full text search text search 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 (). 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 (). document text search 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 .) 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.) 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 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. The examples in the previous section illustrated full text matching using simple constant strings. This section shows how to search table data, optionally using indexes. It is possible to do a full text search without an index. A simple query to print the title of each row that contains the word friend in its body field is: SELECT title FROM pgweb WHERE to_tsvector('english', body) @@ to_tsquery('english', 'friend'); This will also find related words such as friends and friendly, since all these are reduced to the same normalized lexeme. The query above specifies that the english configuration is to be used to parse and normalize the strings. Alternatively we could omit the configuration parameters: SELECT title FROM pgweb WHERE to_tsvector(body) @@ to_tsquery('friend'); This query will use the configuration set by . A more complex example is to select the ten most recent documents that contain create and table in the title or body: SELECT title FROM pgweb WHERE to_tsvector(title || ' ' || body) @@ to_tsquery('create & table') ORDER BY last_mod_date DESC LIMIT 10; For clarity we omitted the coalesce function calls which would be needed to find rows that contain NULL in one of the two fields. Although these queries will work without an index, most applications will find this approach too slow, except perhaps for occasional ad-hoc searches. Practical use of text searching usually requires creating an index. We can create a GIN index () to speed up text searches: CREATE INDEX pgweb_idx ON pgweb USING GIN (to_tsvector('english', body)); Notice that the 2-argument version of to_tsvector is used. Only text search functions that specify a configuration name can be used in expression indexes (). This is because the index contents must be unaffected by . If they were affected, the index contents might be inconsistent because different entries could contain tsvectors that were created with different text search configurations, and there would be no way to guess which was which. It would be impossible to dump and restore such an index correctly. Because the two-argument version of to_tsvector was used in the index above, only a query reference that uses the 2-argument version of to_tsvector with the same configuration name will use that index. That is, WHERE to_tsvector('english', body) @@ 'a & b' can use the index, but WHERE to_tsvector(body) @@ 'a & b' cannot. This ensures that an index will be used only with the same configuration used to create the index entries. It is possible to set up more complex expression indexes wherein the configuration name is specified by another column, e.g.: CREATE INDEX pgweb_idx ON pgweb USING GIN (to_tsvector(config_name, body)); where config_name is a column in the pgweb table. This allows mixed configurations in the same index while recording which configuration was used for each index entry. This would be useful, for example, if the document collection contained documents in different languages. Again, queries that are meant to use the index must be phrased to match, e.g., WHERE to_tsvector(config_name, body) @@ 'a & b'. Indexes can even concatenate columns: CREATE INDEX pgweb_idx ON pgweb USING GIN (to_tsvector('english', title || ' ' || body)); Another approach is to create a separate tsvector column to hold the output of to_tsvector. To keep this column automatically up to date with its source data, use a stored generated column. This example is a concatenation of title and body, using coalesce to ensure that one field will still be indexed when the other is NULL: ALTER TABLE pgweb ADD COLUMN textsearchable_index_col tsvector GENERATED ALWAYS AS (to_tsvector('english', coalesce(title, '') || ' ' || coalesce(body, ''))) STORED; Then we create a GIN index to speed up the search: CREATE INDEX textsearch_idx ON pgweb USING GIN (textsearchable_index_col); Now we are ready to perform a fast full text search: SELECT title FROM pgweb WHERE textsearchable_index_col @@ to_tsquery('create & table') ORDER BY last_mod_date DESC LIMIT 10; One advantage of the separate-column approach over an expression index is that it is not necessary to explicitly specify the text search configuration in queries in order to make use of the index. As shown in the example above, the query can depend on default_text_search_config. Another advantage is that searches will be faster, since it will not be necessary to redo the to_tsvector calls to verify index matches. (This is more important when using a GiST index than a GIN index; see .) The expression-index approach is simpler to set up, however, and it requires less disk space since the tsvector representation is not stored explicitly. To implement full text searching there must be a function to create a tsvector from a document and a tsquery from a user query. Also, we need to return results in a useful order, so we need a function that compares documents with respect to their relevance to the query. It's also important to be able to display the results nicely. PostgreSQL provides support for all of these functions. PostgreSQL provides the function to_tsvector for converting a document to the tsvector data type. to_tsvector to_tsvector( config regconfig, document text) returns tsvector to_tsvector parses a textual document into tokens, reduces the tokens to lexemes, and returns a tsvector which lists the lexemes together with their positions in the document. The document is processed according to the specified or default text search configuration. Here is a simple example: SELECT to_tsvector('english', 'a fat cat sat on a mat - it ate a fat rats'); to_tsvector ----------------------------------------------------- 'ate':9 'cat':3 'fat':2,11 'mat':7 'rat':12 'sat':4 In the example above we see that the resulting tsvector does not contain the words a, on, or it, the word rats became rat, and the punctuation sign - was ignored. The to_tsvector function internally calls a parser which breaks the document text into tokens and assigns a type to each token. For each token, a list of dictionaries () is consulted, where the list can vary depending on the token type. The first dictionary that recognizes the token emits one or more normalized lexemes to represent the token. For example, rats became rat because one of the dictionaries recognized that the word rats is a plural form of rat. Some words are recognized as stop words (), which causes them to be ignored since they occur too frequently to be useful in searching. In our example these are a, on, and it. If no dictionary in the list recognizes the token then it is also ignored. In this example that happened to the punctuation sign - because there are in fact no dictionaries assigned for its token type (Space symbols), meaning space tokens will never be indexed. The choices of parser, dictionaries and which types of tokens to index are determined by the selected text search configuration (). It is possible to have many different configurations in the same database, and predefined configurations are available for various languages. In our example we used the default configuration english for the English language. The function setweight can be used to label the entries of a tsvector with a given weight, where a weight is one of the letters A, B, C, or D. This is typically used to mark entries coming from different parts of a document, such as title versus body. Later, this information can be used for ranking of search results. Because to_tsvector(NULL) will return NULL, it is recommended to use coalesce whenever a field might be null. Here is the recommended method for creating a tsvector from a structured document: UPDATE tt SET ti = setweight(to_tsvector(coalesce(title,'')), 'A') || setweight(to_tsvector(coalesce(keyword,'')), 'B') || setweight(to_tsvector(coalesce(abstract,'')), 'C') || setweight(to_tsvector(coalesce(body,'')), 'D'); Here we have used setweight to label the source of each lexeme in the finished tsvector, and then merged the labeled tsvector values using the tsvector concatenation operator ||. ( gives details about these operations.) PostgreSQL provides the functions to_tsquery, plainto_tsquery, phraseto_tsquery and websearch_to_tsquery for converting a query to the tsquery data type. to_tsquery offers access to more features than either plainto_tsquery or phraseto_tsquery, but it is less forgiving about its input. websearch_to_tsquery is a simplified version of to_tsquery with an alternative syntax, similar to the one used by web search engines. to_tsquery to_tsquery( config regconfig, querytext text) returns tsquery to_tsquery creates a tsquery value from querytext, which must consist of single tokens separated by the tsquery operators & (AND), | (OR), ! (NOT), and <-> (FOLLOWED BY), possibly grouped using parentheses. In other words, the input to to_tsquery must already follow the general rules for tsquery input, as described in . The difference is that while basic tsquery input takes the tokens at face value, to_tsquery normalizes each token into a lexeme using the specified or default configuration, and discards any tokens that are stop words according to the configuration. For example: SELECT to_tsquery('english', 'The & Fat & Rats'); to_tsquery --------------- 'fat' & 'rat' As in basic tsquery input, weight(s) can be attached to each lexeme to restrict it to match only tsvector lexemes of those weight(s). For example: SELECT to_tsquery('english', 'Fat | Rats:AB'); to_tsquery ------------------ 'fat' | 'rat':AB Also, * can be attached to a lexeme to specify prefix matching: SELECT to_tsquery('supern:*A & star:A*B'); to_tsquery -------------------------- 'supern':*A & 