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Tsearch2 - full text search extension for PostgreSQL
[10][Online version] of this document is available
This module is sponsored by Delta-Soft Ltd., Moscow, Russia.
Notice: This version is fully incompatible with old tsearch (V1),
which was deprecated in 7.4 and obsoleted in 8.0.
The Tsearch2 contrib module contains an implementation of a new data
type tsvector - a searchable data type with indexed access. In a
nutshell, tsvector is a set of unique words along with their
positional information in the document, organized in a special
structure optimized for fast access and lookup. Actually, each word
entry, besides its position in the document, could have a weight
attribute, describing importance of this word (at a specific) position
in document. A set of bit-signatures of a fixed length, representing
tsvectors, are stored in a search tree (developed using PostgreSQL
GiST), which provides online update of full text index and fast query
lookup. The module provides indexed access methods, queries,
operations and supporting routines for the tsvector data type and easy
conversion of text data to tsvector. Table driven configuration allows
creation of custom configuration optimized for specific searches using
[1]Online version of this document is available
Tsearch2 - is the full text engine, fully integrated into PostgreSQL
RDBMS.
Main features
* Full online update
* Supports multiple table driven configurations
* flexible and rich linguistic support (dictionaries, stop words),
thesaurus
* full multibyte (UTF-8) support
* Sophisticated ranking functions with support of proximity and
structure information (rank, rank_cd)
* Index support (GiST and Gin) with concurrency and recovery support
* Rich query language with query rewriting support
* Headline support (text fragments with highlighted search terms)
* Ability to plug-in custom dictionaries and parsers
* Template generator for tsearch2 dictionaries with [2]snowball
stemmer support
* It is mature (5 years of development)
Tsearch2, in a nutshell, provides FTS operator (contains) for the new
data types, representing document (tsvector) and query (tsquery).
Table driven configuration allows creation of custom searches using
standard SQL commands.
Configuration allows you to:
* specify the type of lexemes to be indexed and the way they are
processed.
* specify dictionaries to be used along with stop words recognition.
* specify the parser used to process a document.
See [11]Documentation Roadmap for links to documentation.
tsvector is a searchable data type, representing document. It is a set
of unique words along with their positional information in the
document, organized in a special structure optimized for fast access
and lookup. Each entry could be labelled to reflect its importance in
document.
tsquery is a data type for textual queries with support of boolean
operators. It consists of lexemes (optionally labelled) with boolean
operators between.
Table driven configuration allows to specify:
* parser, which used to break document onto lexemes
* what lexemes to index and the way they are processed
* dictionaries to be used along with stop words recognition.
OpenFTS vs Tsearch2
OpenFTS is a middleware between application and database, so it uses
tsearch2 as a storage, while database engine is used as a query executor
(searching). Everything else (parsing of documents, query processing,
linguistics) carry outs on client side. That's why OpenFTS has its own
configuration table (fts_conf) and works with its own set of dictionaries.
OpenFTS is more flexible, because it could be used in multi-server
architecture with separated machines for repository of documents
(documents could be stored in file system), database and query engine.
[3]OpenFTS is a middleware between application and database. OpenFTS
uses tsearch2 as a storage and database engine as a query executor
(searching). Everything else, i.e. parsing of documents, query
processing, linguistics, carry outs on client side. That's why OpenFTS
has its own configuration table (fts_conf) and works with its own set
of dictionaries. OpenFTS is more flexible, because it could be used in
multi-server architecture with separate machines for repository of
documents (documents could be stored in filesystem), database and
query engine.
See [4]Documentation Roadmap for links to documentation.
