torden/py-mdbm

Just! Python interface for Y! MDBM, Python MDBM Wapper

Py-mdbm

• Py-mdbm is a Python binds to Yahoo! MDBM C API.
• MDBM is a super-fast memory-mapped key/value store.
• MDBM is an ndbm work-alike hashed database library based on sdbm which is based on Per-Aake Larson’s Dynamic Hashing algorithms.
• MDBM is a high-performance, memory-mapped hash database similar to the homegrown libhash.
• The records stored in a mdbm database may have keys and values of arbitrary and variable lengths.

Build Stats	Py-mdbm ver.	License	Y! mdbm ver.

Table of Contents

• API
• Install
• Example
• Benchmark
• Link

API

Currently Supported APIs

the following is list of support api on now.

Group	API
File Management	mdbm_open, mdbm_close, mdbm_sync, mdbm_fsync, mdbm_close_fd, mdbm_replace_db, mdbm_replace_file, mdbm_dup_handle, mdbm_pre_split, mdbm_fcopy
Configuration	mdbm_get_version, mdbm_get_size, mdbm_get_page_size, mdbm_get_limit_size, mdbm_get_hash, mdbm_get_alignment, mdbm_set_alignment, mdbm_setspillsize, mdbm_limit_dir_size, mdbm_get_magic_number, mdbm_limit_size_v3, mdbm_set_window_size
Record Access	mdbm_fetch, mdbm_delete, mdbm_store, mdbm_fetch_r, mdbm_fetch_dup_r, mdbm_delete_r, mdbm_store_r, mdbm_fetch_info
Record Iteration	mdbm_first, mdbm_next, mdbm_firstkey, mdbm_nextkey, mdbm_first_r, mdbm_next_r, mdbm_firstkey_r, mdbm_nextkey_r, mdbm_iterate
Locking	mdbm_islocked, mdbm_isowned, mdbm_lock, mdbm_unlock, mdbm_lock_reset, mdbm_delete_lockfiles, mdbm_get_lockmode, mdbm_trylock, mdbm_plock, mdbm_punlock, mdbm_tryplock, mdbm_lock_shared, mdbm_trylock_shared, mdbm_lock_smart, mdbm_trylock_smart, mdbm_unlock_smart
Data Management	mdbm_compress_tree, mdbm_truncate, mdbm_purge, mdbm_clean, mdbm_prune, mdbm_set_cleanfunc
Data Integrity	mdbm_check, mdbm_chk_all_page, mdbm_chk_page, mdbm_protect
Data Display	mdbm_dump_all_page, mdbm_dump_page
Statistics	mdbm_count_records, mdbm_count_pages, mdbm_get_stats, mdbm_get_db_info, mdbm_get_stat_counter, mdbm_get_stat_time, mdbm_reset_stat_operations, mdbm_enable_stat_operations, mdbm_set_stat_time_func, mdbm_get_db_stats, mdbm_get_window_stats, mdbm_get_stat_name, mdbm_set_stats_func, mdbm_chunk_iterate
Cache and Backing Store	mdbm_set_cachemode, mdbm_get_cachemode, mdbm_get_cachemode_name, mdbm_set_backingstore
Import and Export	mdbm_cdbdump_to_file, mdbm_cdbdump_trailer_and_close, mdbm_cdbdump_add_record, mdbm_dbdump_to_file, mdbm_dbdump_trailer_and_close, mdbm_dbdump_add_record, mdbm_dbdump_export_header, mdbm_dbdump_import_header, mdbm_dbdump_import, mdbm_cdbdump_import
Miscellaneous	mdbm_preload, mdbm_get_errno, mdbm_get_page, mdbm_lock_pages, mdbm_unlock_pages, mdbm_get_hash_value, mdbm_select_log_plugin, mdbm_set_log_filename

Deprecated APIs

API	STATUS	COMMENT
mdbm_save	DEPRECATED	mdbm_save is only supported for V2 MDBMs.
mdbm_restore	DEPRECATED	mdbm_restore is only supported for V2 MDBMs.
mdbm_sethash	DEPRECATED	Legacy version of mdbm_set_hash() This function has inconsistent naming, an error return value. It will be removed in a future version.

