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.

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
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Please feel free. I hope it is helpful for you

Name		Name	Last commit message	Last commit date
Latest commit History 56 Commits
.github/workflows		.github/workflows
example		example
src		src
tests		tests
.gitignore		.gitignore
.travis.yml		.travis.yml
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
Makefile		Makefile
README.md		README.md
README.rst		README.rst
README.txt		README.txt
_config.yml		_config.yml
for-benchmark-py26_or_higher-requirements.txt		for-benchmark-py26_or_higher-requirements.txt
pyproject.toml		pyproject.toml
setup.py		setup.py
setup.py2X_37under		setup.py2X_37under

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Py-mdbm

Table of Contents

API

Currently Supported APIs

Deprecated APIs

Only a V2 implementation

Has not been implemented

Support two compatibility version

Python

MDBM

Install

MDBM

py-mdbm (use pip)

py-mdbm (use source)

Download

Build and Test

Check

Benchmark

Example

Creating and populating a database

Python 2 or higher

Python 3 or higher

Fetching records in-place

Fetching and updating records in-place

Deleting records in-place

Iterating over all records

Iterating over all keys

Iteration over all value by key

Benchmark

Spec

Host

VM

Command

File Size

10,000 INSERTs

100,000 INSERTs

1,000,000 INSERTs

10,000 Random Key SELECTs

100,000 Random Key SELECTs

1,000,000 Random Key SELECTs

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