Authors
* Oleg Bartunov <oleg@sai.msu.su>, Moscow, Moscow University, Russia
* Teodor Sigaev <teodor@sigaev.ru>, Moscow, Delta-Soft Ltd.,Russia
* Teodor Sigaev <teodor@sigaev.ru>, Moscow,Moscow University,Russia
Contributors
* Robert John Shepherd and Andrew J. Kopciuch submitted
"Introduction to tsearch" (Robert - tsearch v1, Andrew - tsearch
* Robert John Shepherd and Andrew J. Kopciuch submitted
"Introduction to tsearch" (Robert - tsearch v1, Andrew - tsearch
v2)
* Brandon Craig Rhodes wrote "Tsearch2 Guide" and "Tsearch2
* Brandon Craig Rhodes wrote "Tsearch2 Guide" and "Tsearch2
Reference" and proposed new naming convention for tsearch V2
Features Added with Tsearch2
* Relevance ranking of search results
* Table driven configuration
* Morphology support (ispell dictionaries, snowball stemmers)
* Headline support (text fragments with highlighted search terms)
* Ability to plug-in custom dictionaries and parsers
* Synonym dictionary
* Generator of templates for dictionaries (built-in snowball stemmer
support)
* Statistics of indexed words is available
Sponsors
* ABC Startsiden - compound words support
* University of Mannheim for UTF-8 support (in 8.2)
* jfg:networks ([5]http:www.jfg-networks.com/) for Gin - Generalized
Inverted index (in 8.2)
* Georgia Public Library Service and LibLime, Inc. for Thesaurus
dictionary
* PostGIS community - GiST Concurrency and Recovery
The authors are grateful to the Russian Foundation for Basic Research
and Delta-Soft Ltd., Moscow, Russia for support.
Limitations
* Lexeme should be not longer than 2048 bytes
* The number of lexemes is limited by 2^32. Note, that actual
capacity of tsvector is depends on whether positional information
is stored or not.
* tsvector - the size is limited by approximately 2^20 bytes.
* tsquery - the number of entries (lexemes and operations) < 32768
* Positional information
+ maximal position of lexeme < 2^14 (16384)
+ lexeme could have maximum 256 positions
* Length of lexeme < 2K
* Length of tsvector (lexemes + positions) < 1Mb
* The number of lexemes < 4^32
* 0< Positional information < 16383
* No more than 256 positions per lexeme
* The number of nodes ( lexemes + operations) in tsquery < 32768
References
* GiST development site -
[12]http://www.sai.msu.su/~megera/postgres/gist
* OpenFTS home page - [13]http://openfts.sourceforge.net/
[6]http://www.sai.msu.su/~megera/postgres/gist
* GiN development - [7]http://www.sigaev.ru/gin/
* OpenFTS home page - [8]http://openfts.sourceforge.net/
* Mailing list -
[14]http://sourceforge.net/mailarchive/forum.php?forum=openfts-gen
eral
[15]Documentation Roadmap
[9]http://sourceforge.net/mailarchive/forum.php?forum=openfts-gene
ral
Documentation Roadmap
* Several docs are available from docs/ subdirectory
@ -97,113 +108,103 @@ Documentation Roadmap
+ "Tsearch2 Guide" by Brandon Rhodes
+ "Tsearch2 Reference" by Brandon Rhodes
* Readme.gendict in gendict/ subdirectory
+ [16][Gendict tutorial]
Online version of documentation is always available from Tsearch V2
home page -
[17]http://www.sai.msu.su/~megera/postgres/gist/tsearch/V2/
+ Also, check [10]Gendict tutorial
* Check [11]tsearch2 Wiki pages for various documentation
Support
Authors urgently recommend people to use [18][openfts-general] or
[19][pgsql-general] mailing lists for questions and discussions.
Caution
Authors urgently recommend people to use [12]openfts-general or
[13]pgsql-general mailing lists for questions and discussions.
In spite of apparent easy full text searching with our tsearch module
(authors hope it's so), any serious search engine require profound
study of various aspects, such as stop words, dictionaries, special
parsers. Tsearch module was designed to facilitate both those cases.