Only a V2 implementation

API	STATUS	COMMENT
mdbm_stat_all_page	V3 not supported	There is only a V2 implementation. V3 not currently supported.
mdbm_stat_header	V3 not supported	There is only a V2 implementation. V3 not currently supported.

Has not been implemented

API	STATUS	COMMENT
dbm_chk_error	Not Implemented	This has not been implemented

Support two compatibility version

Python

Version	Support	Test
Python 2.6.x ~ 2.7.x	yes	always
Python 3.0.x ~ 3.x.x	yes	always
PyPy	yes	always
PyPy3	yes	always

MDBM

branch or release ver.	Support	Test	Comment
master	yes	always
4.x	yes	always

Install

MDBM

• MDBM Packages

py-mdbm (use pip)

pip install py-mdbm

py-mdbm (use source)

Download

git clone https://github.com/torden/py-mdbm

Build and Test

cd py-mdbm
CMD_PYTHON=`which python` make

Check

$ python
>>> import mdbm
>>> help(mdbm)

Benchmark

cd py-mdbm
`which pip` install -r for-benchmark-py26_or_higher-requirements.txt
CMD_PYTHON=`which python` CMD_PYTEST=`which pytest` make benchmark

Example

See the Source Code for more details

The following is Sample codes for a first look at py-mdbm

Creating and populating a database

Python 2 or higher

import mdbm
import random

print("[*] Creating and populating a database")

path = "/tmp/test1.mdbm"
flags = mdbm.MDBM_O_RDWR
flags = flags | mdbm.MDBM_O_CREAT
flags = flags | mdbm.MDBM_LARGE_OBJECTS
flags = flags | mdbm.MDBM_ANY_LOCKS
flags = flags | mdbm.MDBM_O_TRUNC
mode = 0o644  # means 0644

dbm = mdbm.open(path, flags, mode)
for i in range(0, 65535):
    k = str(i)
    v = str(random.randrange(0, 65535))

    rv = dbm.store(k, v, mdbm.MDBM_INSERT)
    if not rv:
        print("[-] failed to data store to ", path)
        break

print("[*] count of records : %d" % dbm.count_records())

dbm.close()

print("done")

Python 3 or higher

# encoding: utf-8
import mdbm
import random

print("[*] Creating and populating a database")

path = "/tmp/test1-byte.mdbm"
flags = mdbm.MDBM_O_RDWR
flags = flags | mdbm.MDBM_O_CREAT
flags = flags | mdbm.MDBM_LARGE_OBJECTS
flags = flags | mdbm.MDBM_ANY_LOCKS
flags = flags | mdbm.MDBM_O_TRUNC
mode = 0o644  # means 0644

dbm = mdbm.open(path, flags, mode)

print("|--------|-------|")
print("|  key   |  val  |")
print("|--------|-------|")

# byte
for i in range(0, 10):
    k = bytes(str(i), 'utf-8')
    v = bytes(str(random.randrange(0, 65535)), 'utf-8')

    print("|%08s|%08s|" % (k, v))
    rv = dbm.store(k, v, mdbm.MDBM_INSERT|mdbm.MDBM_CACHE_MODIFY)
    if not rv:
        print("[-] failed to data store to ", path)
        break

# string
for i in range(10, 20):
    k = str(i)
    v = str(random.randrange(0, 65535))

    print("|%08s|%08s|" % (k, v))


    rv = dbm.store(k, v, mdbm.MDBM_INSERT|mdbm.MDBM_CACHE_MODIFY)
    if not rv:
        print("[-] failed to data store to ", path)
        break


print("|--------|--------|")
print("[*] count of records : %d" % dbm.count_records())
print("\n")

dbm.close()

Fetching records in-place

import mdbm
import random

print("[*] Fetching records in-place")

path = "/tmp/test1.mdbm"
flags = mdbm.MDBM_O_RDWR
mode = 0o644  # means 0644

dbm = mdbm.open(path, flags, mode)
dbm.preload()

print("|-------|-------|")
print("|  key  |  val  |")
print("|-------|-------|")

for i in range(0, 10):

    k = str(random.randrange(0, 65534))
    orgval = dbm.fetch(k)
    if not orgval:
        print("[-] failed to fetch value of %s in mdbm" % k)
        break

    print("|%07s|%07s|" % (k, orgval))

print("|-------|-------|")
print("[*] count of records : %d" % dbm.count_records())

dbm.close()

print("done")

