Full Text Searching (or just text
search) provides the capability to identify natural-language
documents that satisfy a query, and optionally to sort them by relevance
to the query. The most common type of search is to find all
documents containing given query terms
and return them in order of their similarity to the query. Notions of
similarity are very flexible and depend on the
specific application. The simplest search considers
query as a set of words and
similarity as the frequency of query words in
Textual search operators have existed in databases for years.
operators for textual data types, but they lack many essential
properties required by modern information systems:
There is no linguistic support, even for English.
Regular expressions are not sufficient because they cannot
easily handle derived words, e.g.,
satisfy. You might miss documents that
you probably would like to find them when searching for
satisfy. It is possible to use
OR to search for multiple
derived forms, but this is tedious and error-prone (some
words can have several thousand derivatives).
They provide no ordering (ranking) of search results, which makes them ineffective when thousands of matching documents are found.
They tend to be slow because there is no index support, so they must process all documents for every search.
Full text indexing allows documents to be preprocessed and an index saved for later rapid searching. Preprocessing includes:
Parsing documents into tokens. It is useful to identify various classes of tokens, e.g., numbers, words, complex words, email addresses, so that they can be processed differently. In principle token classes depend on the specific application, but for most purposes it is adequate to use a predefined set of classes. PostgreSQL uses a parser to perform this step. A standard parser is provided, and custom parsers can be created for specific needs.
Converting tokens into
lexemes. A lexeme is
a string, just like a token, but it has been normalized so that different forms of the
same word are made alike. For example, normalization almost
always includes folding upper-case letters to lower-case,
and often involves removal of suffixes (such as
es in English). This allows searches to
find variant forms of the same word, without tediously
entering all the possible variants. Also, this step
typically eliminates stop words,
which are words that are so common that they are useless
for searching. (In short, then, tokens are raw fragments of
the document text, while lexemes are words that are
believed useful for indexing and searching.) PostgreSQL uses dictionaries to perform this step. Various
standard dictionaries are provided, and custom ones can be
created for specific needs.
Storing preprocessed documents optimized for searching. For example, each document can be represented as a sorted array of normalized lexemes. Along with the lexemes it is often desirable to store positional information to use for proximity ranking, so that a document that contains a more “dense” region of query words is assigned a higher rank than one with scattered query words.
Dictionaries allow fine-grained control over how tokens are normalized. With appropriate dictionaries, you can:
Define stop words that should not be indexed.
Map synonyms to a single word using Ispell.
Map phrases to a single word using a thesaurus.
Map different variations of a word to a canonical form using an Ispell dictionary.
Map different variations of a word to a canonical form using Snowball stemmer rules.
A data type
tsvector is provided for
storing preprocessed documents, along with a type
tsquery for representing processed queries
(Section 8.11). There are
many functions and operators available for these data types
the most important of which is the match operator
@@, which we introduce in Section 12.1.2. Full
text searches can be accelerated using indexes (Section 12.9).
A document is the unit of searching in a full text search system; for example, a magazine article or email message. The text search engine must be able to parse documents and store associations of lexemes (key words) with their parent document. Later, these associations are used to search for documents that contain query words.
For searches within PostgreSQL, a document is normally a textual field within a row of a database table, or possibly a combination (concatenation) of such fields, perhaps stored in several tables or obtained dynamically. In other words, a document can be constructed from different parts for indexing and it might not be stored anywhere as a whole. For example:
SELECT title || ' ' || author || ' ' || abstract || ' ' || body AS document FROM messages WHERE mid = 12; SELECT m.title || ' ' || m.author || ' ' || m.abstract || ' ' || d.body AS document FROM messages m, docs d WHERE mid = did AND mid = 12;
Actually, in these example queries,
coalesce should be used to prevent a single
NULL attribute from causing a
NULL result for the whole
Another possibility is to store the documents as simple text files in the file system. In this case, the database can be used to store the full text index and to execute searches, and some unique identifier can be used to retrieve the document from the file system. However, retrieving files from outside the database requires superuser permissions or special function support, so this is usually less convenient than keeping all the data inside PostgreSQL. Also, keeping everything inside the database allows easy access to document metadata to assist in indexing and display.
For text search purposes, each document must be reduced to
Searching and ranking are performed entirely on the
tsvector representation of a document
— the original text need only be retrieved when the document
has been selected for display to a user. We therefore often
speak of the
tsvector as being the
document, but of course it is only a compact representation of
the full document.
