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F.31. pg_trgm

The pg_trgm module provides functions and operators for determining the similarity of alphanumeric text based on trigram matching, as well as index operator classes that support fast searching for similar strings.

F.31.1. Trigram (or Trigraph) Concepts

A trigram is a group of three consecutive characters taken from a string. We can measure the similarity of two strings by counting the number of trigrams they share. This simple idea turns out to be very effective for measuring the similarity of words in many natural languages.

Note: pg_trgm ignores non-word characters (non-alphanumerics) when extracting trigrams from a string. Each word is considered to have two spaces prefixed and one space suffixed when determining the set of trigrams contained in the string. For example, the set of trigrams in the string "cat" is " c", " ca", "cat", and "at ". The set of trigrams in the string "foo|bar" is " f", " fo", "foo", "oo ", " b", " ba", "bar", and "ar ".

F.31.2. Functions and Operators

The functions provided by the pg_trgm module are shown in Table F-25, the operators in Table F-26.

Table F-25. pg_trgm Functions

Function Returns Description
similarity(text, text) real Returns a number that indicates how similar the two arguments are. The range of the result is zero (indicating that the two strings are completely dissimilar) to one (indicating that the two strings are identical).
show_trgm(text) text[] Returns an array of all the trigrams in the given string. (In practice this is seldom useful except for debugging.)
word_similarity(text, text) real Returns a number that indicates how similar the first string to the most similar word of the second string. The function searches in the second string a most similar word not a most similar substring. The range of the result is zero (indicating that the two strings are completely dissimilar) to one (indicating that the first string is identical to one of the words of the second string).
show_limit() real Returns the current similarity threshold used by the % operator. This sets the minimum similarity between two words for them to be considered similar enough to be misspellings of each other, for example (deprecated).
set_limit(real) real Sets the current similarity threshold that is used by the % operator. The threshold must be between 0 and 1 (default is 0.3). Returns the same value passed in (deprecated).

Table F-26. pg_trgm Operators

Operator Returns Description
text % text boolean Returns true if its arguments have a similarity that is greater than the current similarity threshold set by pg_trgm.similarity_threshold.
text <% text boolean Returns true if its first argument has the similar word in the second argument and they have a similarity that is greater than the current word similarity threshold set by pg_trgm.word_similarity_threshold parameter.
text %> text boolean Commutator of the <% operator.
text <-> text real Returns the "distance" between the arguments, that is one minus the similarity() value.
text <<-> text real Returns the "distance" between the arguments, that is one minus the word_similarity() value.
text <->> text real Commutator of the <<-> operator.

F.31.3. GUC Parameters

pg_trgm.similarity_threshold (real)

Sets the current similarity threshold that is used by the % operator. The threshold must be between 0 and 1 (default is 0.3).

pg_trgm.word_similarity_threshold (real)

Sets the current word similarity threshold that is used by <% and %> operators. The threshold must be between 0 and 1 (default is 0.6).

F.31.4. Index Support

The pg_trgm module provides GiST and GIN index operator classes that allow you to create an index over a text column for the purpose of very fast similarity searches. These index types support the above-described similarity operators, and additionally support trigram-based index searches for LIKE, ILIKE, ~ and ~* queries. (These indexes do not support equality nor simple comparison operators, so you may need a regular B-tree index too.)

Example:

CREATE TABLE test_trgm (t text);
CREATE INDEX trgm_idx ON test_trgm USING GIST (t gist_trgm_ops);

or

CREATE INDEX trgm_idx ON test_trgm USING GIN (t gin_trgm_ops);

At this point, you will have an index on the t column that you can use for similarity searching. A typical query is

SELECT t, similarity(t, 'word') AS sml
  FROM test_trgm
  WHERE t % 'word'
  ORDER BY sml DESC, t;

This will return all values in the text column that are sufficiently similar to word, sorted from best match to worst. The index will be used to make this a fast operation even over very large data sets.

A variant of the above query is

SELECT t, t <-> 'word' AS dist
  FROM test_trgm
  ORDER BY dist LIMIT 10;

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.

Also you can use an index on the t column for word similarity. For example:

SELECT t, word_similarity('word', t) AS sml
  FROM test_trgm
  WHERE 'word' <% t
  ORDER BY sml DESC, t;

This will return all values in the text column that have a word which sufficiently similar to word, sorted from best match to worst. The index will be used to make this a fast operation even over very large data sets.

A variant of the above query is

SELECT t, 'word' <<-> t AS dist
  FROM test_trgm
  ORDER BY dist LIMIT 10;

This can be implemented quite efficiently by GiST indexes, but not by GIN indexes.

Beginning in PostgreSQL 9.1, these index types also support index searches for LIKE and ILIKE, for example

SELECT * FROM test_trgm WHERE t LIKE '%foo%bar';

The index search works by extracting trigrams from the search string and then looking these up in the index. The more trigrams in the search string, the more effective the index search is. Unlike B-tree based searches, the search string need not be left-anchored.

Beginning in PostgreSQL 9.3, these index types also support index searches for regular-expression matches (~ and ~* operators), for example

SELECT * FROM test_trgm WHERE t ~ '(foo|bar)';

The index search works by extracting trigrams from the regular expression and then looking these up in the index. The more trigrams that can be extracted from the regular expression, the more effective the index search is. Unlike B-tree based searches, the search string need not be left-anchored.

For both LIKE and regular-expression searches, keep in mind that a pattern with no extractable trigrams will degenerate to a full-index scan.

The choice between GiST and GIN indexing depends on the relative performance characteristics of GiST and GIN, which are discussed elsewhere.

F.31.5. Text Search Integration

Trigram matching is a very useful tool when used in conjunction with a full text index. In particular it can help to recognize misspelled input words that will not be matched directly by the full text search mechanism.

The first step is to generate an auxiliary table containing all the unique words in the documents:

CREATE TABLE words AS SELECT word FROM
        ts_stat('SELECT to_tsvector(''simple'', bodytext) FROM documents');

where documents is a table that has a text field bodytext that we wish to search. The reason for using the simple configuration with the to_tsvector function, instead of using a language-specific configuration, is that we want a list of the original (unstemmed) words.

Next, create a trigram index on the word column:

CREATE INDEX words_idx ON words USING GIN (word gin_trgm_ops);

Now, a SELECT query similar to the previous example can be used to suggest spellings for misspelled words in user search terms. A useful extra test is to require that the selected words are also of similar length to the misspelled word.

Note: Since the words table has been generated as a separate, static table, it will need to be periodically regenerated so that it remains reasonably up-to-date with the document collection. Keeping it exactly current is usually unnecessary.

F.31.7. Authors

Oleg Bartunov , Moscow, Moscow University, Russia

Teodor Sigaev , Moscow, Delta-Soft Ltd.,Russia

Alexander Korotkov , Moscow, Postgres Professional, Russia

Documentation: Christopher Kings-Lynne

This module is sponsored by Delta-Soft Ltd., Moscow, Russia.

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