Fetching and updating records in-place

import mdbm
import random

print("[*] Fetching and updating records in-place")

path = "/tmp/test1.mdbm"
flags = mdbm.MDBM_O_RDWR
mode = 0o644  # means 0644

dbm = mdbm.open(path, flags, mode)
for i in range(0, 65535):
    k = str(i)
    v = str(random.randrange(0, 65535))

    orgval = dbm.fetch(k)
    if not orgval:
        print("[-] failed to fetch value of %s in mdbm" % k)
        break

    print("[=] key(%s) : replace val(%s) to '%s' : " % (k, orgval, v)),

    rv = dbm.store(k, v, mdbm.MDBM_REPLACE)
    if not rv:
        print("FAIL")
        break
    print("DONE")

print("[*] count of records : %d" % dbm.count_records())

dbm.close()

print("done")

Deleting records in-place

import mdbm
import random

print("[*] Deleting records in-place")

path = "/tmp/test1.mdbm"
flags = mdbm.MDBM_O_RDWR
mode = 0o644  # means 0644

dbm = mdbm.open(path, flags, mode)

for i in range(0, 10):

    k = str(random.randrange(0, 65534))

    rv = dbm.delete(k)
    if not rv:
        print("[-] failed to delete an record, key=%s" % k)

    v = dbm.fetch(k)
    if v:
        print("[-] failed to delete an record, key=%s, val=%s" % (k,v))
        break

print("[*] count of records : %d" % dbm.count_records())

dbm.close()

print("done")

Iterating over all records

import mdbm
import random

print("[*] Iterating over all records")

path = "/tmp/test1.mdbm"
flags = mdbm.MDBM_O_RDWR
mode = 0o644  # means 0644

dbm = mdbm.open(path, flags, mode)

print("|-------|-------|")
print("|  key  |  val  |")
print("|-------|-------|")

kv = dbm.first()

print("|%07s|%07s|" % kv)

while kv:

    print("|%07s|%07s|" % kv)

    kv = dbm.next()

print("|-------|-------|")
print("[*] count of records : %d" % dbm.count_records())

dbm.close()

print("done")

Iterating over all keys

import mdbm
import random

print("[*] Iterating over all records")

path = "/tmp/test1.mdbm"
flags = mdbm.MDBM_O_RDWR
mode = 0o644  # means 0644

dbm = mdbm.open(path, flags, mode)

print("|-------|")
print("|  key  |")
print("|-------|")

k = dbm.firstkey()

print("|%07s|" % k)

while k:

    print("|%07s|" % k)

    k = dbm.nextkey()

print("|-------|")
print("[*] count of records : %d" % dbm.count_records())

dbm.close()

print("done")

Iteration over all value by key

import mdbm
import random

print("[*] Creating and populating a database")

path = "/tmp/test_py_dup.mdbm"
flags = mdbm.MDBM_O_RDWR
flags = flags | mdbm.MDBM_O_CREAT
flags = flags | mdbm.MDBM_LARGE_OBJECTS
flags = flags | mdbm.MDBM_ANY_LOCKS
flags = flags | mdbm.MDBM_O_TRUNC
mode = 0o644  # means 0644

with mdbm.open(path, flags, mode) as dbm:

    for k in range(0, 100):
        key = str(k)

        for i in range(1, 12):
            val = str(123 * i)

            rv = dbm.store(key, val, mdbm.MDBM_INSERT_DUP)
            if not rv:
                print("[-] failed to data store to ", path)
                break

print("[*] Loop through DB, looking at records with the same key.")
with mdbm.open(path, mdbm.MDBM_O_RDONLY, mode) as dbm:

    print("[*] count of records : %d" % dbm.count_records())
    print("|-------|-------|")
    print("|  key  |  val  |")
    print("|-------|-------|")

    k = str(random.randrange(0, 99))

    empty_iter = dbm.init_iter()
    info = dbm.fetch_dup_r(k, empty_iter)
    while info:

        print("|%07s|%07s|" % (k, info['val']))
        info = dbm.fetch_dup_r(k, info['iter'])

print("|-------|-------|")

print("done")

Benchmark

The following is results of Py-mdbm vs AnyDBM vs SQLite3 vs Kyotocabinet benchmarks for simple data storing and random fetching in them.

