LIKE

Tests if a string matches a given pattern.

LIKE and RLIKE are similar. LIKE uses SQL patterns, whereas RLIKE uses POSIX regular expressions.

See also: RLIKE, REGEXP_COUNT, REGEXP_INSTR, REGEXP_SUBSTR, ISPREFIXOF.

Syntax

string_expr [ NOT ] LIKE string_test_expr

Arguments

Parameter

Description

string_expr

String to test

string_test_expr

Test pattern

Test patterns

Pattern

Description

%

match zero or more characters

_ (underscore)

match exactly one character

[A-Z]

match any character between A and Z inclusive. The - character between two other characters forms a range that matches all characters from the first character to the second. For example, [A-Z] matches all ASCII capital letters.

[^A-Z]

match any character not between A and Z

[abcde]

match any one of a b c d and e

[^abcde]

match any character that is not one of a b c d or e

[abcC-F]

match a b c or any character between C and F

  • \ (backslash) - escape character

    Using a backslash (\) indicates that the wildcard is interpreted as a regular character and not as a wildcard.

Returns

TRUE if the test string matches the pattern, or FALSE otherwise.

Notes

  • The test pattern must be literal string. If matching just the beginning of the string, use ISPREFIXOF which supports column references.

  • Starting with version 2020.3.1, Column references or complex expressions are also supported.

  • If the value is NULL, the result is NULL.

Examples

For these examples, assume a table named nba, with the following structure:

CREATE TABLE nba
(
   "Name" varchar(40),
   "Team" varchar(40),
   "Number" tinyint,
   "Position" varchar(2),
   "Age" tinyint,
   "Height" varchar(4),
   "Weight" real,
   "College" varchar(40),
   "Salary" float
 );

Here’s a peek at the table contents (Download nba.csv):

nba.csv

Avery Bradley

Boston Celtics

0

PG

25

2-Jun

180

Texas

7730337

Jae Crowder

Boston Celtics

99

SF

25

6-Jun

235

Marquette

6796117

John Holland

Boston Celtics

30

SG

27

5-Jun

205

Boston University

R.J. Hunter

Boston Celtics

28

SG

22

5-Jun

185

Georgia State

1148640

Jonas Jerebko

Boston Celtics

8

PF

29

10-Jun

231

5000000

Amir Johnson

Boston Celtics

90

PF

29

9-Jun

240

12000000

Jordan Mickey

Boston Celtics

55

PF

21

8-Jun

235

LSU

1170960

Kelly Olynyk

Boston Celtics

41

C

25

Jul-00

238

Gonzaga

2165160

Terry Rozier

Boston Celtics

12

PG

22

2-Jun

190

Louisville

1824360

Match the beginning of a string

nba=> SELECT "Name","Age","Salary","Team" FROM nba WHERE "Team" LIKE 'Portland%' LIMIT 5;
Name            | Age | Salary  | Team
----------------+-----+---------+-----------------------
Cliff Alexander |  20 |  525093 | Portland Trail Blazers
Al-Farouq Aminu |  25 | 8042895 | Portland Trail Blazers
Pat Connaughton |  23 |  625093 | Portland Trail Blazers
Allen Crabbe    |  24 |  947276 | Portland Trail Blazers
Ed Davis        |  27 | 6980802 | Portland Trail Blazers

Tip

ISPREFIXOF is a more performant way to match the beginning of a string, especially This example can be written as

SELECT "Name","Age","Salary","Team" FROM nba WHERE ISPREFIXOF('Portland',"Team") LIMIT 5;

Match a wildcard character by escaping

To match a wildcard, escape it with a backslash escape character:

nba=> SELECT "Name" FROM nba WHERE "Name" LIKE '%\_%';
Name            | Age | Salary  | Team
----------------+-----+---------+-----------------------
R.J._Hunter     |  22 | 1148640 | Boston Celtics

Negate with NOT

nba=> SELECT "Name","Age","Salary","Team" FROM nba WHERE "Team" NOT LIKE 'Portland%' LIMIT 5;
Name          | Age | Salary  | Team
--------------+-----+---------+---------------
Avery Bradley |  25 | 7730337 | Boston Celtics
Jae Crowder   |  25 | 6796117 | Boston Celtics
John Holland  |  27 |         | Boston Celtics
R.J. Hunter   |  22 | 1148640 | Boston Celtics
Jonas Jerebko |  29 | 5000000 | Boston Celtics

Match the middle of a string

nba=> SELECT "Name","Age","Salary","Team" FROM nba WHERE "Team" LIKE '%zz%' LIMIT 5;
Name           | Age | Salary  | Team
---------------+-----+---------+------------------
Jordan Adams   |  21 | 1404600 | Memphis Grizzlies
Tony Allen     |  34 | 5158539 | Memphis Grizzlies
Chris Andersen |  37 | 5000000 | Memphis Grizzlies
Matt Barnes    |  36 | 3542500 | Memphis Grizzlies
Vince Carter   |  39 | 4088019 | Memphis Grizzlies

Find players with a middle name or suffix

nba=> SELECT "Name","Age","Salary","Team" FROM nba WHERE "Name" LIKE '% % %';
Name                     | Age | Salary  | Team
-------------------------+-----+---------+----------------------
James Michael McAdoo     |  23 |  845059 | Golden State Warriors
Luc Richard Mbah a Moute |  29 |  947276 | Los Angeles Clippers
Larry Nance Jr.          |  23 | 1155600 | Los Angeles Lakers
Metta World Peace        |  36 |  947276 | Los Angeles Lakers
Glenn Robinson III       |  22 | 1100000 | Indiana Pacers
Johnny O'Bryant III      |  23 |  845059 | Milwaukee Bucks
Tim Hardaway Jr.         |  24 | 1304520 | Atlanta Hawks
Frank Kaminsky III       |  23 | 2612520 | Charlotte Hornets
Kelly Oubre Jr.          |  20 | 1920240 | Washington Wizards
Otto Porter Jr.          |  23 | 4662960 | Washington Wizards

Find NON-LITERAL patterns

nba=> CREATE TABLE t(x int not null, y text not null, z text not null);
nba=> INSERT INTO t VALUES (1,'abc','a'),(2,'abcd','bc');

Select rows in which z is a prefix of y:

nba=> SELECT * FROM t WHERE y LIKE z || '%';

x |  y  | z
-------------
1 | abc | a

Select rows in which y contains z as a substring:

nba=> SELECT * FROM t WHERE y LIKE z || '%';

x |  y   | z
--------------
1 | abc  | a
2 | abcd | bc

Values that contain wildcards as well:

nba=> CREATE TABLE patterns(x text not null);
nba=> INSERT INTO patterns values ('%'),('a%'),('%a');
nba=> SELECT x, 'abc' LIKE x FROM patterns;

x  |  ?column?
--------------
%  | 1
a% | 1
%a | 0