## Tsearch2 - Introduction

[Online version] of this document is available.

The tsearch2 module is available to add as an extension to the PostgreSQL database to allow for Full Text Indexing. This document is an introduction to installing, configuring, using and maintaining the database with the tsearch2 module activated.

Please, note, tsearch2 module is fully incompatible with old tsearch, which is deprecated in 7.4 and will be obsoleted in 7.5.

### USING TSEARCH2 AND POSTGRESQL FOR A WEB BASED SEARCH ENGINE

This documentation is provided as a short guide on how to quickly get up and running with tsearch2 and PostgreSQL, for those who want to implement a full text indexed based search engine. It is not meant to be a complete in-depth guide into the full ins and outs of the contrib/tsearch2 module, and is primarily aimed at beginners who want to speed up searching of large text fields, or those migrating from other database systems such as MS-SQL.

The README.tsearch2 file included in the contrib/tsearch2 directory contains a brief overview and history behind tsearch. This can also be found online [right here].

Further in depth documentation such as a full function reference, and user guide can be found online at the [tsearch documentation home].

### ACKNOWLEDGEMENTS

Robert John Shepherd originally wrote this documentation for the previous version of tsearch module (v1) included with the postgres release. I took his documentation and updated it to comply with the tsearch2 modifications.

Robert's original acknowledgements:

"Thanks to Oleg Bartunov for taking the time to answer many of my questions regarding this module, and also to Teodor Sigaev for clearing up the process of making your own dictionaries. Plus of course a big thanks to the pair of them for writing this module in the first place!"

I would also like to extend my thanks to the developers, and Oleg Bartunov for all of his direction and help with the new features of tsearch2.

### OVERVIEW

MS-SQL provides a full text indexing (FTI) system which enables the fast searching of text based fields, very useful for websites (and other applications) that require a results set based on key words. PostgreSQL ships with a contributed module called tsearch2, which implements a special type of index that can also be used for full text indexing. Further more, unlike MS' offering which requires regular incremental rebuilds of the text indexes themselves, tsearch2 indexes are always up-to-date and keeping them so induces very little overhead.

Before we get into the details, it is recommended that you have installed and tested PostgreSQL, are reasonably familiar with databases, the SQL query language and also understand the basics of connecting to PostgreSQL from the local shell. This document isn't intended for the complete PostgreSQL newbie, but anyone with a reasonable grasp of the basics should be able to follow it.

### INSTALLATION

Starting with PostgreSQL version 7.4 tsearch2 is now included in the contrib directory with the PostgreSQL sources. contrib/tsearch2 is where you will find everything needed to install and use tsearch2. Please note that tsearch2 will also work with PostgreSQL version 7.3.x, but it is not the module included with the source distribution. You will have to download the module separately and install it in the same fashion.

I installed the tsearch2 module to a PostgreSQL 7.3 database from the contrib directory without squashing the original (old) tsearch module. What I did was move the modules tsearch src driectory into the contrib tree under the name tsearchV2.

Step one is to download the tsearch V2 module :

[http://www.sai.msu.su/~megera/postgres/gist/tsearch/V2/] (check Development History for latest stable version !)

            tar -zxvf tsearch-v2.tar.gz
            mv tsearch2 PGSQL_SRC/contrib/
            cd PGSQL_SRC/contrib/tsearch2

If you are installing from PostgreSQL version 7.4 or higher, you can skip those steps and just change to the contrib/tsearch2 directory in the source tree and continue from there.

Then continue with the regular building and installation process

            gmake
            gmake install
            gmake installcheck

That is pretty much all you have to do, unless of course you get errors. However if you get those, you better go check with the mailing lists over at http://www.postgresql.org or http://openfts.sourceforge.net/ since its never failed for me.

The directory in the contib/ and the directory from the archive is called tsearch2. Tsearch2 is completely incompatible with the previous version of tsearch. This means that both versions can be installed into a single database, and migration the new version may be much easier.

NOTE: the previous version of tsearch found in the contrib/tsearch directory is depricated. ALthough it is still available and included within PostgreSQL version 7.4. It will be removed in version 7.5.