Full text searching in PostgreSQL is based on the match operator
@@, which returns
true if a
(document) matches a
It doesn't matter which data type is written first:
SELECT 'a fat cat sat on a mat and ate a fat rat'::tsvector @@ 'cat & rat'::tsquery; ?column? ---------- t SELECT 'fat & cow'::tsquery @@ 'a fat cat sat on a mat and ate a fat rat'::tsvector; ?column? ---------- f
As the above example suggests, a
tsquery is not just raw text, any more than a
tsvector is. A
tsquery contains search terms, which must be
already-normalized lexemes, and may combine multiple terms
using AND, OR, NOT, and FOLLOWED BY operators. (For syntax
details see Section 8.11.2.) There are
phraseto_tsquery that are helpful in
converting user-written text into a proper
tsquery, primarily by normalizing words appearing
in the text. Similarly,
to_tsvector is used to parse and normalize a
document string. So in practice a text search match would look
more like this:
SELECT to_tsvector('fat cats ate fat rats') @@ to_tsquery('fat & rat'); ?column? ---------- t
Observe that this match would not succeed if written as
SELECT 'fat cats ate fat rats'::tsvector @@ to_tsquery('fat & rat'); ?column? ---------- f
since here no normalization of the word
rats will occur. The elements of a
tsvector are lexemes, which are
assumed already normalized, so
rats does not match
@@ operator also supports
text input, allowing explicit
conversion of a text string to
be skipped in simple cases. The variants available are:
tsvector @@ tsquery tsquery @@ tsvector text @@ tsquery text @@ text
The first two of these we saw already. The form
tsquery is equivalent to
to_tsvector(x) @@ y. The form
text is equivalent to
to_tsvector(x) @@ plainto_tsquery(y).
& (AND) operator specifies that both its
arguments must appear in the document to have a match.
| (OR) operator
specifies that at least one of its arguments must appear, while
! (NOT) operator specifies
that its argument must not appear in order to have a match.
For example, the query
fat & !
rat matches documents that contain
fat but not
Searching for phrases is possible with the help of the
<-> (FOLLOWED BY)
tsquery operator, which matches only
if its arguments have matches that are adjacent and in the
given order. For example:
SELECT to_tsvector('fatal error') @@ to_tsquery('fatal <-> error'); ?column? ---------- t SELECT to_tsvector('error is not fatal') @@ to_tsquery('fatal <-> error'); ?column? ---------- f
There is a more general version of the FOLLOWED BY operator
having the form
N is an integer standing for
the difference between the positions of the matching lexemes.
<1> is the same as
<2> allows exactly one other lexeme to
appear between the matches, and so on. The
phraseto_tsquery function makes use of this
operator to construct a
that can match a multi-word phrase when some of the words are
stop words. For example:
SELECT phraseto_tsquery('cats ate rats'); phraseto_tsquery ------------------------------- 'cat' <-> 'ate' <-> 'rat' SELECT phraseto_tsquery('the cats ate the rats'); phraseto_tsquery ------------------------------- 'cat' <-> 'ate' <2> 'rat'
A special case that's sometimes useful is that
<0> can be used to require that two
patterns match the same word.
Parentheses can be used to control nesting of the
tsquery operators. Without
| binds least
! most tightly.
It's worth noticing that the AND/OR/NOT operators mean
something subtly different when they are within the arguments
of a FOLLOWED BY operator than when they are not, because
within FOLLOWED BY the exact position of the match is
significant. For example, normally
!x matches only documents that do not contain
x anywhere. But
!x <-> y matches
y if it is not immediately after an
x; an occurrence of
x elsewhere in the document does not prevent a
match. Another example is that
y normally only requires that
appear somewhere in the document, but
& y) <-> z requires
y to match
at the same place, immediately before a
z. Thus this query behaves differently from
x <-> z & y <-> z,
which will match a document containing two separate sequences
x z and
z. (This specific query is useless as written, since
y could not match at the same place; but with
more complex situations such as prefix-match patterns, a query
of this form could be useful.)
The above are all simple text search examples. As mentioned
before, full text search functionality includes the ability to
do many more things: skip indexing certain words (stop words),
process synonyms, and use sophisticated parsing, e.g., parse
based on more than just white space. This functionality is
controlled by text search
configurations. PostgreSQL comes with predefined
configurations for many languages, and you can easily create
your own configurations. (psql's
command shows all available configurations.)
During installation an appropriate configuration is selected
is set accordingly in
postgresql.conf. If you are using the same
text search configuration for the entire cluster you can use
the value in
use different configurations throughout the cluster but the
same configuration within any one database, use
ALTER DATABASE ... SET. Otherwise, you can set
default_text_search_config in each
Each text search function that depends on a configuration
has an optional
so that the configuration to use can be specified explicitly.
default_text_search_config is used
only when this argument is omitted.
To make it easier to build custom text search configurations, a configuration is built up from simpler database objects. PostgreSQL's text search facility provides four types of configuration-related database objects:
Text search parsers break documents into tokens and classify each token (for example, as words or numbers).
Text search dictionaries convert tokens to normalized form and reject stop words.
Text search templates provide the functions underlying dictionaries. (A dictionary simply specifies a template and a set of parameters for the template.)
Text search configurations select a parser and a set of dictionaries to use to normalize the tokens produced by the parser.
Text search parsers and templates are built from low-level C
functions; therefore it requires C programming ability to
develop new ones, and superuser privileges to install one into
a database. (There are examples of add-on parsers and templates
contrib/ area of the
Since dictionaries and configurations just parameterize and
connect together some underlying parsers and templates, no
special privilege is needed to create a new dictionary or
configuration. Examples of creating custom dictionaries and
configurations appear later in this chapter.