$PostgreSQL: pgsql/src/backend/access/gin/README,v 1.7 2010/02/08 04:33:52 tgl Exp $
Gin for PostgreSQL
==================
Gin was sponsored by jfg://networks (http://www.jfg-networks.com/)
Gin stands for Generalized Inverted Index and should be considered as a genie,
not a drink.
Generalized means that the index does not know which operation it accelerates.
It instead works with custom strategies, defined for specific data types (read
"Index Method Strategies" in the PostgreSQL documentation). In that sense, Gin
is similar to GiST and differs from btree indices, which have predefined,
comparison-based operations.
An inverted index is an index structure storing a set of (key, posting list)
pairs, where 'posting list' is a set of documents in which the key occurs.
(A text document would usually contain many keys.) The primary goal of
Gin indices is support for highly scalable, full-text search in PostgreSQL.
Gin consists of a B-tree index constructed over entries (ET, entries tree),
where each entry is an element of the indexed value (element of array, lexeme
for tsvector) and where each tuple in a leaf page is either a pointer to a
B-tree over item pointers (PT, posting tree), or a list of item pointers
(PL, posting list) if the tuple is small enough.
Note: There is no delete operation for ET. The reason for this is that in
our experience, the set of distinct words in a large corpus changes very
rarely. This greatly simplifies the code and concurrency algorithms.
Gin comes with built-in support for one-dimensional arrays (eg. integer[],
text[]), but no support for NULL elements. The following operations are
available:
* contains: value_array @> query_array
* overlaps: value_array && query_array
* is contained by: value_array <@ query_array
Synopsis
--------
=# create index txt_idx on aa using gin(a);
Features
--------
* Concurrency
* Write-Ahead Logging (WAL). (Recoverability from crashes.)
* User-defined opclasses. (The scheme is similar to GiST.)
* Optimized index creation (Makes use of maintenance_work_mem to accumulate
postings in memory.)
* Text search support via an opclass
* Soft upper limit on the returned results set using a GUC variable:
gin_fuzzy_search_limit
Gin Fuzzy Limit
---------------
There are often situations when a full-text search returns a very large set of
results. Since reading tuples from the disk and sorting them could take a
lot of time, this is unacceptable for production. (Note that the search
itself is very fast.)
Such queries usually contain very frequent lexemes, so the results are not
very helpful. To facilitate execution of such queries Gin has a configurable
soft upper limit on the size of the returned set, determined by the
'gin_fuzzy_search_limit' GUC variable. This is set to 0 by default (no
limit).
If a non-zero search limit is set, then the returned set is a subset of the
whole result set, chosen at random.
"Soft" means that the actual number of returned results could slightly differ
from the specified limit, depending on the query and the quality of the
system's random number generator.
From experience, a value of 'gin_fuzzy_search_limit' in the thousands
(eg. 5000-20000) works well. This means that 'gin_fuzzy_search_limit' will
have no effect for queries returning a result set with less tuples than this
number.
Limitations
-----------
* No support for multicolumn indices
* Gin doesn't uses scan->kill_prior_tuple & scan->ignore_killed_tuples
* Gin searches entries only by equality matching. This may be improved in
future.
* Gin doesn't support full scans of indices.
* Gin doesn't index NULL values.
Open Items
----------
We appreciate any comments, help and suggestions.
* Teach optimizer/executor that GIN is intrinsically clustered. i.e., it
always returns ItemPointer in ascending order.
* Tweak gincostestimate.
TODO
----
Nearest future:
* Opclasses for all types (no programming, just many catalog changes).
Distant future:
* Replace B-tree of entries to something like GiST
* Add multicolumn support
* Optimize insert operations (background index insertion)
Authors
-------
All work was done by Teodor Sigaev (teodor@sigaev.ru) and Oleg Bartunov
(oleg@sai.msu.su).
65e806cba1