• See the Source Code for more details
• See the Glossary for read result

Spec

Host

Type	Spec
CPU	Inte i-7
RAM	DDR4 32G
HDD	Nvme M.2 SSD

VM

Type	Spec
Machine	VM(VirtualBox)
OS	CentOS 7 64bit
CPU	2 vCore
RAM	8G
AnyDBM	Berkeley DB (Hash, version 9, native byte-order) format
Mdbm	893f7a8 on 26 Jul, MDBM V3 format
SQLite	V3
Kyotocabinet	1.2.76, kch

Command

CMD_PYTHON=`which python` CMD_PYTEST=`which pytest` make benchmark

File Size

Count of Records	Type	File Name	Size
10,000	SQLite3	test_py_benchmark_10000.db	300K
	AnyDBM	test_py_benchmark_10000.dbm	348K
	Kyotocabinet KCH	test_py_benchmark_10000.kch	6.3M
	MDBM	test_py_benchmark_10000.mdbm	260K
	MDBM(TSC)	test_py_benchmark_tsc_10000.mdbm	260K
100,000	SQLite3	test_py_benchmark_100000.db	3.3M
	AnyDBM	test_py_benchmark_100000.dbm	2.5M
	Kyotocabinet KCH	test_py_benchmark_100000.kch	9.1M
	MDBM	test_py_benchmark_100000.mdbm	4.0M
	MDBM(TSC)	test_py_benchmark_tsc_100000.mdbm	4.0M
1,000,000	SQLite3	test_py_benchmark_1000000.db	35M
	AnyDBM	test_py_benchmark_1000000.dbm	39M
	Kyotocabinet KCH	test_py_benchmark_1000000.kch	37M
	MDBM	test_py_benchmark_1000000.mdbm	32M
	MDBM(TSC)	test_py_benchmark_tsc_1000000.mdbm	32M

10,000 INSERTs

platform linux2 -- Python 2.7.14, pytest-3.3.2, py-1.5.2, pluggy-0.6.0
benchmark: 3.1.1 (defaults: timer=time.time disable_gc=False min_rounds=5 min_time=0.000005 max_time=1.0 calibration_precision=10 warmup=False warmup_iterations=100000)
rootdir: /root/PERSONAL/py-mdbm, inifile:
plugins: benchmark-3.1.1
collected 31 items

------------------------------------------------------------------------------------------- benchmark: 5 tests ------------------------------------------------------------------------------------------
Name (time in ms)                          Min                 Max                Mean            StdDev              Median               IQR            Outliers      OPS            Rounds  Iterations
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_mdbm_store_tsc_10000              42.7790 (1.0)       46.4041 (1.0)       44.4735 (1.0)      0.8599 (1.0)       44.7228 (1.01)     1.0532 (1.0)           5;0  22.4853 (1.0)          23           1
test_mdbm_store_10000                  43.0260 (1.01)      55.0859 (1.19)      45.1026 (1.01)     2.8206 (3.28)      44.1189 (1.0)      1.9995 (1.90)          3;2  22.1716 (0.99)         23           1
test_kyotocabinet_kch_store_10000      64.2769 (1.50)      72.2461 (1.56)      66.6182 (1.50)     2.1470 (2.50)      66.5540 (1.51)     2.4997 (2.37)          6;1  15.0109 (0.67)         16           1
test_sqlite3_store_10000               71.1770 (1.66)      89.0980 (1.92)      74.6003 (1.68)     4.5800 (5.33)      73.3149 (1.66)     2.8142 (2.67)          1;1  13.4048 (0.60)         13           1
test_anydbm_store_10000               129.4661 (3.03)     132.9770 (2.87)     131.7690 (2.96)     1.3268 (1.54)     132.4065 (3.00)     2.1240 (2.02)          1;0   7.5890 (0.34)          8           1
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

