Recent Trend

amplify-js
A declarative JavaScript library for application development using cloud services.
lottie-ios
An iOS library to natively render After Effects vector animations
Faze4-Robotic-arm
All files for 6 axis robot arm with cycloidal gearboxes .
xiaobaiyang

Javascript
A repository for All algorithms implemented in Javascript (for educational purposes only)
blog-post-workflow
Show your latest blog posts from any sources or StackOverflow activity on your GitHub profile/project readme automatically using the RSS feed
reverse-interview
Questions to ask the company during your interview
expo
An open-source platform for making universal native apps with React. Expo runs on Android, iOS, and the web.
955.WLB
955 不加班的公司名单 - 工作 955,work–life balance (工作与生活的平衡)
A-to-Z-Resources-for-Students
✅ Curated list of resources for college students
TDengine
An open-source big data platform designed and optimized for the Internet of Things (IoT).
django-jazzmin
Jazzy theme for Django
full-stack-fastapi-postgresql
Full stack, modern web application generator. Using FastAPI, PostgreSQL as database, Docker, automatic HTTPS and more.
Reflection_Summary
算法理论基础知识应知应会
Best-websites-a-programmer-should-visit
? Some useful websites for programmers.
bpytop
Linux/OSX/FreeBSD resource monitor
TelemetrySourcerer
Enumerate and disable common sources of telemetry used by AV/EDR.
instagrabber
InstaGrabber, the open-source Instagram client for Android. Originally by @AwaisKing.
pe_tree

Powershell-Scripts
Helpful list of powershell scripts I have found/created
drawio
Source to app.diagrams.net
analytics
Simple and privacy-friendly alternative to Google Analytics
pycaret
An open source, low-code machine learning library in Python
Ciphey
Automated decryption tool
Data-Science-Interview-Resources
A repository listing out the potential sources which will help you in preparing for a Data Science/Machine Learning interview. New resources added frequently.
ps4-ipv6-uaf

UNSAM_2020c2_Python
Curso de programación en Python - 2do cuatrimestre 2020 - UNSAM
gpu.js
GPU Accelerated JavaScript
how-to-secure-anything
How to systematically secure anything: a repository about security engineering
paperview
A high performance X11 animated wallpaper setter
core
? JAVClub - 让你的大姐姐不再走丢
home-cloud
The "cloud" at home
haoel.github.io

InstaPy
? Instagram Bot - Tool for automated Instagram interactions
bat
A cat(1) clone with wings.
DeOldify
A Deep Learning based project for colorizing and restoring old images (and video!)
educative.io_courses
this is downloadings of all educative.io free student subscription courses as pdf from GitHub student pack
rustlings
? Small exercises to get you used to reading and writing Rust code!
trackerslist
Updated list of public BitTorrent trackers
Statistical-Learning-Method_Code
手写实现李航《统计学习方法》书中全部算法
mobile
React Native client application for COVID Shield on iOS and Android
binary_search
A collection of improved binary search algorithms.
mirai

TapTap
Port of the double tap on back of device feature from Android 11 to any armv8 Android device
complete-javascript-course
Starter files, final projects and FAQ for my Complete JavaScript course
icons
Official open source SVG icon library for Bootstrap.
oneflow
OneFlow is a performance-centered and open-source deep learning framework.
ml-engineer-roadmap
WIP: Roadmap to becoming a machine learning engineer in 2020
hvmi
Hypervisor Memory Introspection Core Library
fhe-toolkit-linux
IBM Fully Homomorphic Encryption Toolkit For Linux
teenyicons
Tiny minimal 1px icons designed to fit in the smallest places.
project-citadel
An open source project management tool with Kanban boards
covid-alert-app
Exposure notification client application / Application client de notification d'exposition
ChromeAppHeroes
?谷粒-Chrome插件英雄榜, 为优秀的Chrome插件写一本中文说明书, 让Chrome插件英雄们造福人类~ ChromePluginHeroes, Write a Chinese manual for the excellent Chrome plugin, let the Chrome plugin heroes benefit the human~ 公众号「0加1」同步更新
SSPanel-Uim
SSPanel V3 魔改再次修改版
UnusualVolumeDetector
Gets the last 5 months of volume history for every ticker, and alerts you when a stock's volume exceeds 10 standard deviations from the mean within the last 3 days
formik
Build forms in React, without the tears ?
learn-cantrill-io-labs
Standard and Advanced Demos for learn.cantrill.io courses
TransCoder
Public release of the TransCoder research project https://arxiv.org/pdf/2006.03511.pdf
bounty-targets-data
This repo contains hourly-updated data dumps of bug bounty platform scopes (like Hackerone/Bugcrowd/Intigriti/etc) that are eligible for reports
CtCI-6th-Edition
Cracking the Coding Interview 6th Ed. Solutions
windows95
?? Windows 95 in Electron. Runs on macOS, Linux, and Windows.
SkyArk
SkyArk helps to discover, assess and secure the most privileged entities in Azure and AWS
interviews
Everything you need to know to get the job.
Android-Analysis
Getting Genymotion & Burpsuite setup for Android Mobile App Analysis
detext
DeText: A Deep Neural Text Understanding Framework for Ranking and Classification Tasks
awesome-java
A curated list of awesome frameworks, libraries and software for the Java programming language.
workflow

