Introduction

12.1.1. What Is a Document?

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;

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.

12.1.2. Basic Text Matching

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 < N > , where 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.)

12.1.3. Configurations

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.