'star':*AB Such a lexeme will match any word in a tsvector that begins with the given string. to_tsquery can also accept single-quoted phrases. This is primarily useful when the configuration includes a thesaurus dictionary that may trigger on such phrases. In the example below, a thesaurus contains the rule supernovae stars : sn: SELECT to_tsquery('''supernovae stars'' & !crab'); to_tsquery --------------- 'sn' & !'crab' Without quotes, to_tsquery will generate a syntax error for tokens that are not separated by an AND, OR, or FOLLOWED BY operator. plainto_tsquery plainto_tsquery( config regconfig, querytext text) returns tsquery plainto_tsquery transforms the unformatted text querytext to a tsquery value. The text is parsed and normalized much as for to_tsvector, then the & (AND) tsquery operator is inserted between surviving words. Example: SELECT plainto_tsquery('english', 'The Fat Rats'); plainto_tsquery ----------------- 'fat' & 'rat' Note that plainto_tsquery will not recognize tsquery operators, weight labels, or prefix-match labels in its input: SELECT plainto_tsquery('english', 'The Fat & Rats:C'); plainto_tsquery --------------------- 'fat' & 'rat' & 'c' Here, all the input punctuation was discarded. phraseto_tsquery phraseto_tsquery( config regconfig, querytext text) returns tsquery phraseto_tsquery behaves much like plainto_tsquery, except that it inserts the <-> (FOLLOWED BY) operator between surviving words instead of the & (AND) operator. Also, stop words are not simply discarded, but are accounted for by inserting <N> operators rather than <-> operators. This function is useful when searching for exact lexeme sequences, since the FOLLOWED BY operators check lexeme order not just the presence of all the lexemes. Example: SELECT phraseto_tsquery('english', 'The Fat Rats'); phraseto_tsquery ------------------ 'fat' <-> 'rat' Like plainto_tsquery, the phraseto_tsquery function will not recognize tsquery operators, weight labels, or prefix-match labels in its input: SELECT phraseto_tsquery('english', 'The Fat & Rats:C'); phraseto_tsquery ----------------------------- 'fat' <-> 'rat' <-> 'c' websearch_to_tsquery( config regconfig, querytext text) returns tsquery websearch_to_tsquery creates a tsquery value from querytext using an alternative syntax in which simple unformatted text is a valid query. Unlike plainto_tsquery and phraseto_tsquery, it also recognizes certain operators. Moreover, this function will never raise syntax errors, which makes it possible to use raw user-supplied input for search. The following syntax is supported: unquoted text: text not inside quote marks will be converted to terms separated by & operators, as if processed by plainto_tsquery. "quoted text": text inside quote marks will be converted to terms separated by <-> operators, as if processed by phraseto_tsquery. OR: the word or will be converted to the | operator. -: a dash will be converted to the ! operator. Other punctuation is ignored. So like plainto_tsquery and phraseto_tsquery, the websearch_to_tsquery function will not recognize tsquery operators, weight labels, or prefix-match labels in its input. Examples: SELECT websearch_to_tsquery('english', 'The fat rats'); websearch_to_tsquery ---------------------- 'fat' & 'rat' (1 row) SELECT websearch_to_tsquery('english', '"supernovae stars" -crab'); websearch_to_tsquery ---------------------------------- 'supernova' <-> 'star' & !'crab' (1 row) SELECT websearch_to_tsquery('english', '"sad cat" or "fat rat"'); websearch_to_tsquery ----------------------------------- 'sad' <-> 'cat' | 'fat' <-> 'rat' (1 row) SELECT websearch_to_tsquery('english', 'signal -"segmentation fault"'); websearch_to_tsquery --------------------------------------- 'signal' & !( 'segment' <-> 'fault' ) (1 row) SELECT websearch_to_tsquery('english', '""" )( dummy \\ query <->'); websearch_to_tsquery ---------------------- 'dummi' & 'queri' (1 row) Ranking attempts to measure how relevant documents are to a particular query, so that when there are many matches the most relevant ones can be shown first. PostgreSQL provides two predefined ranking functions, which take into account lexical, proximity, and structural information; that is, they consider how often the query terms appear in the document, how close together the terms are in the document, and how important is the part of the document where they occur. However, the concept of relevancy is vague and very application-specific. Different applications might require additional information for ranking, e.g., document modification time. The built-in ranking functions are only examples. You can write your own ranking functions and/or combine their results with additional factors to fit your specific needs. The two ranking functions currently available are: ts_rank ts_rank( weights float4[], vector tsvector, query tsquery , normalization integer ) returns float4 Ranks vectors based on the frequency of their matching lexemes. ts_rank_cd ts_rank_cd( weights float4[], vector tsvector, query tsquery , normalization integer ) returns float4 This function computes the cover density ranking for the given document vector and query, as described in Clarke, Cormack, and Tudhope's "Relevance Ranking for One to Three Term Queries" in the journal "Information Processing and Management", 1999. Cover density is similar to ts_rank ranking except that the proximity of matching lexemes to each other is taken into consideration. This function requires lexeme positional information to perform its calculation. Therefore, it ignores any stripped lexemes in the tsvector. If there are no unstripped lexemes in the input, the result will be zero. (See for more information about the strip function and positional information in tsvectors.) For both these functions, the optional weights argument offers the ability to weigh word instances more or less heavily depending on how they are labeled. The weight arrays specify how heavily to weigh each category of word, in the order: {D-weight, C-weight, B-weight, A-weight} If no weights are provided, then these defaults are used: {0.1, 0.2, 0.4, 1.0} Typically weights are used to mark words from special areas of the document, like the title or an initial abstract, so they can be treated with more or less importance than words in the document body. Since a longer document has a greater chance of containing a query term it is reasonable to take into account document size, e.g., a hundred-word document with five instances of a search word is probably more relevant than a thousand-word document with five instances. Both ranking functions take an integer normalization option that specifies whether and how a document's length should impact its rank. The integer option controls several behaviors, so it is a bit mask: you can specify one or more behaviors using | (for example, 2|4). 0 (the default) ignores the document length 1 divides the rank by 1 + the logarithm of the document length 2 divides the rank by the document length 4 divides the rank by the mean harmonic distance between extents (this is implemented only by ts_rank_cd) 8 divides the rank by the number of unique words in document 16 divides the rank by 1 + the logarithm of the number of unique words in document 32 divides the rank by itself + 1 If more than one flag bit is specified, the transformations are applied in the order listed. It is important to note that the ranking functions do not use any global information, so it is impossible to produce a fair normalization to 1% or 100% as sometimes desired. Normalization option 32 (rank/(rank+1)) can be applied to scale all ranks into the range zero to one, but of course this is just a cosmetic change; it will not affect the ordering of the search results. Here is an example that selects only the ten highest-ranked matches: SELECT title, ts_rank_cd(textsearch, query) AS rank FROM apod, to_tsquery('neutrino|(dark & matter)') query WHERE query @@ textsearch ORDER BY rank DESC LIMIT 10; title | rank -----------------------------------------------+---------- Neutrinos in the Sun | 3.1 The Sudbury Neutrino Detector | 2.4 A MACHO View of Galactic Dark Matter | 2.01317 Hot Gas and Dark Matter | 1.91171 The Virgo Cluster: Hot Plasma and Dark Matter | 1.90953 Rafting for Solar Neutrinos | 1.9 NGC 4650A: Strange Galaxy and Dark Matter | 1.85774 Hot Gas and Dark Matter | 1.6123 Ice Fishing for Cosmic Neutrinos | 1.6 Weak Lensing Distorts the Universe | 0.818218 This is the same example using normalized ranking: SELECT title, ts_rank_cd(textsearch, query, 32 /* rank/(rank+1) */ ) AS rank FROM apod, to_tsquery('neutrino|(dark & matter)') query WHERE query @@ textsearch ORDER BY rank DESC LIMIT 10; title | rank -----------------------------------------------+------------------- Neutrinos in the Sun | 0.756097569485493 The Sudbury Neutrino Detector | 0.705882361190954 A MACHO View of Galactic Dark Matter | 0.668123210574724 Hot Gas and Dark Matter | 0.65655958650282 The Virgo Cluster: Hot Plasma and Dark Matter | 0.656301290640973 Rafting for Solar Neutrinos | 0.655172410958162 NGC 4650A: Strange Galaxy and Dark Matter | 0.650072921219637 Hot Gas and Dark Matter | 0.617195790024749 Ice Fishing for Cosmic Neutrinos | 0.615384618911517 Weak Lensing Distorts the Universe | 0.450010798361481 Ranking can be expensive since it requires consulting the tsvector of each matching document, which can be I/O bound and therefore slow. Unfortunately, it is almost impossible to avoid since practical queries often result in large numbers of matches. To present search results it is ideal to show a part of each document and how it is related to the query. Usually, search engines show fragments of the document with marked search terms. PostgreSQL provides a function ts_headline that implements this functionality. ts_headline ts_headline( config regconfig, document text, query tsquery , options text ) returns text ts_headline accepts a document along with a query, and returns an excerpt from the document in which terms from