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Chapter 12. Full Text Search

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 default_text_search_conﬁg.

Amore complex example is to select the ten most recent documents that contain

create

and

table

in the

title

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.

12.2.2. Creating Indexes

We can create a GIN index (Section 12.9) to speedup 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

aconﬁguration name can be used in expression indexes (Section 11.7). This is because the index

contents must be unaffected by default_text_search_conﬁg. If they were affected, the index contents

might be inconsistent because different entries could contain

tsvector

sthat were created with dif-

ferent 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 ref-

erence 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 thatan index willbeusedonly

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’

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Chapter 12. Full Text Search

Indexes can even concatenate columns:

CREATE INDEX pgweb_idx ON pgweb USING gin(to_tsvector(’english’, title || ’ ’ || body));

Another approach is to createa separate

tsvector

column tohold the outputof

to_tsvector

.This

example is a concatenation of

title

and

body

,using

coalesce

to ensure that one ﬁeld will still be

indexed whenthe other is

NULL

ALTER TABLE pgweb ADD COLUMN textsearchable_index_col tsvector;

UPDATE pgweb SET textsearchable_index_col =

to_tsvector(’english’, coalesce(title,”) || ’ ’ || coalesce(body,”));

Then we create a GIN index to speed upthe 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;

When using aseparate column to store the

tsvector

representation, it is necessarytocreate a trigger

to keep the

tsvector

column current anytime

title

body

changes. Section 12.4.3 explains how

to do that.

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 redothe

to_tsvector

calls

to verify index matches. (This is more important when using a GiST index than a GIN index; see

Section 12.9.) The expression-index approach is simpler to set up, however, and it requires less disk

space since the

tsvector

representation is not stored explicitly.

12.3. Controlling Text Search

To implement full textsearchingthere must be a function tocreate a

tsvector

from a document and

tsquery

from a user query. Also, we needto 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.

12.3.1. Parsing Documents

PostgreSQL provides the function

to_tsvector

for converting a document to the

tsvector

data

type.

to_tsvector([

config regconfig

, ]

document text

) returns

tsvector

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Chapter 12. Full Text Search

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

Inthe example above weseethatthe resulting

tsvector

does not containthewords

,or

,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 (Section 12.6) is consulted, where

the list can vary dependingon the token type. The ﬁrst dictionary that recognizes the token emits one

or more normalizedlexemes torepresentthe token. For example,

rats

became

rat

becauseoneof the

dictionaries recognizedthat the word

rats

is a plural form of

rat

.Somewords arerecognizedas stop

words (Section 12.6.1), which causes them to be ignored since they occur too frequently to be useful

in searching. In our example these are

,and

.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 dictionariesassignedfor its tokentype (

Space symbols

), meaningspacetokens willnever be

indexed. The choices of parser, dictionaries and which types of tokens toindex are determined by the

selectedtextsearch conﬁguration(Section12.7). Itis possible tohavemanydifferentconﬁgurations in

the same database, and predeﬁned conﬁgurations are available for various languages. In our example

we used the default conﬁguration

english

for the Englishlanguage.

The function

setweight

can be used to label the entries of a

tsvector

with a given weight, where

aweight is one of the letters

,or

.This is typically usedto markentries coming from different

parts of adocument, suchas title versus body. Later, this informationcanbe used for rankingof 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

.(Section

12.4.1 gives details about these operations.)

12.3.2. Parsing Queries

PostgreSQL provides the functions

to_tsquery

and

plainto_tsquery

for converting a query to

the

tsquery

data type.

to_tsquery

offers access to more features than

plainto_tsquery

,but is

less forgiving about its input.

to_tsquery([

config regconfig

, ]

querytext text

) returns

tsquery

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Chapter 12. Full Text Search

to_tsquery

creates a

tsquery

value from

querytext

,which must consist of single tokens sep-

arated by the Boolean operators

(AND),

(OR) and

(NOT). These operators can be grouped

using parentheses. In other words, the input to

to_tsquery

must already follow the general rules

for

tsquery

input, as described in Section 8.11. The difference is that while basic

tsquery

input

takes the tokens at face value,

to_tsquery

normalizes each token to a lexeme using the speciﬁed or

default conﬁguration, anddiscards 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 toa lexeme to specify preﬁx matching:

SELECT to_tsquery(’supern:

A & star:A

B’);

to_tsquery

--------------------------

’supern’:

A & ’star’:

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

ANDor OR operator.

plainto_tsquery([

config regconfig

, ]

querytext text

) returns

tsquery

plainto_tsquery

transforms unformattedtext

querytext

tsquery

.The textis parsedandnor-

malizedmuchas for

to_tsvector

,thenthe

(AND) Booleanoperator is insertedbetweensurviving

words.

Example:

SELECT plainto_tsquery(’english’, ’The Fat Rats’);

plainto_tsquery

-----------------

’fat’ & ’rat’

Note that

plainto_tsquery

cannot recognize Boolean operators, weight labels, or preﬁx-match

labels in its input:

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Chapter 12. Full Text Search

SELECT plainto_tsquery(’english’, ’The Fat & Rats:C’);

plainto_tsquery

---------------------

’fat’ & ’rat’ & ’c’

Here, all the input punctuation was discarded as being space symbols.

12.3.3. Ranking Search Results

Ranking attempts to measure how relevant documents are to a particular query, sothatwhen there are

manymatches the mostrelevantones canbe shown ﬁrst. PostgreSQL provides two predeﬁned ranking

functions, whichtakeinto account lexical, proximity, andstructuralinformation; thatis, theyconsider

how often the query terms appear inthe 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 modiﬁcationtime. 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 tworanking functions currently available are:

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([

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" inthe journal "Information Processingand Management", 1999. Cover density is simi-

lar to

ts_rank

ranking exceptthattheproximity of matchinglexemes to each other is takeninto

consideration.