100,000 INSERTs

------------------------------------------------------------------------------------------------ benchmark: 5 tests -----------------------------------------------------------------------------------------------
Name (time in ms)                             Min                   Max                  Mean             StdDev                Median                IQR            Outliers     OPS            Rounds  Iterations
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_mdbm_store_100000                   432.5280 (1.0)        444.3109 (1.0)        440.1428 (1.0)       5.1283 (1.0)        443.0151 (1.0)       7.8554 (1.46)          1;0  2.2720 (1.0)           5           1
test_mdbm_store_tsc_100000               443.6021 (1.03)       457.2010 (1.03)       450.7210 (1.02)      6.5694 (1.28)       453.4068 (1.02)     12.3150 (2.28)          2;0  2.2187 (0.98)          5           1
test_kyotocabinet_kch_store_100000       553.1771 (1.28)       572.2950 (1.29)       559.4640 (1.27)      7.3967 (1.44)       557.5171 (1.26)      5.3908 (1.0)           1;1  1.7874 (0.79)          5           1
test_sqlite3_store_100000                668.3731 (1.55)       690.7680 (1.55)       676.8432 (1.54)     10.4372 (2.04)       670.3589 (1.51)     17.5762 (3.26)          1;0  1.4774 (0.65)          5           1
test_anydbm_store_100000               1,746.3379 (4.04)     1,778.0671 (4.00)     1,759.8858 (4.00)     12.6857 (2.47)     1,761.1270 (3.98)     19.0974 (3.54)          2;0  0.5682 (0.25)          5           1
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

1,000,000 INSERTs

----------------------------------------------------------------------------------------- benchmark: 5 tests -----------------------------------------------------------------------------------------
Name (time in s)                            Min                Max               Mean            StdDev             Median               IQR            Outliers     OPS            Rounds  Iterations
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_mdbm_store_1000000                  4.4507 (1.0)       4.5549 (1.0)       4.5087 (1.0)      0.0386 (1.41)      4.5170 (1.00)     0.0471 (1.0)           2;0  0.2218 (1.0)           5           1
test_mdbm_store_tsc_1000000              4.4964 (1.01)      4.5557 (1.00)      4.5252 (1.00)     0.0275 (1.0)       4.5133 (1.0)      0.0494 (1.05)          3;0  0.2210 (1.00)          5           1
test_kyotocabinet_kch_store_1000000      5.5518 (1.25)      7.3104 (1.60)      5.9554 (1.32)     0.7585 (27.62)     5.6386 (1.25)     0.4548 (9.65)          1;1  0.1679 (0.76)          5           1
test_sqlite3_store_1000000               6.9506 (1.56)      7.1580 (1.57)      7.0168 (1.56)     0.0811 (2.95)      6.9938 (1.55)     0.0623 (1.32)          1;1  0.1425 (0.64)          5           1
test_anydbm_store_1000000               18.8494 (4.24)     19.3685 (4.25)     19.1384 (4.24)     0.1884 (6.86)     19.1481 (4.24)     0.1982 (4.21)          2;0  0.0523 (0.24)          5           1
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

10,000 Random Key SELECTs

----------------------------------------------------------------------------------------------- benchmark: 6 tests -----------------------------------------------------------------------------------------------
Name (time in ms)                                 Min                 Max                Mean             StdDev              Median                IQR            Outliers      OPS            Rounds  Iterations
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_mdbm_random_fetch_10000                  33.6039 (1.0)       37.1680 (1.02)      35.4372 (1.0)       0.8726 (1.63)      35.3181 (1.0)       1.0861 (1.73)          9;0  28.2189 (1.0)          29           1
test_mdbm_preload_random_fetch_tsc_10000      34.1651 (1.02)      36.5930 (1.0)       35.5276 (1.00)      0.5728 (1.07)      35.6691 (1.01)      0.6691 (1.06)          8;0  28.1471 (1.00)         29           1
test_mdbm_preload_random_fetch_10000          34.8370 (1.04)      37.1509 (1.02)      35.6486 (1.01)      0.5368 (1.0)       35.6290 (1.01)      0.6291 (1.0)           8;1  28.0516 (0.99)         27           1
test_kyotocabinet_random_fetch_10000          50.1349 (1.49)     315.4690 (8.62)      66.3761 (1.87)     60.3302 (112.39)    52.3400 (1.48)      1.3785 (2.19)          1;1  15.0657 (0.53)         19           1
test_anydbm_random_fetch_10000                98.3920 (2.93)     127.4319 (3.48)     103.2393 (2.91)      8.6436 (16.10)    101.2516 (2.87)      3.1178 (4.96)          1;1   9.6862 (0.34)         10           1
test_sqlite3_random_fetch_10000              179.9428 (5.35)     264.3309 (7.22)     198.3913 (5.60)     32.8237 (61.15)    183.5115 (5.20)     14.0412 (22.32)         1;1   5.0405 (0.18)          6           1
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