the query are highlighted. Specifically, the function will use the query to select relevant text fragments, and then highlight all words that appear in the query, even if those word positions do not match the query's restrictions. The configuration to be used to parse the document can be specified by config; if config is omitted, the default_text_search_config configuration is used. If an options string is specified it must consist of a comma-separated list of one or more option=value pairs. The available options are: MaxWords, MinWords (integers): these numbers determine the longest and shortest headlines to output. The default values are 35 and 15. ShortWord (integer): words of this length or less will be dropped at the start and end of a headline, unless they are query terms. The default value of three eliminates common English articles. HighlightAll (boolean): if true the whole document will be used as the headline, ignoring the preceding three parameters. The default is false. MaxFragments (integer): maximum number of text fragments to display. The default value of zero selects a non-fragment-based headline generation method. A value greater than zero selects fragment-based headline generation (see below). StartSel, StopSel (strings): the strings with which to delimit query words appearing in the document, to distinguish them from other excerpted words. The default values are <b> and </b>, which can be suitable for HTML output. FragmentDelimiter (string): When more than one fragment is displayed, the fragments will be separated by this string. The default is ... . These option names are recognized case-insensitively. You must double-quote string values if they contain spaces or commas. In non-fragment-based headline generation, ts_headline locates matches for the given query and chooses a single one to display, preferring matches that have more query words within the allowed headline length. In fragment-based headline generation, ts_headline locates the query matches and splits each match into fragments of no more than MaxWords words each, preferring fragments with more query words, and when possible stretching fragments to include surrounding words. The fragment-based mode is thus more useful when the query matches span large sections of the document, or when it's desirable to display multiple matches. In either mode, if no query matches can be identified, then a single fragment of the first MinWords words in the document will be displayed. For example: SELECT ts_headline('english', '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.', to_tsquery('english', 'query & similarity')); ts_headline ------------------------------------------------------------ containing given <b>query</b> terms + and return them in order of their <b>similarity</b> to the+ <b>query</b>. SELECT ts_headline('english', 'Search terms may occur many times in a document, requiring ranking of the search matches to decide which occurrences to display in the result.', to_tsquery('english', 'search & term'), 'MaxFragments=10, MaxWords=7, MinWords=3, StartSel=<<, StopSel=>>'); ts_headline ------------------------------------------------------------ <<Search>> <<terms>> may occur + many times ... ranking of the <<search>> matches to decide ts_headline uses the original document, not a tsvector summary, so it can be slow and should be used with care. This section describes additional functions and operators that are useful in connection with text search. showed how raw textual documents can be converted into tsvector values. PostgreSQL also provides functions and operators that can be used to manipulate documents that are already in tsvector form. tsvector concatenation tsvector || tsvector The tsvector concatenation operator returns a vector which combines the lexemes and positional information of the two vectors given as arguments. Positions and weight labels are retained during the concatenation. Positions appearing in the right-hand vector are offset by the largest position mentioned in the left-hand vector, so that the result is nearly equivalent to the result of performing to_tsvector on the concatenation of the two original document strings. (The equivalence is not exact, because any stop-words removed from the end of the left-hand argument will not affect the result, whereas they would have affected the positions of the lexemes in the right-hand argument if textual concatenation were used.) One advantage of using concatenation in the vector form, rather than concatenating text before applying to_tsvector, is that you can use different configurations to parse different sections of the document. Also, because the setweight function marks all lexemes of the given vector the same way, it is necessary to parse the text and do setweight before concatenating if you want to label different parts of the document with different weights. setweight setweight(vector tsvector, weight "char") returns tsvector setweight returns a copy of the input vector in which every position has been labeled with the given weight, either A, B, C, or D. (D is the default for new vectors and as such is not displayed on output.) These labels are retained when vectors are concatenated, allowing words from different parts of a document to be weighted differently by ranking functions. Note that weight labels apply to positions, not lexemes. If the input vector has been stripped of positions then setweight does nothing. length(tsvector) length(vector tsvector) returns integer Returns the number of lexemes stored in the vector. strip strip(vector tsvector) returns tsvector Returns a vector that lists the same lexemes as the given vector, but lacks any position or weight information. The result is usually much smaller than an unstripped vector, but it is also less useful. Relevance ranking does not work as well on stripped vectors as unstripped ones. Also, the <-> (FOLLOWED BY) tsquery operator will never match stripped input, since it cannot determine the distance between lexeme occurrences. A full list of tsvector-related functions is available in . showed how raw textual queries can be converted into tsquery values. PostgreSQL also provides functions and operators that can be used to manipulate queries that are already in tsquery form. tsquery && tsquery Returns the AND-combination of the two given queries. tsquery || tsquery Returns the OR-combination of the two given queries. !! tsquery Returns the negation (NOT) of the given query. tsquery <-> tsquery Returns a query that searches for a match to the first given query immediately followed by a match to the second given query, using the <-> (FOLLOWED BY) tsquery operator. For example: SELECT to_tsquery('fat') <-> to_tsquery('cat | rat'); ?column? ---------------------------- 'fat' <-> ( 'cat' | 'rat' ) tsquery_phrase tsquery_phrase(query1 tsquery, query2 tsquery [, distance integer ]) returns tsquery Returns a query that searches for a match to the first given query followed by a match to the second given query at a distance of exactly distance lexemes, using the <N> tsquery operator. For example: SELECT tsquery_phrase(to_tsquery('fat'), to_tsquery('cat'), 10); tsquery_phrase ------------------ 'fat' <10> 'cat' numnode numnode(query tsquery) returns integer Returns the number of nodes (lexemes plus operators) in a tsquery. This function is useful to determine if the query is meaningful (returns > 0), or contains only stop words (returns 0). Examples: SELECT numnode(plainto_tsquery('the any')); NOTICE: query contains only stopword(s) or doesn't contain lexeme(s), ignored numnode --------- 0 SELECT numnode('foo & bar'::tsquery); numnode --------- 3 querytree querytree(query tsquery) returns text Returns the portion of a tsquery that can be used for searching an index. This function is useful for detecting unindexable queries, for example those containing only stop words or only negated terms. For example: SELECT querytree(to_tsquery('defined')); querytree ----------- 'defin' SELECT querytree(to_tsquery('!defined')); querytree ----------- T ts_rewrite The ts_rewrite family of functions search a given tsquery for occurrences of a target subquery, and replace each occurrence with a substitute subquery. In essence this operation is a tsquery-specific version of substring replacement. A target and substitute combination can be thought of as a query rewrite rule. A collection of such rewrite rules can be a powerful search aid. For example, you can expand the search using synonyms (e.g., new york, big apple, nyc, gotham) or narrow the search to direct the user to some hot topic. There is some overlap in functionality between this feature and thesaurus dictionaries (). However, you can modify a set of rewrite rules on-the-fly without reindexing, whereas updating a thesaurus requires reindexing to be effective. ts_rewrite (query tsquery, target tsquery, substitute tsquery) returns tsquery This form of ts_rewrite simply applies a single rewrite rule: target is replaced by substitute wherever it appears in query. For example: SELECT ts_rewrite('a & b'::tsquery, 'a'::tsquery, 'c'::tsquery); ts_rewrite ------------ 'b' & 'c' ts_rewrite (query tsquery, select text) returns tsquery This form of ts_rewrite accepts a starting query and an SQL select command, which is given as a text string. The select must yield two columns of tsquery type. For each row of the select result, occurrences of the first column value (the target) are replaced by the second column value (the substitute) within the current query value. For example: CREATE TABLE aliases (t tsquery PRIMARY KEY, s tsquery); INSERT INTO aliases VALUES('a', 'c'); SELECT ts_rewrite('a & b'::tsquery, 'SELECT t,s FROM aliases'); ts_rewrite ------------ 'b' & 'c' Note that when multiple rewrite rules are applied in this way, the order of application can be important; so in practice you will want the source query to ORDER BY some ordering key. Let's consider a real-life astronomical example. We'll expand query supernovae using table-driven