# The tsearch2 Guide

Brandon Craig Rhodes
30 June 2003
Updated to 8.2 release by Oleg Bartunov, October 2006

This Guide introduces the reader to the PostgreSQL tsearch2 module, version 2. More formal descriptions of the module's types and functions are provided in the tsearch2 Reference, which is a companion to this document.

First we will examine the `tsvector` and `tsquery` types and how they are used to search documents; next, we will use them to build a simple search engine in SQL; and finally, we will study the internals of document conversion and how you might tune the internals to accommodate various searching needs.

Once you have tsearch2 working with PostgreSQL, you should be able to run the examples here exactly as they are typed.

## Table of Contents

> Introduction to FTS with tsearch2
> Vectors and Queries
> A Simple Search Engine
> Ranking and Position Weights
> Casting Vectors and Queries
> Parsing and Lexing
> Additional information

## Introduction to FTS with tsearch2

The purpose of FTS is to find documents, which satisfy query and optionally return them in some order. Most common case: Find documents containing all query terms and return them in order of their similarity to the query. Document in database can be any text attribute, or combination of text attributes from one or many tables (using joins). Text search operators existed for years, in PostgreSQL they are `~,~*, LIKE, ILIKE`, but they lack linguistic support, tends to be slow and have no relevance ranking. The idea behind tsearch2 is is rather simple - preprocess document at index time to save time at search stage. Preprocessing includes

- document parsing onto words
- linguistic - normalize words to obtain lexemes
- store document in optimized for searching way

Tsearch2, in a nutshell, provides FTS operator (contains) for two new data types, which represent document and query - `tsquery @@ tsvector`.

## Vectors and Queries

> This section introduces the two data types upon which tsearch2 search engines are based, and illustrates their interaction using the simplest possible case. The complex examples we present later on are merely variations and elaborations of this basic mechanism.

The tsearch2 module allows you to index documents by the words they contain, and then perform very efficient searches for documents that contain a given combination of words. Preparing your document index involves two steps:

- Making a list of the words each document contains. You must reduce each document to a `tsvector` which lists each word that appears in the document. This process offers many options, because there is no requirement that you must copy words into the vector exactly as they appear in the document. For example, many developers omit frequent and content-free stop words like the to reduce the size of their index; others reduce different forms of the same word (forked, forking, forks) to a common form (fork) to make search results independent of tense and case. Because words are very often stored in a modified form, we use the special term lexemes for the word forms we actually store in the vector.
- Creating an index of the documents by lexeme. This is managed automatically by tsearch2 when you creat a `gist()` index on the `tsvector` column of a table, which implements a form of the Berkeley Generalized Search Tree. Since PostgreSQL 8.2 tsearch2 supports Gin index, which is an inverted index, commonly used in search engines. It adds scalability to tsearch2.

Once your documents are indexed, performing a search involves:

- Reducing the search terms to lexemes. You must express each search you want to perform as a `tsquery` specifying a boolean combination of lexemes. Note that tsearch2 only finds exact matches between the lexemes in your query and the ones in each vector — even capitalization counts as a difference (which is why all lexemes are usually kept lowercase). So you must process search words the same way you processed document words; if forking became fork in the document's `tsvector`, then the search term forking must also become fork or the search will not find the document.
- Retrieving the documents that match the query. Running a `SELECT` ... `WHERE` `query` `@@` `vector` on the table with the `vector` column will return the documents that match your query.
- Presenting your results. This final stage offers as many options as turning documents into vectors. You can order documents by how well they matched the search terms; create a headline for each document showing some of the phrases in which it uses the search terms; and restrict the number of results retrieved. You will of course want some way to identify each document, so the user can ask for the full text of the ones he wants to read.

And beyond deciding upon rules for turning documents into vectors and for presenting search results to users, you have to decide where to perform these operations — whether one database server will parse documents, perform searches, and prepare search results, or whether to spread the load of these operations across several machines. These are complicated design issues which we will explore later; in this section and the next, we will illustrate what can be accomplished using a single database server.

