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.
LightDB 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. LightDB 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.)
LightDB 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 9.11). There are many
functions and operators available for these data types
(Section 10.13), the most important of which is
the match operator @@, which we introduce in
Section 13.1.2. Full text searches can be accelerated
using indexes (Section 13.9).
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 LightDB, 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 LightDB. 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 LightDB 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 9.11.2.) 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. LightDB comes with predefined
configurations for many languages, and you can easily create your own
configurations. (ltsql's \dF command
shows all available configurations.)
During installation an appropriate configuration is selected and
default_text_search_config is set accordingly
in lightdb.conf. If you are using the same text search
configuration for the entire cluster you can use the value in
lightdb.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.
We provide two configurations for text search parsers. They are set accordingly in lightdb.conf. You can modify this value during the session:
lightdb_tsearch_non_stopwords is used to customize non-stop words, which is just the opposite of stop words.
lightdb_tsearch_word_superpose is used to overlay the effect of using stop words and non-stop words. This configuration does not support chinese text parsing.
To make it easier to build custom text search configurations, a configuration is built up from simpler database objects. LightDB'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
LightDB 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.