A Simple Key-Value Data-store written in Python

Overview

mercury-db

GitHub followers GitHub forks GitHub Repo stars Lines of code GitHub PyPI

This is a File Based Key-Value Datastore that supports basic CRUD (Create, Read, Update, Delete) operations developed using Python.

The data store will support the following functional requirements:

  1. A new key-value pair can be added to the data store using the Create operation. The key is always a string - capped at 32chars. The value is always a JSON object-capped at 16KB.
  2. A Read operation on a key can be performed by providing the key, and receiving the value in response, as a JSON object.
  3. A Delete operation can be performed by providing the key.
  4. Every key supports setting a Time-To-Live property when it is created. This property is optional. If provided, it will be evaluated as an integer defining the number of seconds the key must be retained in the data store. Once the Time-To-Live for a key has expired, the key will no longer be available for Read or Delete operations.

The data store will also support the following non-functional requirements:

  1. The size of the file storing data must never exceed 1GB.
  2. More than one client process cannot be allowed to use the same file as a data store at any given time
  3. A client process is allowed to access the data store using multiple threads, if it desires to The data store must therefore be thread-safe.

Overview

The application has been developed as a library so that users can just import it and create an instance of the class and work with the data store by invoking relevant methods. The application satisfies both the functional and non-functional requirements mentioned above.

File Structure

  • src/mercury_db/datastore.py - The library that contains the methods for performing CRUD Operations.
  • setup.py

Installation

pip install mercury-db

Usage

Consider the following examples:

from src.mercury_db.datastore import *

ds = DataStore()
ds.create('myname', 'Vaidhyanathan', 60)
print(ds.read('myname'))
ds.create('New Delhi', 'India Gate')
ds.delete('myname')
print(ds.read('New Delhi'))
print(ds.read('name'))

Development Environment

  • OS: Linux (Ubuntu) - Linux-5.11.0-41
  • Language(s) used: Python

The application doesn't have any OS specific dependencies and should run without any problems in Mac and Windows as well.

Bugs/Requests

Please use the GitHub issue tracker to submit bugs or request features.

License

Copyright Vaidhyanathan S M, 2021

Distributed under the terms of the MIT license, py-dsa is free and open source software.

Owner
Vaidhyanathan S M
Software Developer | Native Android & Flutter Developer | Python | C++ | Technical Blogger @Medium
Vaidhyanathan S M
Numba-accelerated Pythonic implementation of MPDATA with examples in Python, Julia and Matlab

PyMPDATA PyMPDATA is a high-performance Numba-accelerated Pythonic implementation of the MPDATA algorithm of Smolarkiewicz et al. used in geophysical

Atmospheric Cloud Simulation Group @ Jagiellonian University 15 Nov 23, 2022
Real-time VIBE: Frame by Frame Inference of VIBE (Video Inference for Human Body Pose and Shape Estimation)

Real-time VIBE Inference VIBE frame-by-frame. Overview This is a frame-by-frame inference fork of VIBE at [https://github.com/mkocabas/VIBE]. Usage: i

23 Jul 02, 2022
SLAMP: Stochastic Latent Appearance and Motion Prediction

SLAMP: Stochastic Latent Appearance and Motion Prediction Official implementation of the paper SLAMP: Stochastic Latent Appearance and Motion Predicti

Kaan Akan 34 Dec 08, 2022
Implementation of ReSeg using PyTorch

Implementation of ReSeg using PyTorch ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation Pascal-Part Annotations Pascal VOC 2010

Onur Kaplan 46 Nov 23, 2022
This project helps to colorize grayscale images using multiple exemplars.

