A high-performance dictionary database.

The flaxkv provides an interface very similar to a dictionary for interacting with high-performance key-value databases. More importantly, as a persistent database, it offers performance close to that of native dictionaries (in-memory access).
You can use it just like a Python dictionary without having to worry about blocking your user process when operating the database at any time.

• Always Up-to-date, Never Blocking: It was designed from the ground up to ensure that no write operations block the user process, while users can always read the most recently written data.

• Ease of Use: Interacting with the database feels just like using a Python dictionary! You don't even have to worry about resource release.

• Buffered Writing: Data is buffered and scheduled for write to the database, reducing the overhead of frequent database writes.

• High-Performance Database Backend: Uses the high-performance key-value database LevelDB as its default backend.

• Atomic Operations: Ensures that write operations are atomic, safeguarding data integrity.

• Thread-Safety: Employs only necessary locks to ensure safe concurrent access while balancing performance.

pip install flaxkv 
# Install with server version: pip install flaxkv[server]

from flaxkv import FlaxKV
import numpy as np
import pandas as pd

db = FlaxKV('test_db')
"""
Or start as a server
>>> flaxkv run --port 8000

Client call:
db = FlaxKV('test_db', root_path_or_url='https://localhost:8000')
"""

db[1] = 1
db[1.1] = 1 / 3
db['key'] = 'value'
db['a dict'] = {'a': 1, 'b': [1, 2, 3]}
db['a list'] = [1, 2, 3, {'a': 1}]
db[(1, 2, 3)] = [1, 2, 3]
db['numpy array'] = np.random.randn(100, 100)
db['df'] = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})

db.setdefault('key', 'value_2')
assert db['key'] == 'value'

db.update({"key1": "value1", "key2": "value2"})

assert 'key2' in db

db.pop("key1")
assert 'key1' not in db

for key, value in db.items():
    print(key, value)

print(len(db))

• flaxkv provides performance close to native dictionary (in-memory) access as a persistent database! (See benchmark below)
• You may have noticed that in the previous example code, db.close() was not used to release resources! Because all this will be automatically handled by flaxkv. Of course, you can also manually call db.close() to immediately release resources.

Test Content: Write and read traversal for N numpy array vectors (each vector is 1000-dimensional).

Execute the test:

cd benchmark/
pytest -s -v run.py

• Key-Value Structure: Used to save simple key-value structure data.
• High-Frequency Writing: Very suitable for scenarios that require high-frequency insertion/update of data.
• Machine Learning: flaxkv is very suitable for saving various large datasets of embeddings, images, texts, and other key-value structures in machine learning.

• In the current version, due to the delayed writing feature, in a multi-process environment, one process cannot read the data written by another process in real-time (usually delayed by a few seconds). If immediate writing is desired, the .write_immediately() method must be called. This limitation does not exist in a single-process environment.
• By default, the value does not support the Tuple, Set types. If these types are forcibly set, they will be deserialized into a List.

If FlaxKV has been helpful to your research, please cite:

@misc{flaxkv,
    title={FlaxKV: An Easy-to-use and High Performance Key-Value Database Solution},
    author={K.Y},
    howpublished = {\url{https://github.com/KenyonY/flaxkv}},
    year={2023}
}

Feel free to make contributions to this module by submitting pull requests or raising issues in the repository.

FlaxKV is licensed under the Apache-2.0 License.