### ADDING TSEARCH2 FUNCTIONALITY TO A DATABASE

We should create a database to use as an example for the remainder of this file. We can call the database "ftstest". You can create it from the command line like this:

            #createdb ftstest

If you thought installation was easy, this next bit is even easier. Change to the PGSQL_SRC/contrib/tsearch2 directory and type:

            psql ftstest < tsearch2.sql

The file "tsearch2.sql" holds all the wonderful little goodies you need to do full text indexing. It defines numerous functions and operators, and creates the needed tables in the database. There will be 4 new tables created after running the tsearch2.sql file : pg_ts_dict, pg_ts_parser, pg_ts_cfg, pg_ts_cfgmap are added.

You can check out the tables if you like:

            #psql ftstest
            ftstest=# \d
                        List of relations
             Schema |     Name     | Type  |  Owner
            --------+--------------+-------+----------
             public | pg_ts_cfg    | table | kopciuch
             public | pg_ts_cfgmap | table | kopciuch
             public | pg_ts_dict   | table | kopciuch
             public | pg_ts_parser | table | kopciuch
            (4 rows)

### TYPES AND FUNCTIONS PROVIDED BY TSEARCH2

The first thing we can do is try out some of the types that are provided for us. Lets look at the tsvector type provided for us:

            SELECT 'Our first string used today'::tsvector;
                            tsvector
            ---------------------------------------
             'Our' 'used' 'first' 'today' 'string'
            (1 row)

The results are the words used within our string. Notice they are not in any particular order. The tsvector type returns a string of space separated words.

            SELECT 'Our first string used today first string'::tsvector;
                                tsvector
            -----------------------------------------------
             'Our' 'used' 'again' 'first' 'today' 'string'
            (1 row)

Notice the results string has each unique word ('first' and 'string' only appear once in the tsvector value). Which of course makes sense if you are searching the full text ... you only need to know each unique word in the text.

Those examples were just casting a text field to that of type tsvector. Lets check out one of the new functions created by the tsearch2 module.

The function to_tsvector has 3 possible signatures:

            to_tsvector(oid, text);
            to_tsvector(text, text);
            to_tsvector(text);

We will use the second method using two text fields. The overloaded methods provide us with a way to specifiy the way the searchable text is broken up into words (Stemming process). Right now we will specify the 'default' configuration. See the section on TSEARCH2 CONFIGURATION to learn more about this.

            SELECT to_tsvector('default',
                               'Our first string used today first string');
                            to_tsvector
            --------------------------------------------
             'use':4 'first':2,6 'today':5 'string':3,7
            (1 row)

The result returned from this function is of type tsvector. The results came about by this reasoning: All of the words in the text passed in are stemmed, or not used because they are stop words defined in our configuration. Each lower case morphed word is returned with all of the positons in the text.

In this case the word "Our" is a stop word in the default configuration. That means it will not be included in the result. The word "first" is found at positions 2 and 6 (although "Our" is a stop word, it's position is maintained). The word(s) positioning is maintained exactly as in the original string. The word "used" is morphed to the word "use" based on the default configuration for word stemming, and is found at position 4. The rest of the results follow the same logic. Just a reminder again ... the order of the 'word' position in the output is not in any kind of order. (ie 'use':4 appears first)

If you want to view the output of the tsvector fields without their positions, you can do so with the function "strip(tsvector)".

            SELECT strip(to_tsvector('default',
                         'Our first string used today first string'));
                        strip
            --------------------------------
             'use' 'first' 'today' 'string'

If you wish to know the number of unique words returned in the tsvector you can do so by using the function "length(tsvector)"

            SELECT length(to_tsvector('default',
                          'Our first string used today first string'));
             length
            --------
                  4
            (1 row)

Lets take a look at the function to_tsquery. It also has 3 signatures which follow the same rational as the to_tsvector function:

            to_tsquery(oid, text);
            to_tsquery(text, text);
            to_tsquery(text);

Lets try using the function with a single word :

            SELECT to_tsquery('default', 'word');
             to_tsquery
            -----------
             'word'
             (1 row)

I call the function the same way I would a to_tsvector function, specifying the 'default' configuration for morphing, and the result is the stemmed output 'word'.

Lets attempt to use the function with a string of multiple words:

            SELECT to_tsquery('default', 'this is many words');
            ERROR:  Syntax error

The function can not accept a space separated string. The intention of the to_tsquery function is to return a type of "tsquery" used for searching a tsvector field. What we need to do is search for one to many words with some kind of logic (for now simple boolean).