Multiple Exemplar-based Deep Colorization (Pytorch Implementation) Pretrained Model [Jitendra Chautharia](IIT Jodhpur)1,3, Prerequisites Python 3.6+ N

jitendra chautharia 3 Aug 05, 2022
Yas CRNN model training - Yet Another Genshin Impact Scanner

Yas-Train Yet Another Genshin Impact Scanner 又一个原神圣遗物导出器 介绍 该仓库为 Yas 的模型训练程序 相关资料 MobileNetV3 CRNN 使用 假设你会设置基本的pytorch环境。 生成数据集 python main.py gen 训练

wormtql 18 Jan 08, 2023
Data Consistency for Magnetic Resonance Imaging

Data Consistency for Magnetic Resonance Imaging Data Consistency (DC) is crucial for generalization in multi-modal MRI data and robustness in detectin

Dimitris Karkalousos 19 Dec 12, 2022
一个目标检测的通用框架(不需要cuda编译),支持Yolo全系列(v2~v5)、EfficientDet、RetinaNet、Cascade-RCNN等SOTA网络。

一个目标检测的通用框架(不需要cuda编译),支持Yolo全系列(v2~v5)、EfficientDet、RetinaNet、Cascade-RCNN等SOTA网络。

Haoyu Xu 203 Jan 03, 2023
PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models

PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models This repository is the official implementation of the fol

DistributedML 41 Dec 06, 2022
Rlmm blender toolkit - A set of tools to streamline level generation in UDK straight from Blender

rlmm_blender_toolkit A set of tools to streamline level generation in UDK straig

Rocket League Mapmaking 0 Jan 15, 2022
Code for "MetaMorph: Learning Universal Controllers with Transformers", Gupta et al, ICLR 2022

MetaMorph: Learning Universal Controllers with Transformers This is the code for the paper MetaMorph: Learning Universal Controllers with Transformers

Agrim Gupta 50 Jan 03, 2023
AnimationKit: AI Upscaling & Interpolation using Real-ESRGAN+RIFE

ALPHA 2.5: Frostbite Revival (Released 12/23/21) Changelog: [ UI ] Chained design. All steps link to one another! Use the master override toggles to s

87 Nov 16, 2022
Official implementation of Self-supervised Image-to-text and Text-to-image Synthesis

Self-supervised Image-to-text and Text-to-image Synthesis This is the official implementation of Self-supervised Image-to-text and Text-to-image Synth

6 Jul 31, 2022
Implementation of ICCV 2021 oral paper -- A Novel Self-Supervised Learning for Gaussian Mixture Model

SS-GMM Implementation of ICCV 2021 oral paper -- Self-Supervised Image Prior Learning with GMM from a Single Noisy Image with supplementary material R

HUST-The Tan Lab 4 Dec 05, 2022
Official implementation of "OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association" in PyTorch.

openpifpaf Continuously tested on Linux, MacOS and Windows: New 2021 paper: OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Te

VITA lab at EPFL 50 Dec 29, 2022
Collections for the lasted paper about multi-view clustering methods (papers, codes)

Multi-View Clustering Papers Collections for the lasted paper about multi-view clustering methods (papers, codes). There also exists some repositories

Andrew Guan 10 Sep 20, 2022
PyTorch implementation of CVPR'18 - Perturbative Neural Networks

This is an attempt to reproduce results in Perturbative Neural Networks paper. See original repo for details.

Michael Klachko 57 May 14, 2021
Official code base for the poster "On the use of Cortical Magnification and Saccades as Biological Proxies for Data Augmentation" published in NeurIPS 2021 Workshop (SVRHM)

Self-Supervised Learning (SimCLR) with Biological Plausible Image Augmentations Official code base for the poster "On the use of Cortical Magnificatio

Binxu 8 Aug 17, 2022
Code for the paper "Adapting Monolingual Models: Data can be Scarce when Language Similarity is High"

Wietse de Vries • Martijn Bartelds • Malvina Nissim • Martijn Wieling Adapting Monolingual Models: Data can be Scarce when Language Similarity is High

Wietse de Vries 5 Aug 02, 2021
“英特尔创新大师杯”深度学习挑战赛 赛道3:CCKS2021中文NLP地址相关性任务

ccks2021-track3 CCKS2021中文NLP地址相关性任务-赛道三-冠军方案 团队:我的加菲鱼- wodejiafeiyu 初赛第二/复赛第一/决赛第一 前言 19年开始,陆陆续续参加了一些比赛,拿到过一些top,比较懒一直都没分享过,这次比较幸运又拿了top1,打算分享下 分类的任务

shaochenjie 131 Dec 31, 2022