Development History
Latest news
To the PostgreSQL 8.2 release we added:
* multibyte (UTF-8) support
* Thesaurus dictionary
* Query rewriting
* rank_cd relevation function now support different weights of
lexemes
* GiN support adds scalability of tsearch2
Pre-tsearch era
Development of OpenFTS began in 2000 after realizing that we
needed a search engine optimized for online updates and able to
access metadata from the database. This is essential for online
Development of OpenFTS began in 2000 after realizing that we
need a search engine optimized for online updates with access
to metadata from the database. This is essential for online
news agencies, web portals, digital libraries, etc. Most search
engines available utilize an inverted index which is very fast
for searching but very slow for online updates. Incremental
updates of an inverted index is a complex engineering task
while we needed something light, free and with the ability to
access metadata from the database. The last requirement is very
important because in a real life application a search engine
should always consult metadata ( topic, permissions, date
range, version, etc.). We extensively use PostgreSQL as a
database backend and have no intention to move from it, so the
problem was to find a data structure and a fast way to access
it. PostgreSQL has rather unique data type for storing sets
(think about words) - arrays, but lacks index access to them. A
document is parsed into lexemes, which are identified in
various ways (e.g. stemming, morphology, dictionary), and as a
result is reduced to an array of integer numbers. During our
research we found a paper of Joseph Hellerstein which
introduced an interesting data structure suitable for sets -
RD-tree (Russian Doll tree). It looked very attractive, but
implementing it in PostgreSQL seemed difficult because of our
ignorance of database internals. Further research lead us to
the idea to use GiST for implementing RD-tree, but at that time
the GiST code had for a long while remained untouched and
contained several bugs. After work on improving GiST for
version 7.0.3 of PostgreSQL was done, we were able to implement
RD-Tree and use it for index access to arrays of integers. This
implementation was ideally suited for small arrays and
eliminated complex joins, but was practically useless for
indexing large arrays. The next improvement came from an idea
to represent a document by a single bit-signature, a so-called
superimposed signature (see "Index Structures for Databases
Containing Data Items with Set-valued Attributes", 1997, Sven
Helmer for details). We developeded the contrib/intarray module
and used it for full text indexing.
engines available utilize an inverted index which is very fast
for searching but very slow for online updates. Incremental
updates of an inverted index is a complex engineering task
while we needed something light, free and with the ability to
access metadata from the database. The last requirement was
very important because in a real life application search engine
should always consult metadata ( topic, permissions, date
range, version, etc.). We extensively use PostgreSQL as a
database backend and have no intention to move from it, so the
problem was to find a data structure and a fast way to access
it. PostgreSQL has rather unique data type for storing sets
(think about words) - arrays, but lacks index access to them.
During our research we found a paper of Joseph Hellerstein, who
introduced an interesting data structure suitable for sets -
RD-tree (Russian Doll tree). Further research lead us to the
idea to use GiST for implementing RD-tree, but at that time the
GiST code was intouched for a long time and contained several
bugs. After work on improving GiST for version 7.0.3 of
PostgreSQL was done, we were able to implement RD-Tree and use
it for index access to arrays of integers. This implementation
was ideally suited for small arrays and eliminated complex
joins, but was practically useless for indexing large arrays.
The next improvement came from an idea to represent a document
by a single bit-signature, a so-called superimposed signature
(see "Index Structures for Databases Containing Data Items with
Set-valued Attributes", 1997, Sven Helmer for details). We
developeded the contrib/intarray module and used it for full
text indexing.
tsearch v1
It was inconvenient to use integer id's instead of words, so we
introduced a new data type called 'txtidx' - a searchable data
type (textual) with indexed access. This was a first step of
our work on an implementation of a built-in PostgreSQL full
introduced a new data type called 'txtidx' - a searchable data
type (textual) with indexed access. This was a first step of
our work on an implementation of a built-in PostgreSQL full
text search engine. Even though tsearch v1 had many features of
a search engine it lacked configuration support and relevance
ranking. People were encouraged to use OpenFTS, which provided
relevance ranking based on coordinate information and flexible
configuration. OpenFTS v.0.34 is the last version based on
a search engine it lacked configuration support and relevance
ranking. People were encouraged to use OpenFTS, which provided
relevance ranking based on positional information and flexible
configuration. OpenFTS v.0.34 is the last version based on
tsearch v1.