            SELECT to_tsquery('default', 'searching|sentence');
                  to_tsquery
            ----------------------
             'search' | 'sentenc'
            (1 row)

Notice that the words are separated by the boolean logic "OR", the text could contain boolean operators &,|,!,() with their usual meaning.

You can not use words defined as being a stop word in your configuration. The function will not fail ... you will just get no result, and a NOTICE like this:

            SELECT to_tsquery('default', 'a|is&not|!the');
            NOTICE:  Query contains only stopword(s)
                     or doesn't contain lexem(s), ignored
             to_tsquery
            -----------
            (1 row)

That is a beginning to using the types, and functions defined in the tsearch2 module. There are numerous more functions that I have not touched on. You can read through the tsearch2.sql file built when compiling to get more familiar with what is included.

### INDEXING FIELDS IN A TABLE

The next stage is to add a full text index to an existing table. In this example we already have a table defined as follows:

            CREATE TABLE tblMessages
            (
                    intIndex        int4,
                    strTopic        varchar(100),
                    strMessage      text
            );

We are assuming there are several rows with some kind of data in them. Any data will do, just do several inserts with test strings for a topic, and a message. here is some test data I inserted. (yes I know it's completely useless stuff ;-) but it will serve our purpose right now).

            INSERT INTO tblMessages
                   VALUES ('1', 'Testing Topic', 'Testing message data input');
            INSERT INTO tblMessages
                   VALUES ('2', 'Movie', 'Breakfast at Tiffany\'s');
            INSERT INTO tblMessages
                   VALUES ('3', 'Famous Author', 'Stephen King');
            INSERT INTO tblMessages
                   VALUES ('4', 'Political Topic',
                                'Nelson Mandella is released from prison');
            INSERT INTO tblMessages
                   VALUES ('5', 'Nursery rhyme phrase',
                                'Little jack horner sat in a corner');
            INSERT INTO tblMessages
                   VALUES ('6', 'Gettysburg address quotation',
                                'Four score and seven years ago'
                                ' our fathers brought forth on this'
                                ' continent a new nation, conceived in'
                                ' liberty and dedicated to the proposition'
                                ' that all men are created equal');
            INSERT INTO tblMessages
                   VALUES ('7', 'Classic Rock Bands',
                                'Led Zeppelin Grateful Dead and The Sex Pistols');
            INSERT INTO tblMessages
                   VALUES ('8', 'My birth address',
                                '18 Sommervile road, Regina, Saskatchewan');
            INSERT INTO tblMessages
                   VALUES ('9', 'Joke', 'knock knock : who\'s there?'
                                        ' I will not finish this joke');
            INSERT INTO tblMessages
                   VALUES ('10', 'Computer information',
                                 'My computer is a pentium III 400 mHz'
                                 ' with 192 megabytes of RAM');

The next stage is to create a special text index which we will use for FTI, so we can search our table of messages for words or a phrase. We do this using the SQL command:

            ALTER TABLE tblMessages ADD COLUMN idxFTI tsvector;

Note that unlike traditional indexes, this is actually a new field in the same table, which is then used (through the magic of the tsearch2 operators and functions) by a special index we will create in a moment.

The general rule for the initial insertion of data will follow four steps:

        1. update table
        2. vacuum full analyze
        3. create index
        4. vacuum full analyze

The data can be updated into the table, the vacuum full analyze will reclaim unused space. The index can be created on the table after the data has been inserted. Having the index created prior to the update will slow down the process. It can be done in that manner, this way is just more efficient. After the index has been created on the table, vacuum full analyze is run again to update postgres's statistics (ie having the index take effect).

            UPDATE tblMessages SET idxFTI=to_tsvector('default', strMessage);
            VACUUM FULL ANALYZE;

Note that this only inserts the field strMessage as a tsvector, so if you want to also add strTopic to the information stored, you should instead do the following, which effectively concatenates the two fields into one before being inserted into the table:

            UPDATE tblMessages
                SET idxFTI=to_tsvector('default',coalesce(strTopic,'') ||' '|| coalesce(strMessage,''));
            VACUUM FULL ANALYZE;

Using the coalesce function makes sure this concatenation also works with NULL fields.

