12.1. 簡介
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Full Text Searching (or justtext search) provides the capability to identify natural-languagedocuments_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 ofqueryandsimilarityare very flexible and depend on the specific application. The simplest search considersqueryas a set of words andsimilarityas the frequency of query words in the document.
Textual search operators have existed in databases for years.PostgreSQLhas~,~*,LIKE, andILIKEoperators 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.,satisfiesandsatisfy. You might miss documents that containsatisfies, although you probably would like to find them when searching forsatisfy. It is possible to useORto 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 intotokens. 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.PostgreSQLuses a_parser_to perform this step. A standard parser is provided, and custom parsers can be created for specific needs.
Converting tokens intolexemes. A lexeme is a string, just like a token, but it has beennormalized_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 assoresin 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.)PostgreSQLuses_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 forproximity 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 usingIspell.
Map phrases to a single word using a thesaurus.
Map different variations of a word to a canonical form using anIspelldictionary.
Map different variations of a word to a canonical form usingSnowballstemmer rules.
A data typetsvectoris provided for storing preprocessed documents, along with a typetsqueryfor representing processed queries (). There are many functions and operators available for these data types (), the most important of which is the match operator@@, which we introduce in. Full text searches can be accelerated using indexes ().
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 withinPostgreSQL, 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:
Actually, in these example queries,coalesceshould be used to prevent a singleNULLattribute from causing aNULLresult 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 insidePostgreSQL. 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 preprocessedtsvectorformat. Searching and ranking are performed entirely on thetsvectorrepresentation 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 thetsvectoras being the document, but of course it is only a compact representation of the full document.
Full text searching inPostgreSQLis based on the match operator@@, which returnstrueif atsvector(document) matches atsquery(query). It doesn't matter which data type is written first:
Observe that this match would not succeed if written as
since here no normalization of the wordratswill occur. The elements of atsvectorare lexemes, which are assumed already normalized, soratsdoes not matchrat.
The@@operator also supportstextinput, allowing explicit conversion of a text string totsvectorortsqueryto be skipped in simple cases. The variants available are:
The first two of these we saw already. The formtext@@tsqueryis equivalent toto_tsvector(x) @@ y. The formtext@@textis equivalent toto_tsvector(x) @@ plainto_tsquery(y).
Within atsquery, 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 queryfat & ! ratmatches documents that containfatbut notrat.
Searching for phrases is possible with the help of the<->(FOLLOWED BY)tsqueryoperator, which matches only if its arguments have matches that are adjacent and in the given order. For example:
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. Thephraseto_tsqueryfunction makes use of this operator to construct atsquerythat can match a multi-word phrase when some of the words are stop words. For example:
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 thetsqueryoperators. 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!xmatches only documents that do not containxanywhere. But!x <-> ymatchesyif it is not immediately after anx; an occurrence ofxelsewhere in the document does not prevent a match. Another example is thatx & ynormally only requires thatxandyboth appear somewhere in the document, but(x & y) <-> zrequiresxandyto match at the same place, immediately before az. Thus this query behaves differently fromx <-> z & y <-> z, which will match a document containing two separate sequencesx zandy z. (This specific query is useless as written, sincexandycould 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 bytext search configurations.PostgreSQLcomes with predefined configurations for many languages, and you can easily create your own configurations. (psql's\dFcommand shows all available configurations.)
Each text search function that depends on a configuration has an optionalregconfigargument, so that the configuration to use can be specified explicitly.default_text_search_configis 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 thecontrib/area of thePostgreSQLdistribution.) 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.
As the above example suggests, atsqueryis not just raw text, any more than atsvectoris. Atsquerycontains 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.) There are functionsto_tsquery,plainto_tsquery, andphraseto_tsquerythat are helpful in converting user-written text into a propertsquery, primarily by normalizing words appearing in the text. Similarly,to_tsvectoris used to parse and normalize a document string. So in practice a text search match would look more like this:
During installation an appropriate configuration is selected andis set accordingly inpostgresql.conf. If you are using the same text search configuration for the entire cluster you can use the value inpostgresql.conf. To use different configurations throughout the cluster but the same configuration within any one database, useALTER DATABASE ... SET. Otherwise, you can setdefault_text_search_configin each session.