rewriting rules: CREATE TABLE aliases (t tsquery primary key, s tsquery); INSERT INTO aliases VALUES(to_tsquery('supernovae'), to_tsquery('supernovae|sn')); SELECT ts_rewrite(to_tsquery('supernovae & crab'), 'SELECT * FROM aliases'); ts_rewrite --------------------------------- 'crab' & ( 'supernova' | 'sn' ) We can change the rewriting rules just by updating the table: UPDATE aliases SET s = to_tsquery('supernovae|sn & !nebulae') WHERE t = to_tsquery('supernovae'); SELECT ts_rewrite(to_tsquery('supernovae & crab'), 'SELECT * FROM aliases'); ts_rewrite --------------------------------------------- 'crab' & ( 'supernova' | 'sn' & !'nebula' ) Rewriting can be slow when there are many rewriting rules, since it checks every rule for a possible match. To filter out obvious non-candidate rules we can use the containment operators for the tsquery type. In the example below, we select only those rules which might match the original query: SELECT ts_rewrite('a & b'::tsquery, 'SELECT t,s FROM aliases WHERE ''a & b''::tsquery @> t'); ts_rewrite ------------ 'b' & 'c' trigger for updating a derived tsvector column The method described in this section has been obsoleted by the use of stored generated columns, as described in . When using a separate column to store the tsvector representation of your documents, it is necessary to create a trigger to update the tsvector column when the document content columns change. Two built-in trigger functions are available for this, or you can write your own. tsvector_update_trigger(tsvector_column_name,&zwsp; config_name, text_column_name , ... ) tsvector_update_trigger_column(tsvector_column_name,&zwsp; config_column_name, text_column_name , ... ) These trigger functions automatically compute a tsvector column from one or more textual columns, under the control of parameters specified in the CREATE TRIGGER command. An example of their use is: CREATE TABLE messages ( title text, body text, tsv tsvector ); CREATE TRIGGER tsvectorupdate BEFORE INSERT OR UPDATE ON messages FOR EACH ROW EXECUTE FUNCTION tsvector_update_trigger(tsv, 'pg_catalog.english', title, body); INSERT INTO messages VALUES('title here', 'the body text is here'); SELECT * FROM messages; title | body | tsv ------------+-----------------------+---------------------------- title here | the body text is here | 'bodi':4 'text':5 'titl':1 SELECT title, body FROM messages WHERE tsv @@ to_tsquery('title & body'); title | body ------------+----------------------- title here | the body text is here Having created this trigger, any change in title or body will automatically be reflected into tsv, without the application having to worry about it. The first trigger argument must be the name of the tsvector column to be updated. The second argument specifies the text search configuration to be used to perform the conversion. For tsvector_update_trigger, the configuration name is simply given as the second trigger argument. It must be schema-qualified as shown above, so that the trigger behavior will not change with changes in search_path. For tsvector_update_trigger_column, the second trigger argument is the name of another table column, which must be of type regconfig. This allows a per-row selection of configuration to be made. The remaining argument(s) are the names of textual columns (of type text, varchar, or char). These will be included in the document in the order given. NULL values will be skipped (but the other columns will still be indexed). A limitation of these built-in triggers is that they treat all the input columns alike. To process columns differently — for example, to weight title differently from body — it is necessary to write a custom trigger. Here is an example using PL/pgSQL as the trigger language: CREATE FUNCTION messages_trigger() RETURNS trigger AS $$ begin new.tsv := setweight(to_tsvector('pg_catalog.english', coalesce(new.title,'')), 'A') || setweight(to_tsvector('pg_catalog.english', coalesce(new.body,'')), 'D'); return new; end $$ LANGUAGE plpgsql; CREATE TRIGGER tsvectorupdate BEFORE INSERT OR UPDATE ON messages FOR EACH ROW EXECUTE FUNCTION messages_trigger(); Keep in mind that it is important to specify the configuration name explicitly when creating tsvector values inside triggers, so that the column's contents will not be affected by changes to default_text_search_config. Failure to do this is likely to lead to problems such as search results changing after a dump and restore. ts_stat The function ts_stat is useful for checking your configuration and for finding stop-word candidates. ts_stat(sqlquery text, weights text, OUT word text, OUT ndoc integer, OUT nentry integer) returns setof record sqlquery is a text value containing an SQL query which must return a single tsvector column. ts_stat executes the query and returns statistics about each distinct lexeme (word) contained in the tsvector data. The columns returned are word text — the value of a lexeme ndoc integer — number of documents (tsvectors) the word occurred in nentry integer — total number of occurrences of the word If weights is supplied, only occurrences having one of those weights are counted. For example, to find the ten most frequent words in a document collection: SELECT * FROM ts_stat('SELECT vector FROM apod') ORDER BY nentry DESC, ndoc DESC, word LIMIT 10; The same, but counting only word occurrences with weight A or B: SELECT * FROM ts_stat('SELECT vector FROM apod', 'ab') ORDER BY nentry DESC, ndoc DESC, word LIMIT 10; Text search parsers are responsible for splitting raw document text into tokens and identifying each token's type, where the set of possible types is defined by the parser itself. Note that a parser does not modify the text at all — it simply identifies plausible word boundaries. Because of this limited scope, there is less need for application-specific custom parsers than there is for custom dictionaries. At present PostgreSQL provides just one built-in parser, which has been found to be useful for a wide range of applications. The built-in parser is named pg_catalog.default. It recognizes 23 token types, shown in . Alias Description Example asciiword Word, all ASCII letters elephant word Word, all letters mañana numword Word, letters and digits beta1 asciihword Hyphenated word, all ASCII up-to-date hword Hyphenated word, all letters lógico-matemática numhword Hyphenated word, letters and digits postgresql-beta1 hword_asciipart Hyphenated word part, all ASCII postgresql in the context postgresql-beta1 hword_part Hyphenated word part, all letters lógico or matemática in the context lógico-matemática hword_numpart Hyphenated word part, letters and digits beta1 in the context postgresql-beta1 email Email address foo@example.com protocol Protocol head http:// url URL example.com/stuff/index.html host Host example.com url_path URL path /stuff/index.html, in the context of a URL file File or path name /usr/local/foo.txt, if not within a URL sfloat Scientific notation -1.234e56 float Decimal notation -1.234 int Signed integer -1234 uint Unsigned integer 1234 version Version number 8.3.0 tag XML tag <a href="dictionaries.html"> entity XML entity &amp; blank Space symbols (any whitespace or punctuation not otherwise recognized) The parser's notion of a letter is determined by the database's locale setting, specifically lc_ctype. Words containing only the basic ASCII letters are reported as a separate token type, since it is sometimes useful to distinguish them. In most European languages, token types word and asciiword should be treated alike. email does not support all valid email characters as defined by RFC 5322. Specifically, the only non-alphanumeric characters supported for email user names are period, dash, and underscore. It is possible for the parser to produce overlapping tokens from the same piece of text. As an example, a hyphenated word will be reported both as the entire word and as each component: SELECT alias, description, token FROM ts_debug('foo-bar-beta1'); alias | description | token -----------------+------------------------------------------+--------------- numhword | Hyphenated word, letters and digits | foo-bar-beta1 hword_asciipart | Hyphenated word part, all ASCII | foo blank | Space symbols | - hword_asciipart | Hyphenated word part, all ASCII | bar blank | Space symbols | - hword_numpart | Hyphenated word part, letters and digits | beta1 This behavior is desirable since it allows searches to work for both the whole compound word and for components. Here is another instructive example: SELECT alias, description, token FROM ts_debug('http://example.com/stuff/index.html'); alias | description | token ----------+---------------+------------------------------ protocol | Protocol head | http:// url | URL | example.com/stuff/index.html host | Host | example.com url_path | URL path | /stuff/index.html Dictionaries are used to eliminate words that should not be considered in a search (stop words), and to normalize words so that different derived forms of the same word will match. A successfully normalized word is called a lexeme. Aside from improving search quality, normalization and removal of stop words reduce the size of the tsvector representation of a document, thereby improving performance. Normalization does not always have linguistic meaning and usually depends on application semantics. Some examples of normalization: Linguistic — Ispell dictionaries try to reduce input words to a normalized form; stemmer dictionaries remove word endings URL locations can be canonicalized to make equivalent URLs match: http://www.pgsql.ru/db/mw/index.html http://www.pgsql.ru/db/mw/ http://www.pgsql.ru/db/../db/mw/index.html Color names can be replaced by their hexadecimal values, e.g., red, green, blue, magenta -> FF0000, 00FF00, 0000FF, FF00FF If indexing numbers, we can remove some fractional digits to reduce the range of possible numbers, so for example 3.14159265359, 3.1415926, 3.14 will be the same after normalization if only two digits are kept after the