We need to create the index on the column idxFTI. Keep in mind that the database will update the index when some action is taken. In this case we _need_ the index (The whole point of Full Text INDEXINGi ;-)), so don't worry about any indexing overhead. We will create an index based on the gist function. GiST is an index structure for Generalized Search Tree.

            CREATE INDEX idxFTI_idx ON tblMessages USING gist(idxFTI);
            VACUUM FULL ANALYZE;

After you have converted all of your data and indexed the column, you can select some rows to see what actually happened. I will not display output here but you can play around yourselves and see what happened.

The last thing to do is set up a trigger so every time a row in this table is changed, the text index is automatically updated. This is easily done using:

            CREATE TRIGGER tsvectorupdate BEFORE UPDATE OR INSERT ON tblMessages
                FOR EACH ROW EXECUTE PROCEDURE tsearch2(idxFTI, strMessage);

Or if you are indexing both strMessage and strTopic you should instead do:

            CREATE TRIGGER tsvectorupdate BEFORE UPDATE OR INSERT ON tblMessages
                FOR EACH ROW EXECUTE PROCEDURE
                    tsearch2(idxFTI, strTopic, strMessage);

Before you ask, the tsearch2 function accepts multiple fields as arguments so there is no need to concatenate the two into one like we did before.

If you want to do something specific with columns, you may write your very own trigger function using plpgsql or other procedural languages (but not SQL, unfortunately) and use it instead of tsearch2 trigger.

You could however call other stored procedures from within the tsearch2 function. Lets say we want to create a function to remove certain characters (like the @ symbol from all text).

           CREATE FUNCTION dropatsymbol(text)
                         RETURNS text AS 'select replace($1, \'@\', \' \');' LANGUAGE SQL;

Now we can use this function within the tsearch2 function on the trigger.

          DROP TRIGGER tsvectorupdate ON tblmessages;
            CREATE TRIGGER tsvectorupdate BEFORE UPDATE OR INSERT ON tblMessages
                FOR EACH ROW EXECUTE PROCEDURE tsearch2(idxFTI, dropatsymbol, strMessage);
            INSERT INTO tblmessages VALUES (69, 'Attempt for dropatsymbol', 'Test@test.com');

If at this point you receive an error stating: ERROR: Can't find tsearch config by locale

Do not worry. You have done nothing wrong. And tsearch2 is not broken. All that has happened here is that the configuration is setup to use a configuration based on the locale of the server. All you have to do is change your default configuration, or add a new one for your specific locale. See the section on TSEARCH2 CONFIGURATION.

       SELECT * FROM tblmessages WHERE intindex = 69;

             intindex |         strtopic         |  strmessage   |        idxfti
            ----------+--------------------------+---------------+-----------------------
                    69 | Attempt for dropatsymbol | Test@test.com | 'test':1 'test.com':2
            (1 row)

Notice that the string content was passed throught the stored procedure dropatsymbol. The '@' character was replaced with a single space ... and the output from the procedure was then stored in the tsvector column.

This could be useful for removing other characters from indexed text, or any kind of preprocessing needed to be done on the text prior to insertion into the index.

### QUERYING A TABLE

There are some examples in the README.tsearch2 file for querying a table. One major difference between tsearch and tsearch2 is the operator ## is no longer available. Only the operator @@ is defined, using the types tsvector on one side and tsquery on the other side.

Lets search the indexed data for the word "Test". I indexed based on the the concatenation of the strTopic, and the strMessage:

            SELECT intindex, strtopic FROM tblmessages
                                      WHERE idxfti @@ 'test'::tsquery;
             intindex |   strtopic
            ----------+---------------
                    1 | Testing Topic
            (1 row)

The only result that matched was the row with a topic "Testing Topic". Notice that the word I search for was all lowercase. Let's see what happens when I query for uppercase "Test".

            SELECT intindex, strtopic FROM tblmessages
                                      WHERE idxfti @@ 'Test'::tsquery;
             intindex | strtopic
            ----------+----------
            (0 rows)

We get zero rows returned. The reason is because when the text was inserted, it was morphed to my default configuration (because of the call to to_tsvector in the UPDATE statement). If there was no morphing done, and the tsvector field(s) contained the word 'Text', a match would have been found.