The `default` tsearch2 configuration, which we will learn more about later, provides a good example of a process for reducing documents to vectors:

    =# SELECT set_curcfg('default')
    =# SELECT to_tsvector('The air smells of sea water.')
                 to_tsvector
    -------------------------------------
     'air':2 'sea':5 'smell':3 'water':6
    (1 row)

Note the complex relationship between this document and its vector. The vector lists only words from the document — spaces and punctuation have disappeared. Common words like the and of have been eliminated. The -s that makes smells a plural has been removed, leaving a lexeme that represents the word in its simplest form. And finally, though the vector remembers the positions in which each word appeared, it does not store the lexemes in that order.

Keeping word positions in your vectors is optional, by the way. The positions are necessary for the tsearch2 ranking functions, which you can use to prioritize documents based on how often each document uses the search terms and whether they appear in close proximity. But if you do not perform ranking, or use your own process that ignores the word positions stored in the vector, then you can save space by stripping them from your vectors:

    =# SELECT strip(to_tsvector('The air smells of sea water.'))
                strip
    -----------------------------
     'air' 'sea' 'smell' 'water'
    (1 row)

Now that we have a procedure for creating vectors, we can build an indexed table of vectors very simply:

    =# CREATE TABLE vectors ( vector tsvector )
    =# CREATE INDEX vector_index ON vectors USING gist(vector)
    =# INSERT INTO vectors VALUES (to_tsvector('The path forks here'))
    =# INSERT INTO vectors VALUES (to_tsvector('A crawl leads west'))
    =# INSERT INTO vectors VALUES (to_tsvector('The left fork leads northeast'))
    =# SELECT * FROM vectors
                      vector
    ------------------------------------------
     'fork':3 'path':2
     'lead':3 'west':4 'crawl':2
     'fork':3 'lead':4 'left':2 'northeast':5
    (3 rows)

Now we can search this collection of document vectors using the `@@` operator and a `tsquery` that specifies the combination of lexemes we are looking for. Note that while vectors simply list lexemes, queries always combine them with the operators ‘`&`’ and, ‘`|`’ or, and ‘`!`’ not, plus parentheses for grouping. Some examples of the query syntax:

‘find documents with the word forks in them’ | `'forks'`
‘... with both forks and leads’ | `'forks & leads'`
‘... with either forks or leads’ | `'forks | leads'`
‘... with either forks or leads, but without crawl’ | `'(forks|leads) & !crawl'`

The tsearch2 module provides a `to_tsquery()` function for creating queries that uses the same process as `to_tsvector()` uses to reduce words to lexemes. For instance, it will remove the -s from the plurals in the last example above:

    =# SELECT to_tsquery('(leads|forks) & !crawl')
               to_tsquery
    --------------------------------
     ( 'lead' | 'fork' ) & !'crawl'
    (1 row)

Again, this is critically important because the search operator `@@` only finds exact matches between the words in a query and the words in a vector; if the document vector lists the lexeme fork but the query looks for the plural form forks, the query would not match that document. Thanks to the symmetry between our process for producing vectors and queries, however, the above searches return correct results:

    =# SELECT * FROM vectors WHERE vector @@ to_tsquery('(leads|forks) & !crawl')
                      vector
    ------------------------------------------
     'fork':3 'path':2
     'fork':3 'lead':4 'left':2 'northeast':5
    (2 rows)

You may want to try the other queries shown above, and perhaps invent some of your own.

You should not include stop words in a query, since you cannot search for words you have discarded. If you throw out the word the when building vectors, for example, your index will obviously not know which documents included it. The `to_tsquery()` function will automatically detect this and give you an error to prevent this mistake:

    =# SELECT to_tsquery('the')
    NOTICE:  Query contains only stopword(s) or doesn't contain lexem(s), ignored
     to_tsquery
    ------------

    (1 row)

But if you every build vectors and queries using your own routines, a possibility we will discuss later, then you will need to enforce this rule yourself.

> Now that you understand how vectors and queries work together, you are prepared to tackle many additional topics: how to distribute searching across many servers; how to customize the process by which tsearch2 turns documents and queries into lexemes, or use a process of your own; and how to sort and display search results to your users. But before discussing these detailed questions, we will build a simple search engine to see how easily its basic features work together.

## A Simple Search Engine

> In this section we build a simple search engine out of SQL functions that use the vector and query types described in the previous section. While this example is simpler than a search engine that has to interface with the outside world, it will illustrate the basic principles of building a search engine, and better prepare you for developing your own.