tye
Tye is a tool that makes developing, testing, and deploying microservices and distributed applications easier. Project Tye includes a local orchestrator to make developing microservices easier and the
java-design-patterns
Design patterns implemented in Java
java8-tutorial
Modern Java - A Guide to Java 8
generator-jhipster
JHipster is a development platform to quickly generate, develop, & deploy modern web applications & microservice architectures.
stayaway-app
Official repository for the STAYAWAY COVID mobile application
api-guidelines
Microsoft REST API Guidelines
win10script
This is the Ultimate Windows 10 Script from a creation from multiple debloat scripts and gists from github.
tutorials
Just Announced - "Learn Spring Security OAuth":
Otto
Otto makes machine learning an intuitive, natural language experience.? Facebook AI Challenge winner
first-order-model
This repository contains the source code for the paper First Order Motion Model for Image Animation
laravel-best-practices
Laravel best practices
hiring-without-whiteboards
⭐️ Companies that don't have a broken hiring process
PyTorch_YOLOv4
PyTorch implementation of YOLOv4
macintosh.js
A virtual Apple Macintosh with System 8, running in Electron. I'm sorry.
QuickCut
Your most handy video processing software
Super-mario-bros-PPO-pytorch
Proximal Policy Optimization (PPO) algorithm for Super Mario Bros
arrow
Apache Arrow is a cross-language development platform for in-memory data. It specifies a standardized language-independent columnar memory format for flat and hierarchical data, organized for efficien
swift
The Swift Programming Language
flutter
Flutter makes it easy and fast to build beautiful apps for mobile and beyond.
pikvm
Open and cheap DIY IP-KVM based on Raspberry Pi
ILSpy
.NET Decompiler with support for PDB generation, ReadyToRun, Metadata (&more) - cross-platform!
aluraflix
⚛️ Projeto feito durante a Imersão React da Alura
starship
☄?️ The minimal, blazing-fast, and infinitely customizable prompt for any shell!
leonsans
Leon Sans is a geometric sans-serif typeface made with code in 2019 by Jongmin Kim.
MCVmComputers
Order computer parts from a satellite orbiting around your minecraft world and build actual working computers with them!
CleanArchitecture.WebApi
An implementation of Clean Architecture for ASP.NET Core 3.1 WebAPI. Built with loosely coupled architecture and clean-code practices in mind.
NutShell
RISC-V SoC designed by students in UCAS
bartosz-basics-of-haskell
Code and exercises from Bartosz Milewski's Basics of Haskell Tutorial
fullstack-starterkit
GraphQL first full-stack starter kit with Node, React. Powered by TypeScript
movement-tracking
UP - DOWN - LEFT - RIGHT movement tracking.
OSCP-Exam-Report-Template-Markdown
? OSCP Exam Report Template in Markdown
react-native-instagram-clone
A React Native app - Clone Instagram mobile app (In progress)
felicette
Satellite imagery for dummies.
neovim
Vim-fork focused on extensibility and usability
machine-learning-roadmap
A roadmap connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them.
python-cheatsheet
Comprehensive Python Cheatsheet
awesome-cold-showers
For when people get too hyped up about things
cutter
Free and Open Source Reverse Engineering Platform powered by radare2
ORB_SLAM3
ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM
RustScan
Faster Nmap Scanning with Rust
openpilot
openpilot is an open source driver assistance system. openpilot performs the functions of Automated Lane Centering and Adaptive Cruise Control for over 85 supported car makes and models.
retinaface
The remake of the https://github.com/biubug6/Pytorch_Retinaface
awesome-gpt3