tsearch V2
People recognized tsearch as a powerful tool for full text
searching and insisted on adding ranking support, better
configurability, etc. We already thought about moving most of
the features of OpenFTS to tsearch, and in the early 2003 we
decided to work on a new version of tsearch - tsearch v2. We've
abandoned auxiliary index tables which were used by OpenFTS to
store coordinate information and modified the txtidx type to
store them internally. Also, we've added table-driven
configuration, support of ispell dictionaries, snowball
stemmers and the ability to specify which types of lexemes to
index. Also, it's now possible to generate headlines of
documents with highlighted search terms. These changes make
tsearch more user friendly and turn it into a really powerful
full text search engine. After announcing the alpha version, we
received a proposal from Brandon Rhodes to rename tsearch
functions to be more consistent. So, we have renamed txtidx
type to tsvector and other things as well.
To allow users of tsearch v1 smooth upgrade, we named the module as
tsearch2.
Future release of OpenFTS (v.0.35) will be based on tsearch2. Brave
people could download it from OpenFTS CVS (see link from [20][OpenFTS
page]
People recognized tsearch as a powerful tool for full text
searching and insisted on adding ranking support, better
configurability, etc. We already thought about moving most of
the features of OpenFTS to tsearch, and in the early 2003 we
decided to work on a new version of tsearch. We abandoned
auxiliary index tables which were used by OpenFTS to store
positional information and modified the txtidx type to store
them internally. We added table-driven configuration, support
of ispell dictionaries, snowball stemmers and the ability to
specify which types of lexemes to index. Now, it's possible to
generate headlines of documents with highlighted search terms.
These changes make tsearch more user friendly and turn it into
a really powerful full text search engine. Brandon Rhodes
proposed to rename tsearch functions for consistency and we
renamed txtidx type to tsvector and other things as well. To
allow users of tsearch v1 smooth upgrade, we named the module
as tsearch2. Since version 0.35 OpenFTS uses tsearch2.
References
10. http://www.sai.msu.su/~megera/postgres/gist/tsearch/V2/docs/Tsearch_V2_Readme.html
11. http://www.sai.msu.su/~megera/oddmuse/index.cgi/Tsearch_V2_Readme#Documentation_Roadmap
12. http://www.sai.msu.su/~megera/postgres/gist
13. http://openfts.sourceforge.net/
14. http://sourceforge.net/mailarchive/forum.php?forum=openfts-general
15. http://www.sai.msu.su/~megera/oddmuse/index.cgi?action=anchor&id=Documentation_Roadmap#Documentation_Roadmap
16. http://www.sai.msu.su/~megera/oddmuse/index.cgi?Gendict
17. http://www.sai.msu.su/~megera/postgres/gist/tsearch/V2/
18. http://sourceforge.net/mailarchive/forum.php?forum=openfts-general
19. http://archives.postgresql.org/pgsql-general/
20. http://openfts.sourceforge.net/
1. http://www.sai.msu.su/~megera/postgres/gist/tsearch/V2/docs/Tsearch_V2_Readme.html
2. http://snowball.tartarus.org/
3. http://openfts.sourceforge.net/
4. file://localhost/u/megera/WWW/postgres/gist/tsearch/V2/docs/Tsearch_V2_Readme82.html#dm
5. http:www.jfg-networks.com/
6. http://www.sai.msu.su/~megera/postgres/gist
7. http://www.sigaev.ru/gin/
8. http://openfts.sourceforge.net/
9. http://sourceforge.net/mailarchive/forum.php?forum=openfts-general
10. http://www.sai.msu.su/~megera/wiki/Gendict
11. http://www.sai.msu.su/~megera/wiki/Tsearch2
12. http://sourceforge.net/mailarchive/forum.php?forum=openfts-general
13. http://archives.postgresql.org/pgsql-general/