https://remusao.github.io/posts/python-dbm-module.html

../

TIL--Python has a built-in persistent key-value store

+- 2018-06-10 * Reading time: ~2 minutes

Python is, in a lot of ways, a very rich language. After years of
using it, I still regularly discover new parts of the ecosystem, even
in the standard library. In particular, there are a few modules which
are not very well-known, but can be very useful in some situations.
Today I discovered dbm a persistent key/value store:

Quick start:

import dbm

with dbm.open('my_store', 'c') as db:
  db['key'] = 'value'
  print(db.keys()) # ['key']
  print(db['key']) # 'value'
  print('key' in db) # True

It behaves a lot like a dict except:

  * It persists its values on disk
  * You can only use str or bytes as key and values

The performance is also slower than a dictionary, but faster than
sqlite3.

The benchmark consists in performing 10k writes and 10k random reads:

from random import random
import time

operations = 10000
writes = [str(i) for i in range(operations)]
reads = [str(int(random() * operations)) for _ in range(operations)]

# Create some records
for i in writes:
    db[i] = 'x'

# Read values in random order
for i in reads:
    x = db[i]

Here are the results:

  * dict - total: 0.002 seconds, 0.23398 ms/record x 1
  * dbm - total: 0.054 seconds, 5.35984 ms/record x 27
  * sqlite3 in-memory: total: 2.468 seconds, 246.84346 ms/record x
    1234
  * sqlite3 file: total 42.407 seconds, 4240.69593 ms/record x 21207

Why is sqlite3 so slow? Well, the benchmark is probably not
representative of the typical workload for sqlite (lots of individual
insertions and selections). If we perform the same operations using
executemany and one select of all the keys at once, we get:

  * :memory: - total: 0.038 seconds, 3.81415 ms/record x 19
  * file - total 0.071 seconds, 7.07369 ms/record x 35

It's much better, but still not as fast as dbm (when we persist to a
file). So, if you have a workload where keys and values are added and
retrieved very often, are always str or bytes and need to be
persisted on disk, dbm is a serious contender!

The code used to benchmark can be found here: dbm_bench.py

Leave a comment on GitHub

  *  
  *  
  *  
  *  
  *