Viperdb - A tiny log-structured key-value database written in pure Python

Overview

ViperDB 🐍

codecov

ViperDB is a lightweight embedded key-value store written in pure Python. It has been designed for being extremely simple while efficient.

Features

  • tiny: the main db file consists of just ~300 lines of code.
  • highly coverage: thanks to the small codebase, every single line of code is tested.
  • log-structured: ViperDB takes design concepts by log-structured databases such as Bitcask.
  • written in pure Python: no external dependency needed.

Installation

[email protected]:~$ pip3 install viperdb

Python version

ViperDB has been tested with Python 3.8.

Database layout

ViperDB simply consists of two files: a key log file and a value log file. The first is used to maintain information about values (e.g. offset, size, etc...) which are actually stored in the value log. This layout allows to speed-up db initialization, which consists in loading the pointers to the entire key-space from the key-file to a dictionary. For simplicity, the key file is treated as a text file, with each line containing a json-encoded entry. The value file is viewed as a raw sequence of bytes. Before being written to the value file, each value is encoded according to the following scheme: builtin types (except for the bytes type) are json-encoded, while user-defined classes are pickled.

To keep logic simple, no automatic compaction is performed in the background: unused space must be reclaimed explicitly through the reclaim function.

API usage

from viperdb import ViperDB

db = ViperDB('./db')
# db can be used as a simple dictionary
db['hello'] = 'ViperDB!'
assert db['hello'] == 'ViperDB'

del db['hello']
assert 'hello' not in db

db.reclaim() # call this method periodically to free unused space.
db.close() # flush any pending write and close the database.

Contribute

ViperDB is a very recent project. However, it is actively maintained to ensure high quality. If you find any bug, or have some suggestion, feel free to contribute by opening a new issue or making a pull request.

Owner
Passionate about algorithms and database design, with a strong preference for programming languages such as Java, Python and Golang.
CS506-Spring2022 - Code and Slides for Boston University CS 506

CS 506 - Computational Tools for Data Science Code, slides, and notes for Boston

Lance Galletti 17 May 06, 2022
Learning Visual Words for Weakly-Supervised Semantic Segmentation

[IJCAI 2021] Learning Visual Words for Weakly-Supervised Semantic Segmentation Implementation of IJCAI 2021 paper Learning Visual Words for Weakly-Sup

Lixiang Ru 24 Oct 05, 2022
OpenMMLab Detection Toolbox and Benchmark

MMDetection is an open source object detection toolbox based on PyTorch. It is a part of the OpenMMLab project.

OpenMMLab 22.5k Jan 05, 2023
Unofficial TensorFlow implementation of Protein Interface Prediction using Graph Convolutional Networks.

[TensorFlow] Protein Interface Prediction using Graph Convolutional Networks Unofficial TensorFlow implementation of Protein Interface Prediction usin

YeongHyeon Park 9 Oct 25, 2022
An OpenAI Gym environment for Super Mario Bros

gym-super-mario-bros An OpenAI Gym environment for Super Mario Bros. & Super Mario Bros. 2 (Lost Levels) on The Nintendo Entertainment System (NES) us

Andrew Stelmach 1 Jan 05, 2022
Deeplab-resnet-101 in Pytorch with Jaccard loss

Deeplab-resnet-101 Pytorch with Lovász hinge loss Train deeplab-resnet-101 with binary Jaccard loss surrogate, the Lovász hinge, as described in http:

Maxim Berman 95 Apr 15, 2022
EgGateWayGetShell py脚本

EgGateWayGetShell_py 免责声明 由于传播、利用此文所提供的信息而造成的任何直接或者间接的后果及损失,均由使用者本人负责,作者不为此承担任何责任。 使用 python3 eg.py urls.txt 目标 title:锐捷网络-EWEB网管系统 port:4430 漏洞成因 ?p

榆木 61 Nov 09, 2022
Brain Tumor Detection with Tensorflow Neural Networks.

Brain-Tumor-Detection A convolutional neural network model built with Tensorflow & Keras to detect brain tumor and its different variants. Data of the

404ErrorNotFound 5 Aug 23, 2022
A Tensorflow based library for Time Series Modelling with Gaussian Processes

Markovflow Documentation | Tutorials | API reference | Slack What does Markovflow do? Markovflow is a Python library for time-series analysis via prob

Secondmind Labs 24 Dec 12, 2022
MMGeneration is a powerful toolkit for generative models, based on PyTorch and MMCV.

Documentation: https://mmgeneration.readthedocs.io/ Introduction English | 简体中文 MMGeneration is a powerful toolkit for generative models, especially f

OpenMMLab 1.3k Dec 29, 2022
Aiming at the common training datsets split, spectrum preprocessing, wavelength select and calibration models algorithm involved in the spectral analysis process

Aiming at the common training datsets split, spectrum preprocessing, wavelength select and calibration models algorithm involved in the spectral analysis process, a complete algorithm library is esta

Fu Pengyou 50 Jan 07, 2023
A benchmark dataset for emulating atmospheric radiative transfer in weather and climate models with machine learning (NeurIPS 2021 Datasets and Benchmarks Track)

ClimART - A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models Official PyTorch Implementation Using deep le

21 Dec 31, 2022
Deep Learning Head Pose Estimation using PyTorch.

Hopenet is an accurate and easy to use head pose estimation network. Models have been trained on the 300W-LP dataset and have been tested on real data with good qualitative performance.

Nataniel Ruiz 1.3k Dec 26, 2022
A tutorial on DataFrames.jl prepared for JuliaCon2021

JuliaCon2021 DataFrames.jl Tutorial This is a tutorial on DataFrames.jl prepared for JuliaCon2021. A video recording of the tutorial is available here

Bogumił Kamiński 106 Jan 09, 2023
Pytorch implementation for the Temporal and Object Quantification Networks (TOQ-Nets).

TOQ-Nets-PyTorch-Release Pytorch implementation for the Temporal and Object Quantification Networks (TOQ-Nets). Temporal and Object Quantification Net

Zhezheng Luo 9 Jun 30, 2022
Final project code: Implementing MAE with downscaled encoders and datasets, for ESE546 FA21 at University of Pennsylvania

546 Final Project: Masked Autoencoder Haoran Tang, Qirui Wu 1. Training To train the network, please run mae_pretraining.py. Please modify folder path

Haoran Tang 0 Apr 22, 2022
YOLOv2 in PyTorch

YOLOv2 in PyTorch NOTE: This project is no longer maintained and may not compatible with the newest pytorch (after 0.4.0). This is a PyTorch implement

Long Chen 1.5k Jan 02, 2023
To build a regression model to predict the concrete compressive strength based on the different features in the training data.

Cement-Strength-Prediction Problem Statement To build a regression model to predict the concrete compressive strength based on the different features

Ashish Kumar 4 Jun 11, 2022
This code provides various models combining dilated convolutions with residual networks

Overview This code provides various models combining dilated convolutions with residual networks. Our models can achieve better performance with less

Fisher Yu 1.1k Dec 30, 2022
The Environment I built to study Reinforcement Learning + Pokemon Showdown

pokemon-showdown-rl-environment The Environment I built to study Reinforcement Learning + Pokemon Showdown Been a while since I ran this. Think it is

3 Jan 16, 2022