actual +
21478 | Dried symbol willing design managed shade window pick share faster education drive field land everybody +
30293 | Pencil seen engineer labor image entire smallest serve field should riding smaller window imagine traffic+
Tsearch full text search results ranking
>> SELECT
id,
ts_rank_cd(to_tsvector('english', text), to_tsquery('english', 'fielded & wind:*')) rank,
text
FROM public.document
WHERE to_tsvector('english', text) @@ to_tsquery('english', 'fielded & wind:*')
ORDER BY rank DESC
LIMIT 20;
id | rank | text
--------+-------------+-----------------------------------------------------------------------------------------------------------
100002 | 0.1 | fielded window
9376 | 0.05 | Own mouse girl effect surprise physical newspaper forgot eat upper field element window simply unhappy +
96597 | 0.05 | Opinion fastened pencil rear more theory size window heading field understanding farm up position attack +
44626 | 0.033333335 | Symbol each halfway window swam spider field page shinning donkey chose until cow cabin congress +
80922 | 0.033333335 | Victory famous field shelter girl wind adventure he divide rear tip few studied ruler judge +
30293 | 0.025 | Pencil seen engineer labor image entire smallest serve field should riding smaller window imagine traffic+
1 | 0.016666668 | degree ranch time tall depth men below dead window waste underline bat lamp putting field +
21478 | 0.016666668 | Dried symbol willing design managed shade window pick share faster education drive field land everybody +
60059 | 0.016666668 | However hungry make proud kids come willing field officer row above highest round wind mile +
26001 | 0.014285714 | Earth earlier pocket might sense window way frog fire court family mouth field somebody recognize +
20152 | 0.014285714 | Law pony follow memory star whatever window sets oxygen longer word whom glass field actual +
37470 | 0.0125 | Farm weight balloon buried wind water donkey grain pig week should damage field was he +
49433 | 0.01 | Wind scientist leaving atom year bad child drink shore spirit field facing indicate wagon here +
37851 | 0.007142857 | Field cloud you wife rhythm upward applied weigh continued property replace ahead forgotten trip window +
text='fielded window' record was added manually to show best match result.
GIST vs GIN
We have created GIN index. But there is also GIST index option. Which one is better? It depends...
>> EXPLAIN ANALYZE SELECT text, language
FROM public.document
WHERE
to_tsvector('english', text) @@ to_tsquery('english', 'fielded & window & lamp & depth & test ')
AND language = 'en'
LIMIT 1;
QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------------------------
Limit (cost=0.28..8.30 rows=1 width=103) (actual time=2.699..2.700 rows=0 loops=1)
-> Index Scan using ix_en_document_tsvector_text on document (cost=0.28..8.30 rows=1 width=103) (actual time=2.697..2.697 rows=0 loops=1)
Index Cond: (to_tsvector('english'::regconfig, text) @@ '''field'' & ''window'' & ''lamp'' & ''depth'' & ''test'''::tsquery)
Planning Time: 0.274 ms
Execution Time: 2.730 ms
GIN seems to be a little bit faster. I don't think I could explain it better than the docs already does:
In choosing which index type to use, GiST or GIN, consider these performance differences:
- GIN index lookups are about three times faster than GiST
- GIN indexes take about three times longer to build than GiST
- GIN indexes are moderately slower to update than GiST indexes, but about 10 times slower if fast-update support was disabled (see Section 58.4.1 for details)
- GIN indexes are two-to-three times larger than GiST indexes
197 Dec 29, 2022
152 Oct 29, 2022
1.1k Jan 3, 2023
139 Jan 1, 2023
736 Jan 3, 2023
2 Jan 16, 2022
30 Nov 18, 2022
10.8k Jan 2, 2023
259 Dec 31, 2022
3.4k Dec 30, 2022
1.2k Jan 5, 2023
9 Sep 25, 2022
8 Jun 27, 2022
1 Jan 10, 2022
1 Oct 29, 2021
7 Nov 27, 2022
12 Dec 27, 2022
228 Jan 2, 2023
1 Dec 9, 2021
17 Aug 14, 2022
19 Sep 29, 2022
1 Oct 26, 2021
2.2k Dec 22, 2022
185 Jan 9, 2023
19 Jun 30, 2022
50 Dec 3, 2022
197 Dec 29, 2022
1.1k Dec 22, 2022