decimal point. A dictionary is a program that accepts a token as input and returns: an array of lexemes if the input token is known to the dictionary (notice that one token can produce more than one lexeme) a single lexeme with the TSL_FILTER flag set, to replace the original token with a new token to be passed to subsequent dictionaries (a dictionary that does this is called a filtering dictionary) an empty array if the dictionary knows the token, but it is a stop word NULL if the dictionary does not recognize the input token PostgreSQL provides predefined dictionaries for many languages. There are also several predefined templates that can be used to create new dictionaries with custom parameters. Each predefined dictionary template is described below. If no existing template is suitable, it is possible to create new ones; see the contrib/ area of the PostgreSQL distribution for examples. A text search configuration binds a parser together with a set of dictionaries to process the parser's output tokens. For each token type that the parser can return, a separate list of dictionaries is specified by the configuration. When a token of that type is found by the parser, each dictionary in the list is consulted in turn, until some dictionary recognizes it as a known word. If it is identified as a stop word, or if no dictionary recognizes the token, it will be discarded and not indexed or searched for. Normally, the first dictionary that returns a non-NULL output determines the result, and any remaining dictionaries are not consulted; but a filtering dictionary can replace the given word with a modified word, which is then passed to subsequent dictionaries. The general rule for configuring a list of dictionaries is to place first the most narrow, most specific dictionary, then the more general dictionaries, finishing with a very general dictionary, like a Snowball stemmer or simple, which recognizes everything. For example, for an astronomy-specific search (astro_en configuration) one could bind token type asciiword (ASCII word) to a synonym dictionary of astronomical terms, a general English dictionary and a Snowball English stemmer: ALTER TEXT SEARCH CONFIGURATION astro_en ADD MAPPING FOR asciiword WITH astrosyn, english_ispell, english_stem; A filtering dictionary can be placed anywhere in the list, except at the end where it'd be useless. Filtering dictionaries are useful to partially normalize words to simplify the task of later dictionaries. For example, a filtering dictionary could be used to remove accents from accented letters, as is done by the module. Stop words are words that are very common, appear in almost every document, and have no discrimination value. Therefore, they can be ignored in the context of full text searching. For example, every English text contains words like a and the, so it is useless to store them in an index. However, stop words do affect the positions in tsvector, which in turn affect ranking: SELECT to_tsvector('english', 'in the list of stop words'); to_tsvector ---------------------------- 'list':3 'stop':5 'word':6 The missing positions 1,2,4 are because of stop words. Ranks calculated for documents with and without stop words are quite different: SELECT ts_rank_cd (to_tsvector('english', 'in the list of stop words'), to_tsquery('list & stop')); ts_rank_cd ------------ 0.05 SELECT ts_rank_cd (to_tsvector('english', 'list stop words'), to_tsquery('list & stop')); ts_rank_cd ------------ 0.1 It is up to the specific dictionary how it treats stop words. For example, ispell dictionaries first normalize words and then look at the list of stop words, while Snowball stemmers first check the list of stop words. The reason for the different behavior is an attempt to decrease noise. The simple dictionary template operates by converting the input token to lower case and checking it against a file of stop words. If it is found in the file then an empty array is returned, causing the token to be discarded. If not, the lower-cased form of the word is returned as the normalized lexeme. Alternatively, the dictionary can be configured to report non-stop-words as unrecognized, allowing them to be passed on to the next dictionary in the list. Here is an example of a dictionary definition using the simple template: CREATE TEXT SEARCH DICTIONARY public.simple_dict ( TEMPLATE = pg_catalog.simple, STOPWORDS = english ); Here, english is the base name of a file of stop words. The file's full name will be $SHAREDIR/tsearch_data/english.stop, where $SHAREDIR means the PostgreSQL installation's shared-data directory, often /usr/local/share/postgresql (use pg_config --sharedir to determine it if you're not sure). The file format is simply a list of words, one per line. Blank lines and trailing spaces are ignored, and upper case is folded to lower case, but no other processing is done on the file contents. Now we can test our dictionary: SELECT ts_lexize('public.simple_dict', 'YeS'); ts_lexize ----------- {yes} SELECT ts_lexize('public.simple_dict', 'The'); ts_lexize ----------- {} We can also choose to return NULL, instead of the lower-cased word, if it is not found in the stop words file. This behavior is selected by setting the dictionary's Accept parameter to false. Continuing the example: ALTER TEXT SEARCH DICTIONARY public.simple_dict ( Accept = false ); SELECT ts_lexize('public.simple_dict', 'YeS'); ts_lexize ----------- SELECT ts_lexize('public.simple_dict', 'The'); ts_lexize ----------- {} With the default setting of Accept = true, it is only useful to place a simple dictionary at the end of a list of dictionaries, since it will never pass on any token to a following dictionary. Conversely, Accept = false is only useful when there is at least one following dictionary. Most types of dictionaries rely on configuration files, such as files of stop words. These files must be stored in UTF-8 encoding. They will be translated to the actual database encoding, if that is different, when they are read into the server. Normally, a database session will read a dictionary configuration file only once, when it is first used within the session. If you modify a configuration file and want to force existing sessions to pick up the new contents, issue an ALTER TEXT SEARCH DICTIONARY command on the dictionary. This can be a dummy update that doesn't actually change any parameter values. This dictionary template is used to create dictionaries that replace a word with a synonym. Phrases are not supported (use the thesaurus template () for that). A synonym dictionary can be used to overcome linguistic problems, for example, to prevent an English stemmer dictionary from reducing the word Paris to pari. It is enough to have a Paris paris line in the synonym dictionary and put it before the english_stem dictionary. For example: SELECT * FROM ts_debug('english', 'Paris'); alias | description | token | dictionaries | dictionary | lexemes -----------+-----------------+-------+----------------+--------------+--------- asciiword | Word, all ASCII | Paris | {english_stem} | english_stem | {pari} CREATE TEXT SEARCH DICTIONARY my_synonym ( TEMPLATE = synonym, SYNONYMS = my_synonyms ); ALTER TEXT SEARCH CONFIGURATION english ALTER MAPPING FOR asciiword WITH my_synonym, english_stem; SELECT * FROM ts_debug('english', 'Paris'); alias | description | token | dictionaries | dictionary | lexemes -----------+-----------------+-------+---------------------------+------------+--------- asciiword | Word, all ASCII | Paris | {my_synonym,english_stem} | my_synonym | {paris} The only parameter required by the synonym template is SYNONYMS, which is the base name of its configuration file — my_synonyms in the above example. The file's full name will be $SHAREDIR/tsearch_data/my_synonyms.syn (where $SHAREDIR means the PostgreSQL installation's shared-data directory). The file format is just one line per word to be substituted, with the word followed by its synonym, separated by white space. Blank lines and trailing spaces are ignored. The synonym template also has an optional parameter CaseSensitive, which defaults to false. When CaseSensitive is false, words in the synonym file are folded to lower case, as are input tokens. When it is true, words and tokens are not folded to lower case, but are compared as-is. An asterisk (*) can be placed at the end of a synonym in the configuration file. This indicates that the synonym is a prefix. The asterisk is ignored when the entry is used in to_tsvector(), but when it is used in to_tsquery(), the result will be a query item with the prefix match marker (see ). For example, suppose we have these entries in $SHAREDIR/tsearch_data/synonym_sample.syn: postgres pgsql postgresql pgsql postgre pgsql gogle googl indices index* Then we will get these results: mydb=# CREATE TEXT SEARCH DICTIONARY syn (template=synonym, synonyms='synonym_sample'); mydb=# SELECT ts_lexize('syn', 'indices'); ts_lexize ----------- {index} (1 row) mydb=# CREATE TEXT SEARCH CONFIGURATION tst (copy=simple); mydb=# ALTER TEXT SEARCH CONFIGURATION tst ALTER MAPPING FOR asciiword WITH syn; mydb=# SELECT to_tsvector('tst', 'indices'); to_tsvector ------------- 'index':1 (1 row) mydb=# SELECT to_tsquery('tst', 'indices'); to_tsquery ------------ 'index':* (1 row) mydb=# SELECT 'indexes are very useful'::tsvector; tsvector --------------------------------- 'are' 'indexes' 'useful' 'very' (1 row) mydb=# SELECT 'indexes are very useful'::tsvector @@ to_tsquery('tst', 'indices'); ?column? ---------- t (1 row) A thesaurus dictionary (sometimes abbreviated as TZ) is a collection of words that includes information about the relationships of words and phrases, i.e., broader terms (BT), narrower terms (NT), preferred terms, non-preferred terms, related terms, etc. Basically a thesaurus dictionary replaces all non-preferred terms by one preferred term and, optionally, preserves the original terms for indexing as well. PostgreSQL's current implementation of the thesaurus dictionary is an extension of the