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This is for Debian woody!

Below are some benchmarking figures obtained while reading and writing
to a file with three tables, each table containing 10000 records. For
reference, the same tests have been repeated using the shelve module
that comes with Python. The tests were conducted on a platform with a
2 GHz AMD Athlon chip, an IDE disk at 4600 rpm, and 256 MB of RAM.

Version 0.2

            |  medium size records      |   small size records
            |     (47 Bytes)            |     (16 Bytes)
            +---------------------------+------------------------------
            |      rows/s      filesize |     rows/s       filesize
            |  write    read            | write     read
------------+---------------------------+------------------------------
 no compress|                           |                         
     record | 24400    39000   1184 KB  | 32600     52600   506 KB
      tupla | 17100    81100   1184 KB  | 66666    107142   506 KB
------------+---------------------------+------------------------------
  compress  |                           |                         
     record | 22200    37500    494 KB  | 31900     51700    94 KB
      tupla | 16100    75000    494 KB  | 63900    107142    94 KB
------------+---------------------------+------------------------------
   Shelve   | 25800    14400   2500 KB  | 68200     17000   921 KB

New version (15-Jan-2003)


                               PyTables pre-0.3

Rec length  |      rows/s     |       KB/s      | rows  | filesz | memory |
            |  write    read  |  write    read  |       |   (MB) |   (MB) |
------------+-----------------+-----------------+-------+--------+--------+
     16 B   | 31000   166600  |    480    2600  |  3.e4 |    0.49|    6.5 |
------------+-----------------+-----------------+-------+--------+--------+
     56 B   | 17300   136000  |    942    7460  |  3.e4 |    1.7 |    7.2 |
------------+-----------------+-----------------+-------+--------+--------+
     56 B*  |  1560   136000  |     85    7560  |  3.e4 |    1.7 |    7.2 |
------------+-----------------+-----------------+-------+--------+--------+
     64 B*  |  1540   130000  |     96    8152  |  3.e4 |    1.9 |    7.2 |
------------+-----------------+-----------------+-------+--------+--------+
    550 B*  |   879    81100  |    472   43500  |  3.e4 |   19   |    7.2 |
------------+-----------------+-----------------+-------+--------+--------+
    550 B** | 12000   103000  |   6440   55400  |  3.e5 |   168  |    7.2 |
------------+-----------------+-----------------+-------+--------+--------+
    550 B** | 15500    81100  |   8350   43500  |  3.e4 |   19   |    7.2 |
------------+-----------------+-----------------+-------+--------+--------+
    550 B**c|   909     1100  |    490    1081  |  3.e4 |    0.76|    8.5 |
------------+-----------------+-----------------+-------+--------+--------+
    550 B***|  3600    81100  |   1950   43500  |  3.e4 |   19   |    7.2 |
------------+-----------------+-----------------+-------+--------+--------+

* These are figures obtained with a numarray as part of the record 
** The numarray record fields are not set in each iteration
*** Some numarray elements of a record field are changed on each iteration
**c Like ** but with compression (level 1)


New version (10-March-2003)