Most likely the best way to query the field is to use the to_tsquery function on the right hand side of the @@ operator like this:

            SELECT intindex, strtopic FROM tblmessages
                   WHERE idxfti @@ to_tsquery('default', 'Test | Zeppelin');
             intindex |      strtopic
            ----------+--------------------
                    1 | Testing Topic
                    7 | Classic Rock Bands
            (2 rows)

That query searched for all instances of "Test" OR "Zeppelin". It returned two rows: the "Testing Topic" row, and the "Classic Rock Bands" row. The to_tsquery function performed the correct morphology upon the parameters, and searched the tsvector field appropriately.

The last example here relates to searching for a phrase, for example "minority report". This poses a problem with regard to tsearch2, as it doesn't index phrases, only words. But there is a way around which doesn't appear to have a significant impact on query time, and that is to use a query such as the following:

            SELECT intindex, strTopic FROM tblmessages
                    WHERE idxfti @@ to_tsquery('default', 'gettysburg & address')
                    AND strMessage ~* '.*men are created equal.*';
             intindex |           strtopic
            ----------+------------------------------
                    6 | Gettysburg address quotation
            (1 row)
            SELECT intindex, strTopic FROM tblmessages
                    WHERE idxfti @@ to_tsquery('default', 'gettysburg & address')
                    AND strMessage ~* '.*something that does not exist.*';
             intindex | strtopic
            ----------+----------
            (0 rows)

Of course if your indexing both strTopic and strMessage, and want to search for this phrase on both, then you will have to get out the brackets and extend this query a little more.

### TSEARCH2 CONFIGURATION

Some words such as "and", "the", and "who" are automatically not indexed, since they belong to a pre-existing dictionary of "Stop Words" which tsearch2 does not perform indexing on. If someone needs to search for "The Who" in your database, they are going to have a tough time coming up with any results, since both are ignored in the indexes. But there is a solution.

Lets say we want to add a word into the stop word list for english stemming. We could edit the file :'/usr/local/pgsql/share/english.stop' and add a word to the list. I edited mine to exclude my name from indexing:

        - Edit /usr/local/pgsql/share/english.stop
        - Add 'andy' to the list
        - Save the file.

When you connect to the database, the dict_init procedure is run during initialization. And in my configuration it will read the stop words from the file I just edited. If you were connected to the DB while editing the stop words, you will need to end the current session and re-connect. When you re-connect to the database, 'andy' is no longer indexed:

            SELECT to_tsvector('default', 'Andy');
             to_tsvector
            ------------
            (1 row)

Originally I would get the result :

            SELECT to_tsvector('default', 'Andy');
             to_tsvector
            ------------
             'andi':1
            (1 row)

But since I added it as a stop word, it would be ingnored on the indexing. The stop word added was used in the dictionary "en_stem". If I were to use a different configuration such as 'simple', the results would be different. There are no stop words for the simple dictionary. It will just convert to lower case, and index every unique word.

            SELECT to_tsvector('simple', 'Andy andy The the in out');
                         to_tsvector
            -------------------------------------
             'in':5 'out':6 'the':3,4 'andy':1,2
            (1 row)

All this talk about which configuration to use is leading us into the actual configuration of tsearch2. In the examples in this document the configuration has always been specified when using the tsearch2 functions:

            SELECT to_tsvector('default', 'Testing the default config');
            SELECT to_tsvector('simple', 'Example of simple Config');

The pg_ts_cfg table holds each configuration you can use with the tsearch2 functions. As you can see the ts_name column contains both the 'default' configurations based on the 'C' locale. And the 'simple' configuration which is not based on any locale.

            SELECT * from pg_ts_cfg;
                 ts_name     | prs_name |    locale
            -----------------+----------+--------------
             default         | default  | C
             default_russian | default  | ru_RU.KOI8-R
             simple          | default  |
            (3 rows)

Each row in the pg_ts_cfg table contains the name of the tsearch2 configuration, the name of the parser to use, and the locale mapped to the configuration. There is only one parser to choose from the table pg_ts_parser called 'default'. More parsers could be written, but for our needs we will use the default.

There are 3 configurations installed by tsearch2 initially. If your locale is set to 'en_US' for example (like my laptop), then as you can see there is currently no dictionary configured to use with that locale. You can either set up a new configuration or just use one that already exists. If I do not specify which configuration to use in the to_tsvector function, I receive the following error.