This function requires lexeme positional information to perform its calculation. Therefore, it

ignores any “stripped” lexemes in the

tsvector

.If there areno unstripped lexemes intheinput,

the result will be zero. (See Section 12.4.1 for more information about the

strip

function and

positional information in

tsvector

s.)

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}

Typicallyweights are usedtomarkwordsfrom special areas of the document, like the title or aninitial

abstract, sotheycan 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

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Chapter 12. Full Text Search

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

•

1divides the rankby 1 + the logarithm of the document length

•

2divides the rankby the document length

•

4divides the rank by the mean harmonic distance between extents (this is implemented only by

ts_rank_cd

)

•

8divides the rankby 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 impos-

sible 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

343

Chapter 12. Full Text Search

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.

12.3.4. Highlighting Results

To present search results itis ideal to show a part of each document and howit is relatedto 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([

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. 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:

•

StartSel

StopSel

:the strings withwhich to delimitquery words appearing inthe document, to

distinguish them from other excerpted words. You must double-quote these strings if they contain

spaces or commas.

•

MaxWords

MinWords

:these numbers determine the longest and shortest headlines to output.

•

ShortWord

:words of this length or less will be dropped at the start and end of a headline. The

default value of three eliminates common English articles.

•

HighlightAll

:Boolean ﬂag; if

true

the whole document will be used as the headline, ignoring

the preceding three parameters.

•

MaxFragments

:maximum number of text excerpts or fragments to display. The default value of

zero selects a non-fragment-oriented headline generation method. A value greater than zero selects

fragment-based headline generation. This method ﬁnds text fragments with as many query words

as possible and stretches those fragments around the query words. As a result query words are

close to the middle of each fragment and have words on each side. Each fragment will be of at

most

MaxWords

and words of length

ShortWord

or less are dropped at the start and end of each

fragment. If not all query words are found in the document, then a single fragment of the ﬁrst

MinWords

in the document will be displayed.

•

FragmentDelimiter

:Whenmorethanone fragmentis displayed, the fragmentswill be separated

by this string.

Any unspeciﬁed options receive these defaults:

StartSel=, StopSel=,

MaxWords=35, MinWords=15, ShortWord=3, HighlightAll=FALSE,

MaxFragments=0, FragmentDelimiter=" ... "

For example:

344

Chapter 12. Full Text Search

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(’query & similarity’));

ts_headline

------------------------------------------------------------

containing given query terms

and return them in order of their similarity to the

query.

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(’query & similarity’),

’StartSel = <, StopSel = >’);

ts_headline

-------------------------------------------------------

containing given <query> terms

and return them in order of their <similarity> to the

<query>.

ts_headline

uses the original document, not a

tsvector

summary, so it can be slow and should

be usedwith care. A typical mistake is to call

ts_headline

for every matchingdocument whenonly

ten documents are to be shown. SQL subqueries can help; here is an example:

SELECT id, ts_headline(body, q), rank

FROM (SELECT id, body, q, ts_rank_cd(ti, q) AS rank

FROM apod, to_tsquery(’stars’) q

WHERE ti @@ q

ORDER BY rank DESC

LIMIT 10) AS foo;

12.4. Additional Features

This sectiondescribes additionalfunctions andoperatorsthat are useful in connectionwith textsearch.

12.4.1. Manipulating Documents

Section 12.3.1 showed how raw textual documents can be converted into

tsvector

values. Post-

greSQL also provides functions and operators that can be used to manipulate documents that are

already in

tsvector

form.

345

Chapter 12. Full Text Search

tsvector

The

tsvector

concatenation operator returns a vector which combines the lexemes and posi-

tionalinformationof thetwo vectors givenas arguments. Positions and weight labels areretained

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 argu-

ment 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(

vector tsvector

weight "char"

) returns

tsvector

setweight

returns a copy of the input vector in whichevery position has been labeled with the

given

weight

,either

,or

is thedefault for newvectors andas suchisnotdisplayed 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(

vector tsvector

) returns

integer

Returns the number of lexemes stored in the vector.

strip(

vector tsvector

) returns

tsvector

Returns a vector which lists the same lexemes as the given vector, but which lacks any position

or weight information. While the returned vector is much less useful than an unstripped vector

for relevance ranking, it will usually be much smaller.

12.4.2. Manipulating Queries

Section 12.3.2 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

Returns the AND-combination of the two given queries.

tsquery

Returns the OR-combination of the two given queries.

tsquery

Returns the negation (NOT) of the given query.

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).

346

Chapter 12. Full Text Search

Examples:

SELECT numnode(plainto_tsquery(’the any’));

NOTICE:

query contains only stopword(s) or doesn’t contain lexeme(s), ignored

numnode

---------

SELECT numnode(’foo & bar’::tsquery);

numnode

---------

querytree(

query tsquery

) returns

text

Returns theportion of a

tsquery

that can beused for searching anindex. This function is useful

for detectingunindexable queries, for example those containing onlystop words or only negated

terms. For example:

SELECT querytree(to_tsquery(’!defined’));

querytree

-----------

12.4.2.1. Query Rewriting

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

searchto direct the user to some hot topic. There is some overlapin functionality between this feature

and thesaurus dictionaries (Section 12.6.4). However, you canmodify 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 a 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’

347

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