                               PyTables pre-0.4

Rec     |      rows/s     |       KB/s      | rows  | filesz | memory |%CP|%CP
length  |  write    read  |  write    read  |       |   (MB) |   (MB) |(w)|(r)
--------+-----------------+-----------------+-------+--------+--------+---+----
 16 B   |434000   469000  |   6800    7300  |  3.e4 |    0.49|    6.5 |100|100
--------+-----------------+-----------------+-------+--------+--------+---+----
 16 Bc  |326000   435000  |   5100    6800  |  3.e4 |    0.12|    6.5 |100|100
--------+-----------------+-----------------+-------+--------+--------+---+----
 16 B   |663000   728000  |  10400   11400  |  3.e5 |    4.7 |    7.0 | 99|100
--------+-----------------+-----------------+-------+--------+--------+---+----
 16 B   |679000   797000  |  10600   12500  |  3.e6 |   46.0 |   10.0 | 98| 98
--------+-----------------+-----------------+-------+--------+--------+---+----
 16 Bc  |452000   663000  |   7100   10400  |  3.e6 |    9.3 |   10.0 | 98| 98
--------+-----------------+-----------------+-------+--------+--------+---+----
 16 B   |576000   590000  |   9000    9200  |  3.e7 |  458.0 |   11.0 | 78| 76
--------+-----------------+-----------------+-------+--------+--------+---+----
 56 B   |  3050   380000  |    163   20700  |  3.e4 |    1.7 |    7.2 | 98|100
--------+-----------------+-----------------+-------+--------+--------+---+----
 56 B*  |194000   340000  |  10600   18600  |  3.e4 |    1.7 |    7.2 |100|100
--------+-----------------+-----------------+-------+--------+--------+---+----
 56 B*c |142000   306000  |   7800   16600  |  3.e4 |    0.3 |    7.2 |100|100
--------+-----------------+-----------------+-------+--------+--------+---+----
 56 B*  |273600   589000  |  14800   32214  |  3.e5 |   16.0 |    9.0 |100|100
--------+-----------------+-----------------+-------+--------+--------+---+----
 56 B*c |184000   425000  |  10070   23362  |  3.e5 |    2.7 |    9.7 |100|100
--------+-----------------+-----------------+-------+--------+--------+---+----
 56 B*  |203600   649000  |  11100   35500  |  3.e6 |  161.0 |   12.0 | 72| 99
--------+-----------------+-----------------+-------+--------+--------+---+----
 56 B*  |184000   229000  |  10000   12500  |  1.e7 |  534.0 |   17.0 | 56| 40
--------+-----------------+-----------------+-------+--------+--------+---+----
 56 B*np|184000   229000  |  10000   12500  |  1.e7 |  534.0 |   17.0 | 56| 40
--------+-----------------+-----------------+-------+--------+--------+---+----
550 B   |  2230   143000  |   1195   76600  |  3.e4 |   19   |    9.4 |100|100
--------+-----------------+-----------------+-------+--------+--------+---+----
550 B*  | 76000   250000  |  40900  134000  |  3.e4 |   19   |    9.4 |100|100
--------+-----------------+-----------------+-------+--------+--------+---+----
550 B*c | 13900    30000  |   7400   16100  |  3.e4 |    0.7 |   10.0 | 99| 99
--------+-----------------+-----------------+-------+--------+--------+---+----
550 B*  | 25400   325000  |  13600  174000  |  3.e5 |  167   |   11.0 | 71| 96
--------+-----------------+-----------------+-------+--------+--------+---+----
550 B*  | 18700    28000  |  10000   15100  |  6.e5 |  322   |   13.0 | 76|  9
--------+-----------------+-----------------+-------+--------+--------+---+----
550 B*c |  7300    21000  |   3900   11300  |  6.e5 |   11   |   17.0 | 98| 99
--------+-----------------+-----------------+-------+--------+--------+---+----

* These are figures obtained with a numarray as part of the record 
** The numarray record fields are not set in each iteration
c With compression (level 1)
np No psyco optimizations

			         Shelve
				 
Rec length  |      rows/s     |       KB/s      | rows  | filesz | memory |
            |  write    read  |  write    read  |       |   (MB) |   (MB) |
------------+-----------------+-----------------+-------+--------+--------+
     16 B   | 68200    17000  |   1070     266  |  3.e4 |    0.94|    7.2 |
------------+-----------------+-----------------+-------+--------+--------+
     56 B   | 25000    14400  |   1367     784  |  3.e4 |    2.5 |   10.6 |
------------+-----------------+-----------------+-------+--------+--------+
     56 B*  |  2980     2710  |    162     148  |  3.e4 |    7.3 |   33   |
------------+-----------------+-----------------+-------+--------+--------+
     64 B*  |  2900     2700  |    182     168  |  3.e4 |    7.5 |   33   |
------------+-----------------+-----------------+-------+--------+--------+
    550 B*  |  1090     1310  |    590     710  |  3.e4 |   58   |  122   |
------------+-----------------+-----------------+-------+--------+--------+
    550 B** | 16000    14900  |   2400    1200  |  3.e4 |    2.4 |   10.6 |
------------+-----------------+-----------------+-------+--------+--------+
    550 B***| 28000    11900  |   2400    1100  |  3.e4 |    2.5 |   10.6 |
------------+-----------------+-----------------+-------+--------+--------+
				 
