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 from
our experience, a set of unique words over a large collection change 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.)
  * Tsearch2 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 of 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.

Gin Interface
-------------

Opclass interface pseudocode. An example for a Gin opclass can be found in
ginarayproc.c.

Datum* extractValue(Datum inputValue, uint32* nentries)

    Returns an array of Datum of entries of the value to be indexed.  nentries 
    should contain the number of returned entries.
    
int compareEntry(Datum a, Datum b)

    Compares two entries (not the indexing values)
    
Datum* extractQuery(Datum query, uint32* nentries, StrategyNumber n)

    Returns an array of Datum of entries of the query to be executed.
    n contains the strategy number of the operation.
    
bool consistent(bool[] check, StrategyNumber n, Datum query)

    The size of the check array is the same as sizeof of the array returned by
    extractQuery. Each element of the check array is true if the indexed value
    has a corresponding entry in the query.  i.e. if (check[i] == TRUE) then 
    the i-th entry of the query is present in the indexed value.  The Function
    should return true if the indexed value matches by StrategyNumber and
    the query.

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.
  * GIN stores several ItemPointer to heap tuple, so VACUUM FULL produces
    this warning message:

     WARNING:  index "idx" contains 88395 row versions, but table contains 
     				51812 row versions
     HINT:  Rebuild the index with REINDEX.
	**** Workaround added

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).