synonym dictionary with added phrase support. A thesaurus dictionary requires a configuration file of the following format: # this is a comment sample word(s) : indexed word(s) more sample word(s) : more indexed word(s) ... where the colon (:) symbol acts as a delimiter between a phrase and its replacement. A thesaurus dictionary uses a subdictionary (which is specified in the dictionary's configuration) to normalize the input text before checking for phrase matches. It is only possible to select one subdictionary. An error is reported if the subdictionary fails to recognize a word. In that case, you should remove the use of the word or teach the subdictionary about it. You can place an asterisk (*) at the beginning of an indexed word to skip applying the subdictionary to it, but all sample words must be known to the subdictionary. The thesaurus dictionary chooses the longest match if there are multiple phrases matching the input, and ties are broken by using the last definition. Specific stop words recognized by the subdictionary cannot be specified; instead use ? to mark the location where any stop word can appear. For example, assuming that a and the are stop words according to the subdictionary: ? one ? two : swsw matches a one the two and the one a two; both would be replaced by swsw. Since a thesaurus dictionary has the capability to recognize phrases it must remember its state and interact with the parser. A thesaurus dictionary uses these assignments to check if it should handle the next word or stop accumulation. The thesaurus dictionary must be configured carefully. For example, if the thesaurus dictionary is assigned to handle only the asciiword token, then a thesaurus dictionary definition like one 7 will not work since token type uint is not assigned to the thesaurus dictionary. Thesauruses are used during indexing so any change in the thesaurus dictionary's parameters requires reindexing. For most other dictionary types, small changes such as adding or removing stopwords does not force reindexing. To define a new thesaurus dictionary, use the thesaurus template. For example: CREATE TEXT SEARCH DICTIONARY thesaurus_simple ( TEMPLATE = thesaurus, DictFile = mythesaurus, Dictionary = pg_catalog.english_stem ); Here: thesaurus_simple is the new dictionary's name mythesaurus is the base name of the thesaurus configuration file. (Its full name will be $SHAREDIR/tsearch_data/mythesaurus.ths, where $SHAREDIR means the installation shared-data directory.) pg_catalog.english_stem is the subdictionary (here, a Snowball English stemmer) to use for thesaurus normalization. Notice that the subdictionary will have its own configuration (for example, stop words), which is not shown here. Now it is possible to bind the thesaurus dictionary thesaurus_simple to the desired token types in a configuration, for example: ALTER TEXT SEARCH CONFIGURATION russian ALTER MAPPING FOR asciiword, asciihword, hword_asciipart WITH thesaurus_simple; Consider a simple astronomical thesaurus thesaurus_astro, which contains some astronomical word combinations: supernovae stars : sn crab nebulae : crab Below we create a dictionary and bind some token types to an astronomical thesaurus and English stemmer: CREATE TEXT SEARCH DICTIONARY thesaurus_astro ( TEMPLATE = thesaurus, DictFile = thesaurus_astro, Dictionary = english_stem ); ALTER TEXT SEARCH CONFIGURATION russian ALTER MAPPING FOR asciiword, asciihword, hword_asciipart WITH thesaurus_astro, english_stem; Now we can see how it works. ts_lexize is not very useful for testing a thesaurus, because it treats its input as a single token. Instead we can use plainto_tsquery and to_tsvector which will break their input strings into multiple tokens: SELECT plainto_tsquery('supernova star'); plainto_tsquery ----------------- 'sn' SELECT to_tsvector('supernova star'); to_tsvector ------------- 'sn':1 In principle, one can use to_tsquery if you quote the argument: SELECT to_tsquery('''supernova star'''); to_tsquery ------------ 'sn' Notice that supernova star matches supernovae stars in thesaurus_astro because we specified the english_stem stemmer in the thesaurus definition. The stemmer removed the e and s. To index the original phrase as well as the substitute, just include it in the right-hand part of the definition: supernovae stars : sn supernovae stars SELECT plainto_tsquery('supernova star'); plainto_tsquery ----------------------------- 'sn' & 'supernova' & 'star' The Ispell dictionary template supports morphological dictionaries, which can normalize many different linguistic forms of a word into the same lexeme. For example, an English Ispell dictionary can match all declensions and conjugations of the search term bank, e.g., banking, banked, banks, banks', and bank's. The standard PostgreSQL distribution does not include any Ispell configuration files. Dictionaries for a large number of languages are available from Ispell. Also, some more modern dictionary file formats are supported — MySpell (OO < 2.0.1) and Hunspell (OO >= 2.0.2). A large list of dictionaries is available on the OpenOffice Wiki. To create an Ispell dictionary perform these steps: download dictionary configuration files. OpenOffice extension files have the .oxt extension. It is necessary to extract .aff and .dic files, change extensions to .affix and .dict. For some dictionary files it is also needed to convert characters to the UTF-8 encoding with commands (for example, for a Norwegian language dictionary): iconv -f ISO_8859-1 -t UTF-8 -o nn_no.affix nn_NO.aff iconv -f ISO_8859-1 -t UTF-8 -o nn_no.dict nn_NO.dic copy files to the $SHAREDIR/tsearch_data directory load files into PostgreSQL with the following command: CREATE TEXT SEARCH DICTIONARY english_hunspell ( TEMPLATE = ispell, DictFile = en_us, AffFile = en_us, Stopwords = english); Here, DictFile, AffFile, and StopWords specify the base names of the dictionary, affixes, and stop-words files. The stop-words file has the same format explained above for the simple dictionary type. The format of the other files is not specified here but is available from the above-mentioned web sites. Ispell dictionaries usually recognize a limited set of words, so they should be followed by another broader dictionary; for example, a Snowball dictionary, which recognizes everything. The .affix file of Ispell has the following structure: prefixes flag *A: . > RE # As in enter > reenter suffixes flag T: E > ST # As in late > latest [^AEIOU]Y > -Y,IEST # As in dirty > dirtiest [AEIOU]Y > EST # As in gray > grayest [^EY] > EST # As in small > smallest And the .dict file has the following structure: lapse/ADGRS lard/DGRS large/PRTY lark/MRS Format of the .dict file is: basic_form/affix_class_name In the .affix file every affix flag is described in the following format: condition > [-stripping_letters,] adding_affix Here, condition has a format similar to the format of regular expressions. It can use groupings [...] and [^...]. For example, [AEIOU]Y means that the last letter of the word is "y" and the penultimate letter is "a", "e", "i", "o" or "u". [^EY] means that the last letter is neither "e" nor "y". Ispell dictionaries support splitting compound words; a useful feature. Notice that the affix file should specify a special flag using the compoundwords controlled statement that marks dictionary words that can participate in compound formation: compoundwords controlled z Here are some examples for the Norwegian language: SELECT ts_lexize('norwegian_ispell', 'overbuljongterningpakkmesterassistent'); {over,buljong,terning,pakk,mester,assistent} SELECT ts_lexize('norwegian_ispell', 'sjokoladefabrikk'); {sjokoladefabrikk,sjokolade,fabrikk} MySpell format is a subset of Hunspell. The .affix file of Hunspell has the following structure: PFX A Y 1 PFX A 0 re . SFX T N 4 SFX T 0 st e SFX T y iest [^aeiou]y SFX T 0 est [aeiou]y SFX T 0 est [^ey] The first line of an affix class is the header. Fields of an affix rules are listed after the header: parameter name (PFX or SFX) flag (name of the affix class) stripping characters from beginning (at prefix) or end (at suffix) of the word adding affix condition that has a format similar to the format of regular expressions. The .dict file looks like the .dict file of Ispell: larder/M lardy/RT large/RSPMYT largehearted MySpell does not support compound words. Hunspell has sophisticated support for compound words. At present, PostgreSQL implements only the basic compound word operations of Hunspell. The Snowball dictionary template is based on a project by Martin Porter, inventor of the popular Porter's stemming algorithm for the English language. Snowball now provides stemming algorithms for many languages (see the Snowball site for more information). Each algorithm understands how to reduce common variant forms of words to a base, or stem, spelling within its language. A Snowball dictionary requires a language parameter to identify which stemmer to use, and optionally can specify a stopword file name that gives a list of words to eliminate. (PostgreSQL's standard stopword lists are also provided by the Snowball project.) For example, there is a built-in definition equivalent to CREATE TEXT SEARCH DICTIONARY english_stem ( TEMPLATE = snowball, Language = english, StopWords = english ); The stopword file format is the same as already explained. A Snowball dictionary recognizes everything, whether or not it is able to simplify the word, so it should be placed at the end of the dictionary list. It is useless to have it before any other dictionary because a token will never pass through it to the next dictionary. A text search configuration specifies all options necessary to transform a document into a tsvector: the parser to use to break text into tokens, and the dictionaries to use to transform each token into a lexeme. Every call of to_tsvector or to_tsquery needs a text search configuration to perform its processing. The configuration parameter specifies the name of the default configuration, which is the