Building a search engine involves only a few improvements upon the rudimentary vector searches described in the last section.

- Because the user wants to read documents, not vectors, you must provide some way for the full text of each document to be accessed — either by storing the entire text of each document in the database, or storing an identifier like a URL, file name, or document routing number that lets you fetch the document from other storage.
- You can make it easier for user interface code to refer to each document by providing a unique identifier for each document, perhaps with a `SERIAL` column.
- Search results should be ordered by relevance. If you leave word positions in your vectors, you can either have PostgreSQL `ORDER` your results `BY` a ranking function, or you can fetch the vectors yourself and perform your own sort. If you choose to ignore word positions or strip them from your vectors, you will have to determine relevance yourself, using either the full text of the document or other information about each document you may possess.
- For each document returned by a search, you will usually want to display a summary called a headline that shows short excerpts illustrating how the document uses the query words. Headlines are usually generated from the full text of the document, not from position information in the `tsvector`, since excerpts lacking stop words, punctuation, and suffixes would not be comprehensible. If you store the full text of each document in the database, headlines can be generated very simply by a tsearch2 function. If you store your documents elsewhere, then you will either have to transmit each document to the database every time you want to run the headline function on it, or use your own headline code outside of the database.

We can easily construct a simple search engine that accomplishes these goals. First we build a table that, for each document, stores a unique identifier, the full text of the document, and its `tsvector`:

    =# CREATE TABLE docs ( id SERIAL, doc TEXT, vector tsvector )
    =# CREATE INDEX docs_index ON docs USING gist(vector);

Note that although searches will still work on tables where you have neglected to create a `gist()` index over your vectors, they will run much more slowly since they will have to compare the query against every document vector in the table.

Because the table we have created stores each document in two different ways — both as text and as a vector — our `INSERT` statements must provide the document in both forms. While more advanced PostgreSQL programmers might accomplish this with a database trigger or rule, for this simple example we will use a small SQL function:

    =# CREATE FUNCTION insdoc(text) RETURNS void LANGUAGE sql AS
      'INSERT INTO docs (doc, vector) VALUES ($1, to_tsvector($1));'

Now, by calling `insdoc()` several times, we can populate our table with documents:

    =# SELECT insdoc('A low crawl over cobbles leads inward to the west.')
    =# SELECT insdoc('The canyon runs into a mass of boulders -- dead end.')
    =# SELECT insdoc('You are crawling over cobbles in a low passage.')
    =# SELECT insdoc('Cavernous passages lead east, north, and south.')
    =# SELECT insdoc('To the east a low wide crawl slants up.')
    =# SELECT insdoc('You are in the south side chamber.')
    =# SELECT insdoc('The passage here is blocked by a recent cave-in.')
    =# SELECT insdoc('You are in a splendid chamber thirty feet high.')

Now we can build a search function. Its `SELECT` statement is based upon the same `@@` operation illustrated in the previous section. But instead of returning matching vectors, we return for each document its `SERIAL` identifier, so the user can retrieve it later; a headline that illustrates its use of the search terms; and a ranking with which we also order the results. Our search operation can be coded as a single `SELECT` statement returning its own kind of table row, which we call a `finddoc_t`:

    =# CREATE TYPE finddoc_t AS (id INTEGER, headline TEXT, rank REAL)
    =# CREATE FUNCTION finddoc(text) RETURNS SETOF finddoc_t LANGUAGE sql AS '
       SELECT id, headline(doc, q), rank(vector, q)
         FROM docs, to_tsquery($1) AS q
         WHERE vector @@ q ORDER BY rank(vector, q) DESC'

This function is a rather satisfactory search engine. Here is one example search, after which the user fetches the top-ranking document itself; with similar commands you can try queries of your own:

    =# SELECT * FROM finddoc('passage|crawl')
     id |                       headline                        | rank
    ----+-------------------------------------------------------+------
      3 | <b>crawling</b> over cobbles in a low <b>passage</b>. | 0.19
      1 | <b>crawl</b> over cobbles leads inward to the west.   |  0.1
      4 | <b>passages</b> lead east, north, and south.          |  0.1
      5 | <b>crawl</b> slants up.                               |  0.1
      7 | <b>passage</b> here is blocked by a recent  cave-in.  |  0.1
    (5 rows)
    =# SELECT doc FROM docs WHERE id = 3
                           doc
    -------------------------------------------------
     You are crawling over cobbles in a low passage.
    (1 row)

While by default the `headline()` function surrounds matching words with `<b>` and `</b>` in order to distinguish them from the surrounding text, you can provide options that change its behavior; consult the tsearch2 Reference for more details about Headline Functions.