            SELECT to_tsvector('learning tsearch is like going to school');
            ERROR:  Can't find tsearch config by locale

We will create a new configuration for use with the server encoding 'en_US'. The first step is to add a new configuration into the pg_ts_cfg table. We will call the configuration 'default_english', with the default parser and use the locale 'en_US'.

            INSERT INTO pg_ts_cfg (ts_name, prs_name, locale)
                   VALUES ('default_english', 'default', 'en_US');

We have only declared that there is a configuration called 'default_english'. We need to set the configuration of how 'default_english' will work. The next step is creating a new dictionary to use. The configuration of the dictionary is completlely different in tsearch2. In the prior versions to make changes, you would have to re-compile your changes into the tsearch.so. All of the configuration has now been moved into the system tables created by executing the SQL code from tsearch2.sql

Lets take a first look at the pg_ts_dict table

            ftstest=# \d pg_ts_dict
                    Table "public.pg_ts_dict"
             Column      |  Type   | Modifiers
            -----------------+---------+-----------
             dict_name       | text    | not null
             dict_init       | oid     |
             dict_initoption | text    |
             dict_lexize     | oid     | not null
             dict_comment    | text    |
            Indexes: pg_ts_dict_idx unique btree (dict_name)

The dict_name column is the name of the dictionary, for example 'simple', 'en_stem' or 'ru_stem'. The dict_init column is an OID of a stored procedure to run for initialization of that dictionary, for example 'snb_en_init' or 'snb_ru_init'. The dict_init option is used for options passed to the init function for the stored procedure. In the cases of 'en_stem' or 'ru_stem' it is a path to a stopword file for that dictionary, for example '/usr/local/pgsql/share/english.stop'. This is however dictated by the dictionary. ISpell dictionaries may require different options. The dict_lemmatize column is another OID of a stored procedure to the function used to lemmitize, for example 'snb_lemmatize'. The dict_comment column is just a comment.

Next we will configure the use of a new dictionary based on ISpell. We will assume you have ISpell installed on you machine. (in /usr/local/lib)

There has been some confusion in the past as to which files are used from ISpell. ISpell operates using a hash file. This is a binary file created by the ISpell command line utility "buildhash". This utility accepts a file containing the words from the dictionary, and the affixes file and the output is the hash file. The default installation of ISPell installs the english hash file english.hash, which is the exact same file as american.hash. ISpell uses this as the fallback dictionary to use.

This hash file is not what tsearch2 requires as the ISpell interface. The file(s) needed are those used to create the hash. Tsearch uses the dictionary words for morphology, so the listing is needed not spellchecking. Regardless, these files are included in the ISpell sources, and you can use them to integrate into tsearch2. This is not complicated, but is not very obvious to begin with. The tsearch2 ISpell interface needs only the listing of dictionary words, it will parse and load those words, and use the ISpell dictionary for lexem processing.

I found the ISPell make system to be very finicky. Their documentation actually states this to be the case. So I just did things the command line way. In the ISpell source tree under langauges/english there are several files in this directory. For a complete description, please read the ISpell README. Basically for the english dictionary there is the option to create the small, medium, large and extra large dictionaries. The medium dictionary is recommended. If the make system is configured correctly, it would build and install the english.has file from the medium size dictionary. Since we are only concerned with the dictionary word listing ... it can be created from the /languages/english directory with the following command:

       sort -u -t/ +0f -1 +0 -T /usr/tmp -o english.med english.0 english.1

This will create a file called english.med. You can copy this file to whever you like. I place mine in /usr/local/lib so it coincides with the ISpell hash files. You can now add the tsearch2 configuration entry for the ISpell english dictionary. We will also continue to use the english word stop file that was installed for the en_stem dictionary. You could use a different one if you like. The ISpell configuration is based on the "ispell_template" dictionary installed by default with tsearch2. We will use the OIDs to the stored procedures from the row where the dict_name = 'ispell_template'.

            INSERT INTO pg_ts_dict
                   (SELECT 'en_ispell',
                           dict_init,
                           'DictFile="/usr/local/lib/english.med",'
                           'AffFile="/usr/local/lib/english.aff",'
                           'StopFile="/usr/local/pgsql/share/english.stop"',
                           dict_lexize
                    FROM pg_ts_dict
                    WHERE dict_name = 'ispell_template');

Now that we have a dictionary we can specify it's use in a query to get a lexem. For this we will use the lexize function. The lexize function takes the name of the dictionary to use as an argument. Just as the other tsearch2 functions operate.

