pg_trgm

PostgreSQL 9.1.0 Documentation
Prev | Fast Backward | Appendix F. Additional Supplied Modules | Fast Forward | Next

# F.35. pg_trgm

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

## F.35.1. 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: A string 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` ".

## F.35.2. Functions and Operators

The functions provided by the `pg_trgm` module are shown in Table F-22, the operators in Table F-23.

Table F-22. `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.)
`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.
`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.

Table F-23. `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 `set_limit`.
`text` `<->` `text` | `real` | Returns the "distance" between the arguments, that is one minus the `similarity()` value.

## F.35.3. 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` and `ILIKE` 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.

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.

The choice between GiST and GIN indexing depends on the relative performance characteristics of GiST and GIN, which are discussed elsewhere. As a rule of thumb, a GIN index is faster to search than a GiST index, but slower to build or update; so GIN is better suited for static data and GiST for often-updated data.

## F.35.4. 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.35.5. 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.35.6. Authors

Oleg Bartunov `<oleg@sai.msu.su>`, Moscow, Moscow University, Russia

Teodor Sigaev `<teodor@sigaev.ru>`, Moscow, Delta-Soft Ltd.,Russia

Documentation: Christopher Kings-Lynne

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

Prev | Home | Next
pg_test_fsync | Up | pg_upgrade