flaxkv 0.1.7

A high-performance dictionary database.

🗲 FlaxKV

A high-performance dictionary database.

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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.

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Key Features

• 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 LMDB 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.

TODO

• Client-Server Architecture
• Benchmark

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Quick Start

Installation

pip install flaxkv

Usage

from flaxkv import dictdb
import numpy as np

db = dictdb('./test_db')
# or run server `flaxkv run --port 8000`, then:
# db = dictdb('http://localhost:8000', remote=True, db_name='test_db', rebuild=False)

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.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))

Tips

• flaxkv provides performance close to native dictionary (in-memory) access as a persistent database! (There should be a benchmark here)
• 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.
• Since flaxkv saves data by buffered writing, this feature of delayed writing may not write data to the disk in time in some scenarios (such as in Jupyter), in this case, you can use db.write_immediately() to immediately trigger a write operation.

Benchmark

todo

Use Cases

• 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.

Citation

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}
}

Contributions

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

License

FlaxKV is licensed under the Apache-2.0 License.