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 표 F.24, the operators
in 표 F.25.
표 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
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.
표 F.25. pg_trgm
Operators
Operator Description |
---|
Returns |
Returns |
Commutator of the |
Returns |
Commutator of the |
Returns the “distance” between the arguments, that is
one minus the |
Returns the “distance” between the arguments, that is
one minus the |
Commutator of the |
Returns the “distance” between the arguments, that is
one minus the |
Commutator of the |
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).
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);
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 http://www.sai.msu.su/~megera/postgres/gist/
Tsearch2 Development Site http://www.sai.msu.su/~megera/postgres/gist/tsearch/V2/
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.