F.31. pg_trgm
pg_trgm 模組提供了用於根據 trigram 配對決定包含字母及數字文字內容相似性的函數和運算子,以及支援快速搜索相似字串的索引運算子類。

F.31.1. Trigram (or Trigraph) Concepts

trigram 是從字串中提取的一組三個連續字元。我們可以透過計算兩個字串共享的三連詞的數量來衡量它們的相似性。這個簡單的想法對測量許多自然語言中單詞的相似性非常有用。

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.24, the operators in Table F.25.

Table F.24. 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 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(text, text)
real
Same as word_similarity(text, text), 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()
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).
Consider the following example:
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# SELECT word_similarity('word', 'two words');
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word_similarity
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-----------------
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0.8
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(1 row)
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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(text, text) selects an extent of words in the second string. In the example above, strict_word_similarity(text, text) would select the extent of a single word 'words', whose set of trigrams is {" w"," wo","wor","ord","rds","ds "}.
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# SELECT strict_word_similarity('word', 'two words'), similarity('word', 'words');
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strict_word_similarity | similarity
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------------------------+------------
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0.571429 | 0.571429
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(1 row)
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Thus, the strict_word_similarity(text, text) function is useful for finding the similarity to whole words, while word_similarity(text, text) is more suitable for finding the similarity for parts of words.

Table F.25. 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 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.

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 the <% and %> operators. The threshold must be between 0 and 1 (default is 0.6).pg_trgm.strict_word_similarity_threshold (real)
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).

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:
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CREATE TABLE test_trgm (t text);
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CREATE INDEX trgm_idx ON test_trgm USING GIST (t gist_trgm_ops);
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or
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CREATE INDEX trgm_idx ON test_trgm USING GIN (t gin_trgm_ops);
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At this point, you will have an index on the t column that you can use for similarity searching. A typical query is
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SELECT t, similarity(t, 'word') AS sml
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FROM test_trgm
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WHERE t % 'word'
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ORDER BY sml DESC, t;
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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
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SELECT t, t <-> 'word' AS dist
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FROM test_trgm
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ORDER BY dist LIMIT 10;
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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:
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SELECT t, word_similarity('word', t) AS sml
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FROM test_trgm
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WHERE 'word' <% t
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ORDER BY sml DESC, t;
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and
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SELECT t, strict_word_similarity('word', t) AS sml
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FROM test_trgm
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WHERE 'word' <<% t
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ORDER BY sml DESC, t;
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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:
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SELECT t, 'word' <<-> t AS dist
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FROM test_trgm
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ORDER BY dist LIMIT 10;
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and
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SELECT t, 'word' <<<-> t AS dist
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FROM test_trgm
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ORDER BY dist LIMIT 10;
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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
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SELECT * FROM test_trgm WHERE t LIKE '%foo%bar';
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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
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SELECT * FROM test_trgm WHERE t ~ '(foo|bar)';
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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:
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CREATE TABLE words AS SELECT word FROM
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ts_stat('SELECT to_tsvector(''simple'', bodytext) FROM documents');
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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:
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CREATE INDEX words_idx ON words USING GIN (word gin_trgm_ops);
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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.31.6. References

F.31.7. Authors

Oleg Bartunov <[email protected]>, Moscow, Moscow University, Russia
Teodor Sigaev <[email protected]>, Moscow, Delta-Soft Ltd.,Russia
Alexander Korotkov <[email protected]>, Moscow, Postgres Professional, Russia
Documentation: Christopher Kings-Lynne
This module is sponsored by Delta-Soft Ltd., Moscow, Russia.\