       SELECT lexize('en_ispell', 'program');
              lexize
            -----------
             {program}
            (1 row)

If you wanted to always use the ISpell english dictionary you have installed, you can configure tsearch2 to always use a specific dictionary.

      SELCECT set_curdict('en_ispell');

Lexize is meant to turn a word into a lexem. It is possible to receive more than one lexem returned for a single word.

     SELECT lexize('en_ispell', 'conditionally');
               lexize
            -----------------------------
             {conditionally,conditional}
            (1 row)

The lexize function is not meant to take a full string as an argument to return lexems for. If you passed in an entire sentence, it attempts to find that entire sentence in the dictionary. SInce the dictionary contains only words, you will receive an empty result set back.

          SELECT lexize('en_ispell', 'This is a senctece to lexize');
             lexize
            --------

            (1 row)

    If you parse a lexem from a word not in the dictionary, then you will receive an empty result. This makes sense because the word "tsearch" is not int the english dictionary. You can create your own additions to the dictionary if you like. This may be useful for scientific or technical glossaries that need to be indexed. SELECT lexize('en_ispell', 'tsearch'); lexize -------- (1 row)

This is not to say that tsearch will be ignored when adding text information to the the tsvector index column. This will be explained in greater detail with the table pg_ts_cfgmap.

Next we need to set up the configuration for mapping the dictionay use to the lexxem parsings. This will be done by altering the pg_ts_cfgmap table. We will insert several rows, specifying to using the new dictionary we installed and configured for use within tsearch2. There are several type of lexims we would be concerned with forcing the use of the ISpell dictionary.

            INSERT INTO pg_ts_cfgmap (ts_name, tok_alias, dict_name)
                   VALUES ('default_english', 'lhword', '{en_ispell,en_stem}');
            INSERT INTO pg_ts_cfgmap (ts_name, tok_alias, dict_name)
                   VALUES ('default_english', 'lpart_hword', '{en_ispell,en_stem}');
            INSERT INTO pg_ts_cfgmap (ts_name, tok_alias, dict_name)
                   VALUES ('default_english', 'lword', '{en_ispell,en_stem}');

We have just inserted 3 records to the configuration mapping, specifying that the lexem types for "lhword, lpart_hword and lword" are to be stemmed using the 'en_ispell' dictionary we added into pg_ts_dict, when using the configuration ' default_english' which we added to pg_ts_cfg.

There are several other lexem types used that we do not need to specify as using the ISpell dictionary. We can simply insert values using the 'simple' stemming process dictionary.

            INSERT INTO pg_ts_cfgmap
                   VALUES ('default_english', 'url', '{simple}');
            INSERT INTO pg_ts_cfgmap
                   VALUES ('default_english', 'host', '{simple}');
            INSERT INTO pg_ts_cfgmap
                   VALUES ('default_english', 'sfloat', '{simple}');
            INSERT INTO pg_ts_cfgmap
                   VALUES ('default_english', 'uri', '{simple}');
            INSERT INTO pg_ts_cfgmap
                   VALUES ('default_english', 'int', '{simple}');
            INSERT INTO pg_ts_cfgmap
                   VALUES ('default_english', 'float', '{simple}');
            INSERT INTO pg_ts_cfgmap
                   VALUES ('default_english', 'email', '{simple}');
            INSERT INTO pg_ts_cfgmap
                   VALUES ('default_english', 'word', '{simple}');
            INSERT INTO pg_ts_cfgmap
                   VALUES ('default_english', 'hword', '{simple}');
            INSERT INTO pg_ts_cfgmap
                   VALUES ('default_english', 'nlword', '{simple}');
            INSERT INTO pg_ts_cfgmap
                   VALUES ('default_english', 'nlpart_hword', '{simple}');
            INSERT INTO pg_ts_cfgmap
                   VALUES ('default_english', 'part_hword', '{simple}');
            INSERT INTO pg_ts_cfgmap
                   VALUES ('default_english', 'nlhword', '{simple}');
            INSERT INTO pg_ts_cfgmap
                   VALUES ('default_english', 'file', '{simple}');
            INSERT INTO pg_ts_cfgmap
                   VALUES ('default_english', 'uint', '{simple}');
            INSERT INTO pg_ts_cfgmap
                   VALUES ('default_english', 'version', '{simple}');

Our addition of a configuration for 'default_english' is now complete. We have successfully created a new tsearch2 configuration. At the same time we have also set the new configuration to be our default for en_US locale.

