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. This module is considered trusted, that is, it can be installed by non-superusers who have CREATE privilege on the current database. 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. 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 . The functions provided by the pg_trgm module are shown in , the operators in . Function Description similarity 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 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 word_similarity ( text, text ) real Returns a number that indicates the greatest similarity between the set of trigrams in the first string and any continuous extent of an ordered set of trigrams in the second string. For details, see the explanation below. strict_word_similarity strict_word_similarity ( text, text ) real Same as word_similarity, but forces extent boundaries to match word boundaries. Since we don't have cross-word trigrams, this function actually returns greatest similarity between first string and any continuous extent of words of the second string. show_limit 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; instead use SHOW pg_trgm.similarity_threshold.) set_limit 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; instead use SET pg_trgm.similarity_threshold.) Consider the following example: # SELECT word_similarity('word', 'two words'); word_similarity ----------------- 0.8 (1 row) In the first string, the set of trigrams is {" w"," wo","wor","ord","rd "}. In the second string, the ordered set of trigrams is {" t"," tw","two","wo "," w"," wo","wor","ord","rds","ds "}. The most similar extent of an ordered set of trigrams in the second string is {" w"," wo","wor","ord"}, and the similarity is 0.8. This function returns a value that can be approximately understood as the greatest similarity between the first string and any substring of the second string. However, this function does not add padding to the boundaries of the extent. Thus, the number of additional characters present in the second string is not considered, except for the mismatched word boundaries. At the same time, strict_word_similarity selects an extent of words in the second string. In the example above, strict_word_similarity would select the extent of a single word 'words', whose set of trigrams is {" w"," wo","wor","ord","rds","ds "}. # SELECT strict_word_similarity('word', 'two words'), similarity('word', 'words'); strict_word_similarity | similarity ------------------------+------------ 0.571429 | 0.571429 (1 row) Thus, the strict_word_similarity function is useful for finding the similarity to whole words, while word_similarity is more suitable for finding the similarity for parts of words. Operator 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 the similarity between the trigram set in the first argument and a continuous extent of an ordered trigram set in the second argument 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 boolean Returns true if its second argument has a continuous extent of an ordered trigram set that matches word boundaries, and its similarity to the trigram set of the first argument is greater than the current strict word similarity threshold set by the pg_trgm.strict_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. text <<<-> text real Returns the distance between the arguments, that is one minus the strict_word_similarity() value. text <->>> text real Commutator of the <<<-> operator. pg_trgm.similarity_threshold (real) pg_trgm.similarity_threshold configuration parameter 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) pg_trgm.word_similarity_threshold configuration parameter Sets the current word similarity threshold that is used by the <% and %> operators. The threshold must be between 0 and 1 (default is 0.6). pg_trgm.strict_word_similarity_threshold (real) pg_trgm.strict_word_similarity_threshold configuration parameter Sets the current strict word similarity threshold that is used by the <<% and %>> operators. The threshold must be between 0 and 1 (default is 0.5). 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. The similarity comparisons are case-insensitive in a default build of pg_trgm. Inequality operators are not supported. Note that those indexes may not be as efficient as regular B-tree indexes for equality operator. 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); gist_trgm_ops GiST opclass approximates a set of trigrams as a bitmap signature. Its optional integer parameter siglen determines the signature length in bytes. The default length is 12 bytes. Valid values of signature length are between 1 and 2024 bytes. Longer signatures lead to a more precise search (scanning a smaller fraction of the index and fewer heap pages), at the cost of a larger index. Example of creating such an index with a signature length of 32 bytes: CREATE INDEX trgm_idx ON test_trgm USING GIST (t gist_trgm_ops(siglen=32)); 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 or strict word similarity. Typical queries are: SELECT t, word_similarity('word', t) AS sml FROM test_trgm WHERE 'word' <% t ORDER BY sml DESC, t; and SELECT t, strict_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 for which there is a continuous extent in the corresponding ordered trigram set that is sufficiently similar to the trigram set of word, sorted from best match to worst. The index will be used to make this a fast operation even over very large data sets. Possible variants of the above queries are: SELECT t, 'word' <<-> t AS dist FROM test_trgm ORDER BY dist LIMIT 10; and 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. 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. 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. GiST Development Site Tsearch2 Development Site Oleg Bartunov oleg@sai.msu.su, Moscow, Moscow University, Russia Teodor Sigaev teodor@sigaev.ru, Moscow, Delta-Soft Ltd.,Russia Alexander Korotkov a.korotkov@postgrespro.ru, Moscow, Postgres Professional, Russia Documentation: Christopher Kings-Lynne This module is sponsored by Delta-Soft Ltd., Moscow, Russia.