Though a search may match hundreds or thousands of documents, you will usually present only ten or twenty results to the user at a time. This can be most easily accomplished by limiting your query with a `LIMIT` and an `OFFSET` clause — to display results ten at a time, for example, your would generate your first page of results with `LIMIT` `10` `OFFSET` `0`, your second page with `LIMIT` `10` `OFFSET` `10`, your third page with `LIMIT` `10` `OFFSET` `20`, and so forth. There are two problems with this approach, however.

The first problem is the strain of running the query over again for every page of results the user views. For small document collections or lightly loaded servers, this may not be a problem; but the impact can be high when a search must repeatedly rank and sort the same ten thousand results on an already busy server. So instead of selecting only one page of results, you will probably use `LIMIT` and `OFFSET` to return a few dozen or few hundred results, which you can cache and display to the user one page at a time. Whether a result cache rewards your effort will depend principally on the behavior of your users — how often they even view the second page of results, for instance.

The second issue solved by caching involves consistency. If the database is changing while the user browses their results, then documents might appear and disappear as they page through them. In some cases the user might even miss a particular result — perhaps the one they were looking for — if, say, its rank improves from 31th to 30th after they load results 21–30 but before they view results 31–40. While many databases are static or infrequently updated, and will not present this problem, users searching very dymanic document collections might benefit from the stable results that caches yield.

> Having seen the features of a search engine implemented entirely within the database, we will learn about some specific tsearch2 features. First we will look in more detail at document ranking.

## Ranking and Position Weights

> When we built our simple search engine, we used the `rank()` function to order our results. Here we describe tsearch2 ranking in more detail.

There are two functions with which tsearch2 can rank search results. They both use the lexeme positions listed in the `tsvector`, so you cannot rank vectors from which these have been removed with `strip()`. The `rank()` function existed in older versions of OpenFTS, and has the feature that you can assign different weights to words from different sections of your document. The `rank_cd()` uses a recent technique for weighting results and also allows different weight to be given to different sections of your document (since 8.2).

Both ranking functions allow you to specify, as an optional last argument, whether you want their results normalized — whether the rank returned should be adjusted for document length. Specifying a last argument of `0` (zero) makes no adjustment; `1` (one) divides the document rank by the logarithm of the document length; and `2` divides it by the plain length. In all of these examples we omit this optional argument, which is the same as specifying zero — we are making no adjustment for document length.

The `rank_cd()` function uses an experimental measurement called cover density ranking that rewards documents when they make frequent use of the search terms that are close together in the document. You can read about the algorithm in more detail in Clarke et al., “Relevance Ranking for One to Three Term Queries.” An optional first argument allows you to tune their formula; for details see the section on ranking in the Reference.

Currently tsearch2 supports four different weight labels: `'D'`, the default weight; and `'A'`, `'B'`, and `'C'`. All vectors created with `to_tsvector()` assign the weight `'D'` to each position, which as the default is not displayed when you print a vector out.

If you want positions with weights other than `'D'`, you have two options: either you can author a vector directly through the `::tsvector` casting operation, as described in the following section, which lets you give each position whichever weight you want; or you can pass a vector through the `setweight()` function which sets all of its position weights to a single value. An example of the latter:

    =# SELECT vector FROM docs WHERE id = 3
                     vector
    ----------------------------------------
     'low':8 'cobbl':5 'crawl':3 'passag':9
    (1 row)
    =# SELECT setweight(vector, 'A') FROM docs WHERE id = 3
                     setweight
    --------------------------------------------
     'low':8A 'cobbl':5A 'crawl':3A 'passag':9A
    (1 row)

Merely changing all of the weights in a vector is not very useful, of course, since this results still in all words having the same weight. But if we parse different parts of a document separately, giving each section its own weight, and then concatenate the vectors of each part into a single vector, the result can be very useful. We can construct a simple example in which document titles are given greater weight that text in the body of the document:

    =# CREATE TABLE tdocs ( id SERIAL, title TEXT, doc TEXT, vector tsvector )
    =# CREATE INDEX tdocs_index ON tdocs USING gist(vector);
    =# CREATE FUNCTION instdoc(text, text) RETURNS void LANGUAGE sql AS
      'INSERT INTO tdocs (title, doc, vector)
       VALUES ($1, $2, setweight(to_tsvector($1), ''A'') || to_tsvector($2));'

Now words from a document title will be weighted differently than those in the main text if we provide the title and body as separate arguments:

    =# SELECT instdoc('Spendid Chamber',
     'The walls are frozen rivers of orange stone.')
     instdoc
    ---------

    (1 row)
    =# SELECT vector FROM tdocs
                                        vector
    ------------------------------------------------------------------------------
     'wall':4 'orang':9 'river':7 'stone':10 'frozen':6 'chamber':2A 'spendid':1A
    (1 row)

Note that although the necessity is unusual, you can constrain search terms to only match words from certain sections by following them with a colon and a list of the sections in which the word can occur; by default this list is `'ABCD'` so that search terms match words from all sections. For example, here we search for a word both generally, and then looking only for specific weights:

    =# SELECT title, doc FROM tdocs WHERE vector @@ to_tsquery('spendid')
          title      |                     doc
    -----------------+----------------------------------------------
     Spendid Chamber | The walls are frozen rivers of orange stone.
    (1 row)
    =# SELECT title, doc FROM tdocs WHERE vector @@ to_tsquery('spendid:A')
          title      |                     doc
    -----------------+----------------------------------------------
     Spendid Chamber | The walls are frozen rivers of orange stone.
    (1 row)
    =# SELECT title, doc FROM tdocs WHERE vector @@ to_tsquery('spendid:D')
     title | doc
    -------+-----
    (0 rows)

> Our examples so far use tsearch2 to parse our documents into vectors. When your application needs absolute control over vector content, you will want to use direct type casting, which is described in the next section.

## Casting Vectors and Queries

> While tsearch2 has powerful and flexible ways to process documents and turn them into document vectors, you will sometimes want to parse documents on your own and place the results directly in vectors. Here we show you how.

In the preceding examples, we used the `to_tsvector()` function when we needed a document's text reduced to a document vector. We saw that the function stripped whitespace and punctuation, eliminated common words, and altered suffixes to reduce words to a common form. While these operations are often desirable, and while in the sections below we will gain precise control over this process, there are occasions on which you want to avoid the changes that `to_tsvector()` makes to text and specify explicitly the words that you want in your vectors. Or you may want to create queries directly rather than through `to_tsquery()`.

For example, you may have already developed your own routine for reducing your documents to searchable lexemes, and do not want your carefully generated terms altered by passing them through `to_tsvector()`. Or you might be developing and debugging parsing routines of your own that you are not ready to load into the database. In either case, you will find that direct insertion is easily accomplished if you simply follow some simple rules.

Vectors are created directly when you cast a string of whitespace separated lexemes to the `tsvector` type:

    =# select 'the only exit is the way you came in'::tsvector
                         tsvector
    --------------------------------------------------
     'in' 'is' 'the' 'way' 'you' 'came' 'exit' 'only'
    (1 row)

Notice that the conversion interpreted the string simply as a list of lexemes to be included in the vector. Their order was lost, as was the number of times each lexeme appeared. You must keep in mind that directly creating vectors with casting is not an alternative means of parsing; it is a way of directly entering lexemes into a vector without parsing.

Queries can also be created through casting, if you separate lexemes with boolean operators rather than with whitespace. When creating your own vectors and queries, remember that the search operator `@@` finds only exact matches between query lexemes and vector lexemes — if they are not exactly the same string, they will not be considered a match.

