booklet 0.0.13

A python key-value file database

Introduction

Booklet is a pure python key-value file database. It allows for multiple serializers for both the keys and values. The API is designed to use all of the same python dictionary methods python programmers are used to in addition to the typical dbm methods.

Installation

Install via pip:

pip install booklet

Or conda:

conda install -c mullenkamp booklet

I’ll probably put it on conda-forge once I feel like it’s up to an appropriate standard…

Serialization

Both the keys and values stored in Booklet must be bytes when written to disk. This is the default when “open” is called. Booklet allows for various serializers to be used for taking input keys and values and converting them to bytes. There are many in-built serializers. Check the booklet.available_serializers list for what’s available. Some serializers require additional packages to be installed (e.g. orjson, zstd, etc). If you want to serialize to json, then it is highly recommended to use orjson as it is substantially faster than the standard json python module. If in-built serializers are assigned at initial file creation, then they will be saved on future reading and writing on the same file (i.e. they don’t need to be passed after the first time). The user can also pass custom serializers to the key_serializer and value_serializer parameters. These must have “dumps” and “loads” static methods. This allows the user to chain a serializer and a compressor together if desired. Custom serializers must be passed for writing and reading as they are not stored in the booklet file.

import booklet

print(booklet.available_serializers)

Usage

The docstrings have a lot of info about the classes and methods. Files should be opened with the booklet.open function. Read the docstrings of the open function for more details.

Write data using the context manager

import booklet

with booklet.open('test.blt', 'n', value_serializer='pickle', key_serializer='str') as db:
  db['test_key'] = ['one', 2, 'three', 4]

Read data using the context manager

with booklet.open('test.blt', 'r') as db:
  test_data = db['test_key']

Notice that you don’t need to pass serializer parameters when reading (and additional writing) when in-built serializers are used. Booklet stores this info on the initial file creation.

In most cases, the user should use python’s context manager “with” when reading and writing data. This will ensure data is properly written and (optionally) locks are released on the file. If the context manager is not used, then the user must be sure to run the db.sync() at the end of a series of writes to ensure the data has been fully written to disk.

Write data without using the context manager

import booklet

db = booklet.open('test.blt', 'n', value_serializer='pickle', key_serializer='str')

db['test_key'] = ['one', 2, 'three', 4]
db['2nd_test_key'] = ['five', 6, 'seven', 8]

db.sync()
db.close()

Read data without using the context manager

db = booklet.open('test.blt', 'r')

test_data1 = db['test_key']
test_data2 = db['2nd_test_key']

db.close()

Custom serializers

import orjson

class Orjson:
  def dumps(obj):
      return orjson.dumps(obj, option=orjson.OPT_NON_STR_KEYS | orjson.OPT_OMIT_MICROSECONDS | orjson.OPT_SERIALIZE_NUMPY)
  def loads(obj):
      return orjson.loads(obj)

with booklet.open('test.blt', 'n', value_serializer=Orjson, key_serializer='str') as db:
  db['test_key'] = ['one', 2, 'three', 4]

The Orjson class is actually already built into the package. You can pass the string ‘orjson’ to either serializer parameters to use the above serializer. This is just an example of a serializer.

Here’s another example with compression.

import orjson
import zstandard as zstd

class OrjsonZstd:
  def dumps(obj):
      return zstd.compress(orjson.dumps(obj, option=orjson.OPT_NON_STR_KEYS | orjson.OPT_OMIT_MICROSECONDS | orjson.OPT_SERIALIZE_NUMPY))
  def loads(obj):
      return orjson.loads(zstd.decompress(obj))

with booklet.open('test.blt', 'n', value_serializer=OrjsonZstd, key_serializer='str') as db:
  db['big_test'] = list(range(1000000))

with booklet.open('test.blt', 'r', value_serializer=OrjsonZstd) as db:
  big_test_data = db['big_test']

The open flag follows the standard dbm options:

Value	Meaning
'r'	Open existing database for reading only (default)
'w'	Open existing database for reading and writing
'c'	Open database for reading and writing, creating it if it doesn’t exist
'n'	Always create a new, empty database, open for reading and writing

TODO

I need to write a lot more tests for the functionality. I also need to figure out why the prune function does not work…Currently, stale data cannot be removed from a book, but this will be possible in the future.

Benchmarks

From my initial tests, the performance is comporable to other very fast key-value databases (e.g. gdbm, lmdb). Proper benchmarks will be coming soon…