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

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(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 "}.

# 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(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

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:

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 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.

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.31.6. References

GiST Development Site http://www.sai.msu.su/~megera/postgres/gist/

Tsearch2 Development Site http://www.sai.msu.su/~megera/postgres/gist/tsearch/V2/

F.31.7. Authors

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.\