This is a R-Tree implementation using GiST.
Code (for PG95) are taken from http://s2k-ftp.cs.berkeley.edu:8000/gist/pggist/
and changed according to new version of GiST (7.1 and above)

All work was done by Teodor Sigaev (teodor@stack.net) and Oleg Bartunov
(oleg@sai.msu.su). See http://www.sai.msu.su/~megera/postgres/gist
for additional information.

CHANGES:
     Oct 10 MSD 2001

     1. Implemented new linear algorithm for picksplit
         ref. ( 'New Linear Node Splitting Algorithm for R-tree',
               C.H.Ang and T.C.Tan )

     Tue May 29 17:04:16 MSD 2001
     
     1. Small fixes in polygon code
        Thanks to Dave Blasby <dblasby@refractions.net>

     Mon May 28 19:42:14 MSD 2001

     1. Full implementation of R-tree using GiST - gist_box_ops,gist_poly_ops
     2. gist_poly_ops is lossy
     3. NULLs support
     4. works with multi-key GiST

NOTICE:
     This version will only work with PostgreSQL version 7.1 and above
     because of changes in the function call interface.

INSTALLATION:

  gmake
  gmake install
  -- load functions
  psql <database> < rtree_gist.sql

REGRESSION TEST:

   gmake installcheck

EXAMPLE USAGE:

   create table boxtmp (b box);
   -- create index
   create index bix on boxtmp using gist (b gist_box_ops);
   -- query
   select * from boxtmp where b && '(1000,1000,0,0)'::box;


BENCHMARKS:

 subdirectory bench contains benchmark suite.
 Prerequisities: perl, DBI, DBD:Pg, Time::HiRes

  cd ./bench
  1. createdb TEST
  2. psql TEST < ../box.sql
  3. ./create_test.pl | psql TEST 
     -- change $NUM - number of rows in test dataset
  4. ./bench.pl - perl script to benchmark queries. 
                  Run script without arguments to see available options.

     a)test without GiST index, using built-in R-Tree
       ./bench.pl -d TEST 
     b)test R-Tree using GiST index
       ./bench.pl -d TEST -g 


RESULTS:

1. One interesting thing is that insertion time for built-in R-Tree is 
   about 8 times more than ones for GiST implementation of R-Tree !!!
2. Postmaster requires much more memory for built-in R-Tree
3. Search time depends on dataset. In our case we got:
        +------------+-----------+--------------+
        |Number boxes|R-tree, sec|R-tree using  |
        |            |           |   GiST, sec  |
        +------------+-----------+--------------+
        |          10|      0.002|         0.002|
        +------------+-----------+--------------+
        |         100|      0.002|         0.002|
        +------------+-----------+--------------+
        |        1000|      0.002|         0.002|
        +------------+-----------+--------------+
        |       10000|      0.015|         0.025|
        +------------+-----------+--------------+
        |       20000|      0.029|         0.048|
        +------------+-----------+--------------+
        |       40000|      0.055|         0.092|
        +------------+-----------+--------------+
        |       80000|      0.113|         0.178|
        +------------+-----------+--------------+
        |      160000|      0.338|         0.337|
        +------------+-----------+--------------+
        |      320000|      0.674|         0.673|
        +------------+-----------+--------------+