To include lexeme positions in your vector, write the positions exactly the way tsearch2 displays them when it prints vectors: by following each lexeme with a colon and a comma-separated list of integer positions. If you list a lexeme more than once, then all the positions listed for it are combined into a single list. For example, here are two ways of writing the same vector, depending on whether you mention ‘`the`’ twice or combine its positions into a list yourself:

    =# select 'the:1 only:2 exit:3 is:4 the:5 way:6 you:7 came:8 in:9'::tsvector
                                  tsvector
    --------------------------------------------------------------------
     'in':9 'is':4 'the':1,5 'way':6 'you':7 'came':8 'exit':3 'only':2
    (1 row)
    =# select 'the:1,5 only:2 exit:3 is:4 way:6 you:7 came:8 in:9'::tsvector
                                  tsvector
    --------------------------------------------------------------------
     'in':9 'is':4 'the':1,5 'way':6 'you':7 'came':8 'exit':3 'only':2
    (1 row)

Things can get slightly tricky if you want to include apostrophes, backslashes, or spaces inside your lexemes (wanting to include either of the latter would be unusual, but they can be included if you follow the rules). The main problem is that the apostrophe and backslash are important both to PostgreSQL when it is interpreting a string, and to the `tsvector` conversion function. You may want to review section “String Constants” in the PostgreSQL documentation before proceeding.

When you cast strings directly into vectors:

- The string is interpreted as a whitespace-separated list of lexemes, any of which can be suffixed with a colon and a list of positions.
- A lexeme can be quoted by preceding it with an apostrophe, in which case it runs until the next apostrophe; otherwise a lexeme ends with the first whitespace or colon encountered.
- Any character preceded by a backslash, including whitespace, the apostrophe, the colon, and the backslash itself, loses its normal meaning and is treated as a letter. Backslashes are effective both inside and outside of apostrophe-quoted lexemes.
- A lexeme can be suffixed with a list of positions by appending a colon and a comma-separated list of integers, each of which can itself be followed by a letter to designate a position weight (position weights are described below).

Here are some example strings, showing the lexeme you want to insert together with the string that the `::tsvector` operator needs to see, and how you would type that string at the PostgreSQL prompt:

For the lexeme... | you need the string... | which you can type as:
`nugget` | `nugget` | `'nugget'`
`won't` | `won't` | `'won''t'`
`pinin'` | `pinin'` | `'pinin'''`
`'bout` | `\'bout` | `'\\''bout'`
`white mist` | `white\ mist` | `'white\\ mist'`
`or:` | `'white mist'` | `'''white mist'''`
`won't budge` | `won\'t\ budge` | `'won\\''t\\ budge'`
`or:` | `'won\'t budge'` | `'''won\\''t budge'''`
`back\slashed` | `back\\slashed` | `'back\\\\slashed'`

Remember to use the quoted quoting shown at the right only when typing in strings as part of a PostgreSQL query. If you are providing strings through a library that automatically quotes them or provides them in binary form to PostgreSQL, then you can use the strings in the middle instead — suitably quoted in the language you are using, of course.

Position weights are described below and can be written exactly as they will be displayed when you select a weighted vector:

    =# select 'weighty:1,3A trivial:2B,4'::tsvector
               tsvector
    -------------------------------
     'trivial':2B,4 'weighty':1,3A
    (1 row)

Note that if you are composing SQL queries in a scripting language like Perl or Python, that itself considers quotes and backslashes special, then you may have another quoting layer to deal with on top of the two layers already shown above. In such cases you may want to write a function that performs the necessary quoting for you.

> Having seen how to create vectors of your own, it is time to learn how the native tsearch2 parser reduces documents to vectors.

## Parsing and Lexing

> The previous section described how you can bypass the parser provided by tsearch2 and populate your table of documents with vectors of your own devising. But for those interested in the native tsearch2 facilities, we present here an overview of how it goes about reducing documents to vectors.

The `to_tsvector()` function reduces documents to vectors in two stages. First, a parser breaks the input document into short sequences of text called tokens. Each token is usually a word, space, or piece of punctuation, though some parsers return larger and more exotic items like HTML tags as single tokens. Each token returned by the parser is either discarded or passed to a dictionary that converts it into a lexeme. The resulting lexemes are collected into a vector and returned.

The choice of which parser and dictionaries `to_tsvector()` should use is controlled by your choice of configuration. The tsearch2 module comes with several configurations, and you can define more of your own; in fact the creation of a new configuration is illustrated below, in the section on position weights.