100,000 Random Key SELECTs

-------------------------------------------------------------------------------------------------- benchmark: 5 tests --------------------------------------------------------------------------------------------------
Name (time in ms)                                    Min                   Max                  Mean            StdDev                Median               IQR            Outliers     OPS            Rounds  Iterations
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_mdbm_preload_random_fetch_tsc_100000       351.3479 (1.0)        362.6180 (1.02)       358.0612 (1.01)     4.9707 (3.89)       360.7321 (1.02)     8.1980 (3.88)          1;0  2.7928 (0.99)          5           1
test_mdbm_preload_random_fetch_100000           352.9408 (1.00)       360.9550 (1.01)       356.8196 (1.01)     3.2021 (2.51)       357.4481 (1.01)     5.0185 (2.38)          2;0  2.8025 (0.99)          5           1
test_mdbm_random_fetch_100000                   353.4501 (1.01)       356.4832 (1.0)        354.6917 (1.0)      1.2767 (1.0)        354.3482 (1.0)      2.1121 (1.0)           1;0  2.8193 (1.0)           5           1
test_kyotocabinet_random_fetch_100000           513.2129 (1.46)       516.0379 (1.45)       514.8367 (1.45)     1.3007 (1.02)       515.3730 (1.45)     2.3472 (1.11)          1;0  1.9424 (0.69)          5           1
test_anydbm_random_fetch_100000               1,196.3558 (3.41)     1,217.2129 (3.41)     1,207.2943 (3.40)     7.5601 (5.92)     1,206.6510 (3.41)     8.3598 (3.96)          2;0  0.8283 (0.29)          5           1
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

1,000,000 Random Key SELECTs

--------------------------------------------------------------------------------------------- benchmark: 5 tests --------------------------------------------------------------------------------------------
Name (time in s)                                   Min                Max               Mean            StdDev             Median               IQR            Outliers     OPS            Rounds  Iterations
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_mdbm_preload_random_fetch_tsc_1000000      3.6708 (1.0)       3.7161 (1.0)       3.7020 (1.0)      0.0194 (1.29)      3.7138 (1.00)     0.0255 (1.71)          1;0  0.2701 (1.0)           5           1
test_mdbm_preload_random_fetch_1000000          3.6781 (1.00)      3.7315 (1.00)      3.7045 (1.00)     0.0212 (1.41)      3.7021 (1.0)      0.0336 (2.25)          2;0  0.2699 (1.00)          5           1
test_mdbm_random_fetch_1000000                  3.6957 (1.01)      3.7336 (1.00)      3.7079 (1.00)     0.0150 (1.0)       3.7054 (1.00)     0.0149 (1.0)           1;0  0.2697 (1.00)          5           1
test_kyotocabinet_random_fetch_1000000          5.2549 (1.43)      5.2865 (1.42)      5.2677 (1.42)     0.0151 (1.01)      5.2599 (1.42)     0.0273 (1.82)          1;0  0.1898 (0.70)          5           1
test_anydbm_random_fetch_1000000               12.3323 (3.36)     12.4784 (3.36)     12.4044 (3.35)     0.0586 (3.90)     12.3911 (3.35)     0.0927 (6.20)          2;0  0.0806 (0.30)          5           1
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Link

• Yahoo! MDBM
• MDBM::Concept
• MDBM::Build
• MDBM::Document
• MDBM::FAQ
• DBM
• MDBM::Macro(const)
• MDBM Packages
• Go-mdbm
• PHP-mdbm
• Py-mdbm in Pypi

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Please feel free. I hope it is helpful for you