            SELECT to_tsvector('default_english',
                               'learning tsearch is like going to school');
                               to_tsvector
            --------------------------------------------------
             'go':5 'like':4 'learn':1 'school':7 'tsearch':2
            SELECT to_tsvector('learning tsearch is like going to school');
                                to_tsvector
            --------------------------------------------------
             'go':5 'like':4 'learn':1 'school':7 'tsearch':2
            (1 row)

Notice here that words like "tsearch" are still parsed and indexed in the tsvector column. There is a lexem returned for the word becuase in the configuration mapping table, we specify words to be used from the 'en_ispell' dictionary first, but as a fallback to use the 'en_stem' dictionary. Therefore a lexem is not returned from en_ispell, but is returned from en_stem, and added to the tsvector.

     SELECT to_tsvector('learning tsearch is like going to computer school');
                                    to_tsvector
            ---------------------------------------------------------------------------
             'go':5 'like':4 'learn':1 'school':8 'compute':7 'tsearch':2 'computer':7
            (1 row)

Notice in this last example I added the word "computer" to the text to be converted into a tsvector. Because we have setup our default configuration to use the ISpell english dictionary, the words are lexized, and computer returns 2 lexems at the same position. 'compute':7 and 'computer':7 are now both indexed for the word computer.

You can create additional dictionarynlists, or use the extra large dictionary from ISpell. You can read through the ISpell documents, and source tree to make modifications as you see fit.

In the case that you already have a configuration set for the locale, and you are changing it to your new dictionary configuration. You will have to set the old locale to NULL. If we are using the 'C' locale then we would do this:

            UPDATE pg_ts_cfg SET locale=NULL WHERE locale = 'C';

That about wraps up the configuration of tsearch2. There is much more you can do with the tables provided. This was just an introduction to get things working rather quickly.

### ADDING NEW DICTIONARIES TO TSEARCH2

To aid in the addition of new dictionaries to the tsearch2 module you can use another additional module in combination with tsearch2. The gendict module is included into tsearch2 distribution and is available from gendict/ subdirectory.

I will not go into detail about installation and instructions on how to use gendict to it's fullest extent right now. You can read the README.gendict ... it has all of the instructions and information you will need.

### BACKING UP AND RESTORING DATABASES THAT FEATURE TSEARCH2

Believe it or not, this isn't as straight forward as it should be, and you will have problems trying to backup and restore any database which uses tsearch2 unless you take the steps shown below. And before you ask using pg_dumpall will result in failure every time. These took a lot of trial and error to get working, but the process as laid down below has been used a dozen times now in live production environments so it should work fine.

HOWEVER never rely on anyone elses instructions to backup and restore a database system, always develop and understand your own methodology, and test it numerous times before you need to do it for real.

To Backup a PostgreSQL database that uses the tsearch2 module:

1) Backup any global database objects such as users and groups (this step is usually only necessary when you will be restoring to a virgin system)

            pg_dumpall -g > GLOBALobjects.sql

2) Backup the full database schema using pg_dump

            pg_dump -s DATABASE > DATABASEschema.sql

3) Backup the full database using pg_dump

            pg_dump -Fc DATABASE > DATABASEdata.tar

To Restore a PostgreSQL database that uses the tsearch2 module:

1) Create the blank database

            createdb DATABASE

2) Restore any global database objects such as users and groups (this step is usually only necessary when you will be restoring to a virgin system)

            psql DATABASE < GLOBALobjects.sql

3) Create the tsearch2 objects, functions and operators

            psql DATABASE < tsearch2.sql

4) Edit the backed up database schema and delete all SQL commands which create tsearch2 related functions, operators and data types, BUT NOT fields in table definitions that specify tsvector types. If your not sure what these are, they are the ones listed in tsearch2.sql. Then restore the edited schema to the database

            psql DATABASE < DATABASEschema.sql

5) Restore the data for the database

            pg_restore -N -a -d DATABASE DATABASEdata.tar

If you get any errors in step 4, it will most likely be because you forgot to remove an object that was created in tsearch2.sql. Any errors in step 5 will mean the database schema was probably restored wrongly.