* These are figures obtained with a numarray as part of the record 
** The nuamrray records are not set on each iteration
*** Some numarray elements of a record field are changed on each iteration


		  Python cPickle & bsddb3 RECNO with variable length
				 
Rec     |     Krows/s     |       MB/s      | Krows | filesz | memory |%CP|%CP
length  |  write    read  |  write    read  |       |   (MB) |   (MB) |(w)|(r)
--------+-----------------+-----------------+-------+--------+--------+---+----
 16 B   |  23.0     4.3   |  0.65     0.12  |   30  |    2.3 |    6.0 |100|100
--------+-----------------+-----------------+-------+--------+--------+---+----
 16 B   |  22.0     4.3   |  0.60     0.12  |  300  |   24   |   25.0 |100|100
--------+-----------------+-----------------+-------+--------+--------+---+----
 56 B   |  12.3     2.0   |  0.68     0.11  |   30  |    5.8 |    6.2 |100|100
--------+-----------------+-----------------+-------+--------+--------+---+----
 56 B   |   8.8     2.0   |  0.44     0.11  |  300  |   61   |    6.2 |100|100
--------+-----------------+-----------------+-------+--------+--------+---+----


		  Python struct & bsddb3 RECNO with fixed length
				 
Rec     |     Krows/s     |       MB/s      | Krows | filesz | memory |%CP|%CP
length  |  write    read  |  write    read  |       |   (MB) |   (MB) |(w)|(r)
--------+-----------------+-----------------+-------+--------+--------+---+----
 16 B   |   61      71    |   1.6     1.9   |   30  |    1.0 |    5.0 |100|100
--------+-----------------+-----------------+-------+--------+--------+---+----
 16 B   |   56      65    |   1.5     1.8   |  300  |   10   |    5.8 |100|100
--------+-----------------+-----------------+-------+--------+--------+---+----
 16 B   |   51      61    |   1.4     1.6   | 3000  |  100   |    6.1 |100|100
--------+-----------------+-----------------+-------+--------+--------+---+----
 56 B   |   51      52    |   2.7     2.8   |   30  |    1.8 |    5.8 |100|100
--------+-----------------+-----------------+-------+--------+--------+---+----
 56 B   |   18      50    |   1.0     2.7   |  300  |   18   |    6.2 |100|100
--------+-----------------+-----------------+-------+--------+--------+---+----
 56 B   |   16      48    |   0.9     2.6   | 1000  |   61   |    6.5 |100|100
--------+-----------------+-----------------+-------+--------+--------+---+----


			         PySqlLite
				 
Rec     |      rows/s     |       KB/s      | rows  | filesz | memory |%CP|%CP
length  |  write    read  |  write    read  |       |   (MB) |   (MB) |(w)|(r)
--------+-----------------+-----------------+-------+--------+--------+---+----
 16 B   |  4290  1400000  |   200    48000  |  3.e4 |    1.4 |    5.0 |100|100
--------+-----------------+-----------------+-------+--------+--------+---+----
 16 B   |  3660  1030000  |   182    51000  |  3.e5 |   15   |    5.0 |100|100
--------+-----------------+-----------------+-------+--------+--------+---+----
 16 B   |  3580   230000  |   192    12380  |  6.e6 |  322   |    5.0 |100| 25
--------+-----------------+-----------------+-------+--------+--------+---+----
 56 B   |  2990   882000  |   250    76000  |  3.e4 |    2.6 |    5.0 |100|100
--------+-----------------+-----------------+-------+--------+--------+---+----
 56 B   |  2900   857000  |   270    80000  |  3.e5 |   28   |    5.0 |100|100
--------+-----------------+-----------------+-------+--------+--------+---+----
 56 B   |  2900   120000  |   302    13100  |  3.e6 |  314   |    5.0 |100| 11
--------+-----------------+-----------------+-------+--------+--------+---+----