one used by text search functions if an explicit configuration parameter is omitted. It can be set in postgresql.conf, or set for an individual session using the SET command. Several predefined text search configurations are available, and you can create custom configurations easily. To facilitate management of text search objects, a set of SQL commands is available, and there are several psql commands that display information about text search objects (). As an example we will create a configuration pg, starting by duplicating the built-in english configuration: CREATE TEXT SEARCH CONFIGURATION public.pg ( COPY = pg_catalog.english ); We will use a PostgreSQL-specific synonym list and store it in $SHAREDIR/tsearch_data/pg_dict.syn. The file contents look like: postgres pg pgsql pg postgresql pg We define the synonym dictionary like this: CREATE TEXT SEARCH DICTIONARY pg_dict ( TEMPLATE = synonym, SYNONYMS = pg_dict ); Next we register the Ispell dictionary english_ispell, which has its own configuration files: CREATE TEXT SEARCH DICTIONARY english_ispell ( TEMPLATE = ispell, DictFile = english, AffFile = english, StopWords = english ); Now we can set up the mappings for words in configuration pg: ALTER TEXT SEARCH CONFIGURATION pg ALTER MAPPING FOR asciiword, asciihword, hword_asciipart, word, hword, hword_part WITH pg_dict, english_ispell, english_stem; We choose not to index or search some token types that the built-in configuration does handle: ALTER TEXT SEARCH CONFIGURATION pg DROP MAPPING FOR email, url, url_path, sfloat, float; Now we can test our configuration: SELECT * FROM ts_debug('public.pg', ' PostgreSQL, the highly scalable, SQL compliant, open source object-relational database management system, is now undergoing beta testing of the next version of our software. '); The next step is to set the session to use the new configuration, which was created in the public schema: => \dF List of text search configurations Schema | Name | Description ---------+------+------------- public | pg | SET default_text_search_config = 'public.pg'; SET SHOW default_text_search_config; default_text_search_config ---------------------------- public.pg The behavior of a custom text search configuration can easily become confusing. The functions described in this section are useful for testing text search objects. You can test a complete configuration, or test parsers and dictionaries separately. The function ts_debug allows easy testing of a text search configuration. ts_debug ts_debug( config regconfig, document text, OUT alias text, OUT description text, OUT token text, OUT dictionaries regdictionary[], OUT dictionary regdictionary, OUT lexemes text[]) returns setof record ts_debug displays information about every token of document as produced by the parser and processed by the configured dictionaries. It uses the configuration specified by config, or default_text_search_config if that argument is omitted. ts_debug returns one row for each token identified in the text by the parser. The columns returned are alias text — short name of the token type description text — description of the token type token text — text of the token dictionaries regdictionary[] — the dictionaries selected by the configuration for this token type dictionary regdictionary — the dictionary that recognized the token, or NULL if none did lexemes text[] — the lexeme(s) produced by the dictionary that recognized the token, or NULL if none did; an empty array ({}) means it was recognized as a stop word Here is a simple example: SELECT * FROM ts_debug('english', 'a fat cat sat on a mat - it ate a fat rats'); alias | description | token | dictionaries | dictionary | lexemes -----------+-----------------+-------+----------------+--------------+--------- asciiword | Word, all ASCII | a | {english_stem} | english_stem | {} blank | Space symbols | | {} | | asciiword | Word, all ASCII | fat | {english_stem} | english_stem | {fat} blank | Space symbols | | {} | | asciiword | Word, all ASCII | cat | {english_stem} | english_stem | {cat} blank | Space symbols | | {} | | asciiword | Word, all ASCII | sat | {english_stem} | english_stem | {sat} blank | Space symbols | | {} | | asciiword | Word, all ASCII | on | {english_stem} | english_stem | {} blank | Space symbols | | {} | | asciiword | Word, all ASCII | a | {english_stem} | english_stem | {} blank | Space symbols | | {} | | asciiword | Word, all ASCII | mat | {english_stem} | english_stem | {mat} blank | Space symbols | | {} | | blank | Space symbols | - | {} | | asciiword | Word, all ASCII | it | {english_stem} | english_stem | {} blank | Space symbols | | {} | | asciiword | Word, all ASCII | ate | {english_stem} | english_stem | {ate} blank | Space symbols | | {} | | asciiword | Word, all ASCII | a | {english_stem} | english_stem | {} blank | Space symbols | | {} | | asciiword | Word, all ASCII | fat | {english_stem} | english_stem | {fat} blank | Space symbols | | {} | | asciiword | Word, all ASCII | rats | {english_stem} | english_stem | {rat} For a more extensive demonstration, we first create a public.english configuration and Ispell dictionary for the English language: CREATE TEXT SEARCH CONFIGURATION public.english ( COPY = pg_catalog.english ); CREATE TEXT SEARCH DICTIONARY english_ispell ( TEMPLATE = ispell, DictFile = english, AffFile = english, StopWords = english ); ALTER TEXT SEARCH CONFIGURATION public.english ALTER MAPPING FOR asciiword WITH english_ispell, english_stem; SELECT * FROM ts_debug('public.english', 'The Brightest supernovaes'); alias | description | token | dictionaries | dictionary | lexemes -----------+-----------------+-------------+-------------------------------+----------------+------------- asciiword | Word, all ASCII | The | {english_ispell,english_stem} | english_ispell | {} blank | Space symbols | | {} | | asciiword | Word, all ASCII | Brightest | {english_ispell,english_stem} | english_ispell | {bright} blank | Space symbols | | {} | | asciiword | Word, all ASCII | supernovaes | {english_ispell,english_stem} | english_stem | {supernova} In this example, the word Brightest was recognized by the parser as an ASCII word (alias asciiword). For this token type the dictionary list is english_ispell and english_stem. The word was recognized by english_ispell, which reduced it to the noun bright. The word supernovaes is unknown to the english_ispell dictionary so it was passed to the next dictionary, and, fortunately, was recognized (in fact, english_stem is a Snowball dictionary which recognizes everything; that is why it was placed at the end of the dictionary list). The word The was recognized by the english_ispell dictionary as a stop word () and will not be indexed. The spaces are discarded too, since the configuration provides no dictionaries at all for them. You can reduce the width of the output by explicitly specifying which columns you want to see: SELECT alias, token, dictionary, lexemes FROM ts_debug('public.english', 'The Brightest supernovaes'); alias | token | dictionary | lexemes -----------+-------------+----------------+------------- asciiword | The | english_ispell | {} blank | | | asciiword | Brightest | english_ispell | {bright} blank | | | asciiword | supernovaes | english_stem | {supernova} The following functions allow direct testing of a text search parser. ts_parse ts_parse(parser_name text, document text, OUT tokid integer, OUT token text) returns setof record ts_parse(parser_oid oid, document text, OUT tokid integer, OUT token text) returns setof record ts_parse parses the given document and returns a series of records, one for each token produced by parsing. Each record includes a tokid showing the assigned token type and a token which is the text of the token. For example: SELECT * FROM ts_parse('default', '123 - a number'); tokid | token -------+-------- 22 | 123 12 | 12 | - 1 | a 12 | 1 | number ts_token_type ts_token_type(parser_name text, OUT tokid integer, OUT alias text, OUT description text) returns setof record ts_token_type(parser_oid oid, OUT tokid integer, OUT alias text, OUT description text) returns setof record ts_token_type returns a table which describes each type of token the specified parser can recognize. For each token type, the table gives the integer tokid that the parser uses to label a token of that type, the alias that names the token type in configuration commands, and a short description. For example: SELECT * FROM ts_token_type('default'); tokid | alias | description -------+-----------------+------------------------------------------ 1 | asciiword | Word, all ASCII 2 | word | Word, all letters 3 | numword | Word, letters and digits 4 | email | Email address 5 | url | URL 6 | host | Host 7 | sfloat | Scientific notation 8 | version | Version number 9 | hword_numpart | Hyphenated word part, letters and digits 10 | hword_part | Hyphenated word part, all letters 11 | hword_asciipart | Hyphenated word part, all ASCII 12 | blank | Space symbols 13 | tag | XML tag 14 | protocol | Protocol head 15 | numhword | Hyphenated word, letters and digits 16 | asciihword | Hyphenated word, all ASCII 17 | hword | Hyphenated word, all letters 18 | url_path | URL path 19 | file | File or path name 20 | float | Decimal notation 21 | int | Signed integer 22 | uint | Unsigned integer 23 | entity | XML entity The ts_lexize function facilitates dictionary testing. ts_lexize ts_lexize(dict regdictionary, token text) returns text[] ts_lexize returns an array of lexemes if the input token is known to the dictionary, or an empty array if the token is known to the dictionary but it is a stop word, or NULL if it is an unknown word. Examples: SELECT ts_lexize('english_stem', 'stars'); ts_lexize ----------- {star} SELECT ts_lexize('english_stem', 'a'); ts_lexize ----------- {} The ts_lexize function expects a single token, not text. Here is a case where this can be confusing: SELECT ts_lexize('thesaurus_astro', 'supernovae stars') is null; ?column? ---------- t The thesaurus dictionary thesaurus_astro does