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 query and
   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 the document.
  
   Textual search operators have existed in databases for years.
   PostgreSQL has
   ~, ~*, LIKE, and
   ILIKE 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.,
     satisfies and satisfy. You might
     miss documents that contain satisfies, although 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 s or 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
   (Section 9.13), 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 m.mid = d.did AND m.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 document.
    
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 the
    preprocessed tsvector format.  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 tsvector
    (document) matches a tsquery (query).
    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
    functions to_tsquery, plainto_tsquery,
    and 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 rat.
   
    The @@ operator also
    supports text input, allowing explicit conversion of a text
    string to tsvector or tsquery 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 text @@ tsquery
    is equivalent to to_tsvector(x) @@ y.
    The form text @@ text
    is equivalent to to_tsvector(x) @@ plainto_tsquery(y).
   
    Within a tsquery, the & (AND) operator
    specifies that both its arguments must appear in the document to have a
    match.  Similarly, the | (OR) operator specifies that
    at least one of its arguments must appear, while the ! (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 rat.
   
    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 <,
    where N>N is an integer standing for the difference between
    the positions of the matching lexemes.  <1> is
    the same as <->, while <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 tsquery 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 parentheses, | binds least tightly,
    then &, then <->,
    and ! 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 x & y normally only requires that x
    and y both appear somewhere in the document, but
    (x & y) <-> z requires x
    and 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
    y z.  (This specific query is useless as written,
    since x and 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 \dF command
    shows all available configurations.)
   
    During installation an appropriate configuration is selected and
    default_text_search_config 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
    postgresql.conf.  To 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 session.
   
    Each text search function that depends on a configuration has an optional
    regconfig argument, 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 in the contrib/ area of the
    PostgreSQL distribution.)  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.