LOWER LIKE vs iLIKE

The answer depends on many factors like Postgres version, encoding and locale - LC_COLLATE in particular.

The bare expression lower(description) LIKE '%abc%' is typically a bit faster than description ILIKE '%abc%', and either is a bit faster than the equivalent regular expression: description ~* 'abc'. This matters for sequential scans where the expression has to be evaluated for every tested row.

But for big tables like you demonstrate in your answer one would certainly use an index. For arbitrary patterns (not only left-anchored) I suggest a trigram index using the additional module pg_trgm. Then we talk about milliseconds instead of seconds and the difference between the above expressions is nullified.

GIN and GiST indexes (using the gin_trgm_ops or gist_trgm_ops operator classes) support LIKE (~~), ILIKE (~~*), ~, ~* (and some more variants) alike. With a trigram GIN index on description (typically bigger than GiST, but faster for reads), your query would use description ILIKE 'case_insensitive_pattern'.

Related:

• PostgreSQL LIKE query performance variations
• Similar UTF-8 strings for autocomplete field

Basics for pattern matching in Postgres:

• Pattern matching with LIKE, SIMILAR TO or regular expressions in PostgreSQL

When working with said trigram index it's typically more practical to work with:

description ILIKE '%abc%'

Or with the case-insensitive regexp operator (without % wildcards):

description ~* 'abc'

An index on (description) does not support queries on lower(description) like:

lower(description) LIKE '%abc%'

And vice versa.

With predicates on lower(description) exclusively, the expression index is the slightly better option.

In all other cases, an index on (description) is preferable as it supports both case-sensitive and -insensitive predicates.

According to my tests (ten of each query), LOWER LIKE is about 17% faster than iLIKE.

Explanation

I created a million rows contain some random mixed text data:

require 'securerandom'inserts = []1000000.times do |i|        inserts << "(1, 'fake', '#{SecureRandom.urlsafe_base64(64)}')"endsql = "insert into books (user_id, title, description) values #{inserts.join(', ')}"ActiveRecord::Base.connection.execute(sql)

Verify the number of rows:

my_test_db=# select count(id) from books ;  count  --------- 1000009

(Yes, I have nine extra rows from other tests - not a problem.)

Example query and results:

my_test_db=# SELECT "books".* FROM "books" WHERE "books"."published" = 'f'my_test_db=# and (LOWER(description) LIKE '%abcde%') ;   id    | user_id | title |                                      description                                       | published ---------+---------+-------+----------------------------------------------------------------------------------------+------ 1232322 |       1 | fake  | 5WRGr7oCKABcdehqPKsUqV8ji61rsNGS1TX6pW5LJKrspOI_ttLNbaSyRz1BwTGQxp3OaxW7Xl6fzVpCu9y3fA | f 1487103 |       1 | fake  | J6q0VkZ8-UlxIMZ_MFU_wsz_8MP3ZBQvkUo8-2INiDIp7yCZYoXqRyp1Lg7JyOwfsIVdpPIKNt1uLeaBCdelPQ | f 1817819 |       1 | fake  | YubxlSkJOvmQo1hkk5pA1q2mMK6T7cOdcU3ADUKZO8s3otEAbCdEcmm72IOxiBdaXSrw20Nq2Lb383lq230wYg | f

Results for LOWER LIKE

my_test_db=# EXPLAIN ANALYZE SELECT "books".* FROM "books" WHERE "books"."published" = 'f' and (LOWER(description) LIKE '%abcde%') ;                                                   QUERY PLAN                                                   ---------------------------------------------------------------------------------------------------------------- Seq Scan on books  (cost=0.00..32420.14 rows=1600 width=117) (actual time=938.627..4114.038 rows=3 loops=1)   Filter: ((NOT published) AND (lower(description) ~~ '%abcde%'::text))   Rows Removed by Filter: 1000006 Total runtime: 4114.098 ms

Results for iLIKE

my_test_db=# EXPLAIN ANALYZE SELECT "books".* FROM "books" WHERE "books"."published" = 'f' and (description iLIKE '%abcde%') ;                                                   QUERY PLAN                                                   ---------------------------------------------------------------------------------------------------------------- Seq Scan on books  (cost=0.00..29920.11 rows=100 width=117) (actual time=1147.612..4986.771 rows=3 loops=1)   Filter: ((NOT published) AND (description ~~* '%abcde%'::text))   Rows Removed by Filter: 1000006 Total runtime: 4986.831 ms

Database info disclosure

Postgres version:

my_test_db=# select version();                                                                                 version-------------------------------------------------------------------------------------------------------------------------------------------------------------------------- PostgreSQL 9.2.4 on x86_64-apple-darwin12.4.0, compiled by i686-apple-darwin11-llvm-gcc-4.2 (GCC) 4.2.1 (Based on Apple Inc. build 5658) (LLVM build 2336.11.00), 64-bit

Collation setting:

my_test_db=# select datcollate from pg_database where datname = 'my_test_db'; datcollate  ------------- en_CA.UTF-8

Table definition:

my_test_db=# \d books                                       Table "public.books"   Column    |            Type             |                       Modifiers-------------+-----------------------------+------------------------------------------------------- id          | integer                     | not null default nextval('books_id_seq'::regclass) user_id     | integer                     | not null title       | character varying(255)      | not null description | text                        | not null default ''::text published   | boolean                     | not null default falseIndexes:    "books_pkey" PRIMARY KEY, btree (id)

In my rails Project. ILIKE is almost 10x faster then LOWER LIKE, I add a GIN index on entities.name column

> Entity.where("LOWER(name) LIKE ?", name.strip.downcase).limit(1).firstEntity Load (2443.9ms)  SELECT  "entities".* FROM "entities" WHERE (lower(name) like 'baidu') ORDER BY "entities"."id" ASC LIMIT $1  [["LIMIT", 1]]

> Entity.where("name ILIKE ?", name.strip).limit(1).firstEntity Load (285.0ms)  SELECT  "entities".* FROM "entities" WHERE (name ilike 'Baidu') ORDER BY "entities"."id" ASC LIMIT $1  [["LIMIT", 1]]

# explain analyze SELECT  "entities".* FROM "entities" WHERE (name ilike 'Baidu') ORDER BY "entities"."id" ASC LIMIT 1;                                                                   QUERY PLAN------------------------------------------------------------------------------------------------------------------------------------------------ Limit  (cost=3186.03..3186.04 rows=1 width=1588) (actual time=7.812..7.812 rows=1 loops=1)   ->  Sort  (cost=3186.03..3187.07 rows=414 width=1588) (actual time=7.811..7.811 rows=1 loops=1)         Sort Key: id         Sort Method: quicksort  Memory: 26kB         ->  Bitmap Heap Scan on entities  (cost=1543.21..3183.96 rows=414 width=1588) (actual time=7.797..7.805 rows=1 loops=1)               Recheck Cond: ((name)::text ~~* 'Baidu'::text)               Rows Removed by Index Recheck: 6               Heap Blocks: exact=7               ->  Bitmap Index Scan on index_entities_on_name  (cost=0.00..1543.11 rows=414 width=0) (actual time=7.787..7.787 rows=7 loops=1)                     Index Cond: ((name)::text ~~* 'Baidu'::text) Planning Time: 6.375 ms Execution Time: 7.874 ms(12 rows)

GIN index is really helpful to improve ILIKE performance