know the phrase supernovae stars, but ts_lexize fails since it does not parse the input text but treats it as a single token. Use plainto_tsquery or to_tsvector to test thesaurus dictionaries, for example: SELECT plainto_tsquery('supernovae stars'); plainto_tsquery ----------------- 'sn' text search indexes There are two kinds of indexes that can be used to speed up full text searches: GIN and GiST. Note that indexes are not mandatory for full text searching, but in cases where a column is searched on a regular basis, an index is usually desirable. To create such an index, do one of: index GIN text search CREATE INDEX name ON table USING GIN (column); Creates a GIN (Generalized Inverted Index)-based index. The column must be of tsvector type. index GiST text search CREATE INDEX name ON table USING GIST (column [ { DEFAULT | tsvector_ops } (siglen = number) ] ); Creates a GiST (Generalized Search Tree)-based index. The column can be of tsvector or tsquery type. Optional integer parameter siglen determines signature length in bytes (see below for details). GIN indexes are the preferred text search index type. As inverted indexes, they contain an index entry for each word (lexeme), with a compressed list of matching locations. Multi-word searches can find the first match, then use the index to remove rows that are lacking additional words. GIN indexes store only the words (lexemes) of tsvector values, and not their weight labels. Thus a table row recheck is needed when using a query that involves weights. A GiST index is lossy, meaning that the index might produce false matches, and it is necessary to check the actual table row to eliminate such false matches. (PostgreSQL does this automatically when needed.) GiST indexes are lossy because each document is represented in the index by a fixed-length signature. The signature length in bytes is determined by the value of the optional integer parameter siglen. The default signature length (when siglen is not specified) is 124 bytes, the maximum signature length is 2024 bytes. The signature is generated by hashing each word into a single bit in an n-bit string, with all these bits OR-ed together to produce an n-bit document signature. When two words hash to the same bit position there will be a false match. If all words in the query have matches (real or false) then the table row must be retrieved to see if the match is correct. Longer signatures lead to a more precise search (scanning a smaller fraction of the index and fewer heap pages), at the cost of a larger index. A GiST index can be covering, i.e., use the INCLUDE clause. Included columns can have data types without any GiST operator class. Included attributes will be stored uncompressed. Lossiness causes performance degradation due to unnecessary fetches of table records that turn out to be false matches. Since random access to table records is slow, this limits the usefulness of GiST indexes. The likelihood of false matches depends on several factors, in particular the number of unique words, so using dictionaries to reduce this number is recommended. Note that GIN index build time can often be improved by increasing , while GiST index build time is not sensitive to that parameter. Partitioning of big collections and the proper use of GIN and GiST indexes allows the implementation of very fast searches with online update. Partitioning can be done at the database level using table inheritance, or by distributing documents over servers and collecting external search results, e.g., via Foreign Data access. The latter is possible because ranking functions use only local information. Information about text search configuration objects can be obtained in psql using a set of commands: \dF{d,p,t}+ PATTERN An optional + produces more details. The optional parameter PATTERN can be the name of a text search object, optionally schema-qualified. If PATTERN is omitted then information about all visible objects will be displayed. PATTERN can be a regular expression and can provide separate patterns for the schema and object names. The following examples illustrate this: => \dF *fulltext* List of text search configurations Schema | Name | Description --------+--------------+------------- public | fulltext_cfg | => \dF *.fulltext* List of text search configurations Schema | Name | Description ----------+---------------------------- fulltext | fulltext_cfg | public | fulltext_cfg | The available commands are: \dF+ PATTERN List text search configurations (add + for more detail). => \dF russian List of text search configurations Schema | Name | Description ------------+---------+------------------------------------ pg_catalog | russian | configuration for russian language => \dF+ russian Text search configuration "pg_catalog.russian" Parser: "pg_catalog.default" Token | Dictionaries -----------------+-------------- asciihword | english_stem asciiword | english_stem email | simple file | simple float | simple host | simple hword | russian_stem hword_asciipart | english_stem hword_numpart | simple hword_part | russian_stem int | simple numhword | simple numword | simple sfloat | simple uint | simple url | simple url_path | simple version | simple word | russian_stem \dFd+ PATTERN List text search dictionaries (add + for more detail). => \dFd List of text search dictionaries Schema | Name | Description ------------+-----------------+----------------------------------------------------------- pg_catalog | arabic_stem | snowball stemmer for arabic language pg_catalog | armenian_stem | snowball stemmer for armenian language pg_catalog | basque_stem | snowball stemmer for basque language pg_catalog | catalan_stem | snowball stemmer for catalan language pg_catalog | danish_stem | snowball stemmer for danish language pg_catalog | dutch_stem | snowball stemmer for dutch language pg_catalog | english_stem | snowball stemmer for english language pg_catalog | finnish_stem | snowball stemmer for finnish language pg_catalog | french_stem | snowball stemmer for french language pg_catalog | german_stem | snowball stemmer for german language pg_catalog | greek_stem | snowball stemmer for greek language pg_catalog | hindi_stem | snowball stemmer for hindi language pg_catalog | hungarian_stem | snowball stemmer for hungarian language pg_catalog | indonesian_stem | snowball stemmer for indonesian language pg_catalog | irish_stem | snowball stemmer for irish language pg_catalog | italian_stem | snowball stemmer for italian language pg_catalog | lithuanian_stem | snowball stemmer for lithuanian language pg_catalog | nepali_stem | snowball stemmer for nepali language pg_catalog | norwegian_stem | snowball stemmer for norwegian language pg_catalog | portuguese_stem | snowball stemmer for portuguese language pg_catalog | romanian_stem | snowball stemmer for romanian language pg_catalog | russian_stem | snowball stemmer for russian language pg_catalog | serbian_stem | snowball stemmer for serbian language pg_catalog | simple | simple dictionary: just lower case and check for stopword pg_catalog | spanish_stem | snowball stemmer for spanish language pg_catalog | swedish_stem | snowball stemmer for swedish language pg_catalog | tamil_stem | snowball stemmer for tamil language pg_catalog | turkish_stem | snowball stemmer for turkish language pg_catalog | yiddish_stem | snowball stemmer for yiddish language \dFp+ PATTERN List text search parsers (add + for more detail). => \dFp List of text search parsers Schema | Name | Description ------------+---------+--------------------- pg_catalog | default | default word parser => \dFp+ Text search parser "pg_catalog.default" Method | Function | Description -----------------+----------------+------------- Start parse | prsd_start | Get next token | prsd_nexttoken | End parse | prsd_end | Get headline | prsd_headline | Get token types | prsd_lextype | Token types for parser "pg_catalog.default" Token name | Description -----------------+------------------------------------------ asciihword | Hyphenated word, all ASCII asciiword | Word, all ASCII blank | Space symbols email | Email address entity | XML entity file | File or path name float | Decimal notation host | Host hword | Hyphenated word, all letters hword_asciipart | Hyphenated word part, all ASCII hword_numpart | Hyphenated word part, letters and digits hword_part | Hyphenated word part, all letters int | Signed integer numhword | Hyphenated word, letters and digits numword | Word, letters and digits protocol | Protocol head sfloat | Scientific notation tag | XML tag uint | Unsigned integer url | URL url_path | URL path version | Version number word | Word, all letters (23 rows) \dFt+ PATTERN List text search templates (add + for more detail). => \dFt List of text search templates Schema | Name | Description ------------+-----------+----------------------------------------------------------- pg_catalog | ispell | ispell dictionary pg_catalog | simple | simple dictionary: just lower case and check for stopword pg_catalog | snowball | snowball stemmer pg_catalog | synonym | synonym dictionary: replace word by its synonym pg_catalog | thesaurus | thesaurus dictionary: phrase by phrase substitution The current limitations of PostgreSQL's text search features are: The length of each lexeme must be less than 2 kilobytes The length of a tsvector (lexemes + positions) must be less than 1 megabyte The number of lexemes must be less than 264 Position values in tsvector must be greater than 0 and no more than 16,383 The match distance in a <N> (FOLLOWED BY) tsquery operator cannot be more than 16,384 No more than 256 positions per lexeme The number of nodes (lexemes + operators) in a tsquery must be less than 32,768 For comparison, the PostgreSQL 8.1 documentation contained 10,441 unique words, a total of 335,420 words, and the most frequent word postgresql was mentioned 6,127 times in 655 documents. Another example — the PostgreSQL mailing list archives contained 910,989 unique words with 57,491,343 lexemes in 461,020 messages.