To learn about parsing in more detail, we will study this example:

    =# select to_tsvector('default',
         'The walls extend upward for well over 100 feet.')
                           to_tsvector
    ----------------------------------------------------------
     '100':8 'feet':9 'wall':2 'well':6 'extend':3 'upward':4
    (1 row)

Unlike the `to_tsvector()` calls used in the above examples, this one specifies the `'default'` configuration explicitly. When we called `to_tsvector()` in earlier examples with only one argument, it used the current configuration, which is chosen automatically based on your `LOCALE` if that locale is mentioned in the `pg_ts_cfg` table (which is shown under the first bullet in the description below). If your locale is not listed in the table, your attempts to use the current configuration will return:

    ERROR:  Can't find tsearch2 config by locale

You can always change the current configuration manually by calling the `set_curcfg()` function described in the section on Configurations in the Reference.

Each configuration serves as an index into two different tables: in `pg_ts_cfg` it determines which parser will break our text into tokens, and in `pg_ts_cfgmap` it directs each token to a dictionary for processing. The steps in detail are:

- First, our text is parsed, using the parser listed for our configuration in the `pg_ts_cfg` table. We are using the `'default'` configuration, so the table tells us to use the `'default'` parser:

      =# SELECT * FROM pg_ts_cfg WHERE ts_name = 'default'
       ts_name | prs_name | locale
      ---------+----------+--------
       default | default  | C
      (1 row)

  So our text will be parsed as though we had called:

      =# select * from parse('default',
           'The walls extend upward for well over 100 feet.')

  This breaks the text into a list of tokens which are each labelled with an integer type:

  The1♦12walls1♦12extend1♦12upward1♦12for1♦12well1♦12over1♦1210022♦12feet1.12

  Each word has been assigned type 1; each space (represented here by a diamond) and the period, type 12; and the number one hundred, type 22. We can retrieve the alias for each type through the `token_type` function:

      =# select * from token_type('default')
           where tokid = 1 or tokid = 12 or tokid = 22
       tokid | alias |      descr
      -------+-------+------------------
           1 | lword | Latin word
          12 | blank | Space symbols
          22 | uint  | Unsigned integer
      (3 rows)

- Next, the tokens are assigned to dictionaries by looking up their type aliases in `pg_ts_cfgmap` to determine which dictionary should process each token. Since we are using the `'default'` configuration:

      =# select * from pg_ts_cfgmap where ts_name = 'default' and
            (tok_alias = 'lword' or tok_alias = 'blank' or tok_alias = 'uint')
       ts_name | tok_alias | dict_name
      ---------+-----------+-----------
       default | lword     | {en_stem}
       default | uint      | {simple}
      (2 rows)

  Since this map provides no dictionary for `blank` tokens, the spaces and period are simply discarded, leaving nine tokens, which are then numbered by their position:

  The1 walls2 extend3 upward4 for5 well6 over7 1008 feet9

- Finally, the words are reduced to lexemes by their respective dictionaries. The `100` is submitted to the `simple` dictionary, which returns tokens unaltered except for making them lowercase:

      =# select lexize('simple', '100')
       lexize
      --------
       {100}
      (1 row)

  The other words are submitted to `en_stem` which reduces each English word to a linguistic stem, and then discards stems which belong to its list of stop words; you can see the list of stop words in the file whose path is in the `dict_initoption` field of the `pg_ts_dict` table entry for `en_stem`. The first three words of our text illustrate respectively an `en_stem` stop word, a word which `en_stem` alters by stemming, and a word which `en_stem` leaves alone:

      =# select lexize('en_stem', 'The')
       lexize
      --------
       {}
      (1 row)
      =# select lexize('en_stem', 'walls')
       lexize
      --------
       {wall}
      (1 row)
      =# select lexize('en_stem', 'extend')
        lexize
      ----------
       {extend}
      (1 row)

  Once `en_stem` is done discarding stop words and stemming the rest, we are left with:

  wall2 extend3 upward4 well6 1008 feet9

  Which is precisely the result of the example that began this section.

Query words are stemmed by the `to_tsquery()` function using the same scheme to determine the dictionary for each token, with the difference that the query parser recognizes as special the boolean operators that separate query words.

## Additional information

More information about tsearch2 is available from tsearch2 page. Also, it's worth to check tsearch2 wiki pages.