GitHub520
?让你“爱”上 GitHub,解决访问时图裂、加载慢的问题。
LeetcodeTop
汇总各大互联网公司容易考察的高频leetcode题?
angular-tetris
Tetris game built with Angular 10 and Akita ?
umi-core
UMI Core Go Library
RustScan
Faster Nmap Scanning with Rust
rpi-power-monitor
Raspberry Pi Power Monitor
umi-core-py
UMI Core Python Library
gpt3-sandbox
The goal of this project is to enable users to create cool web demos using the newly released OpenAI GPT-3 API with just a few lines of Python.
easy_rust
Rust explained using easy English
rengine
reNgine is an automated reconnaissance framework meant for gathering information during penetration testing of web applications. reNgine has customizable scan engines, which can be used to scan the we
industry-machine-learning
A curated list of applied machine learning and data science notebooks and libraries across different industries (by @firmai)
umi-core-js
UMI Core JS Library
bloatbox
☑️? Get rid of bloatware and clean your Windows 10 Start menu
umi-core-php
UMI Core PHP Library
proposal-record-tuple
ECMAScript proposal for the Record and Tuple value types. | Stage 2: it will change!
jetbrains-agent-latest
jetbrains全家桶永久激活破解,不需要修改host。完美破解!共享给各个程序员兄弟使用。适用于2020版本。
applied-ml
Curated papers, articles & videos on data science & machine learning applied in production, with results.
lotus
Implementation of the Filecoin protocol, written in Go
cat
CAT 作为服务端项目基础组件,提供了 Java, C/C++, Node.js, Python, Go 等多语言客户端,已经在美团点评的基础架构中间件框架(MVC框架,RPC框架,数据库框架,缓存框架等,消息队列,配置系统等)深度集成,为美团点评各业务线提供系统丰富的性能指标、健康状况、实时告警等。
fawkes
Fawkes, privacy preserving tool against facial recognition systems. More info at http://sandlab.cs.uchicago.edu/fawkes
terminal
The new Windows Terminal and the original Windows console host, all in the same place!
kibana
Your window into the Elastic Stack
terraform
Terraform enables you to safely and predictably create, change, and improve infrastructure. It is an open source tool that codifies APIs into declarative configuration files that can be shared amongst
gotraining
Go Training Class Material :
JavaFamily
【Java面试+Java学习指南】 一份涵盖大部分Java程序员所需要掌握的核心知识。
storybook
? The UI component workshop. Develop, document, & test for React, Vue, Angular, Ember, Web Components, & more!
awesome-remote-job
A curated list of awesome remote jobs and resources. Inspired by https://github.com/vinta/awesome-python
vueuse
? Collection of Composition API utils for Vue 2 and 3
fe-interview
前端面试每日 3+1,以面试题来驱动学习,提倡每日学习与思考,每天进步一点!每天早上5点纯手工发布面试题(死磕自己,愉悦大家),3000+道前端面试题全面覆盖,HTML/CSS/JavaScript/Vue/React/Nodejs/TypeScript/ECMAScritpt/Webpack/Jquery/小程序/软技能……
stock
stock,股票系统。使用python进行开发。
awesome-ml-courses
Awesome free machine learning and AI courses with video lectures.
laravel-boilerplate
The Laravel Boilerplate Project - https://laravel-boilerplate.com
reactjs-interview-questions
List of top 500 ReactJS Interview Questions & Answers....Coding exercise questions are coming soon!!
lx-music-desktop
一个基于 electron 的音乐软件
number-verifier
Number Verifier is a SMS verification tool that makes it easy to get a disposable SMS number and bypass SMS number verifications on any site.
CyberProfDevelopmentCovidResources
An awesome list of FREE resources for training, conferences, speaking, labs, reading, etc that are free all the time or during COVID-19 that cybersecurity professionals with downtime can take advantag
opentelemetry-specification
Specifications for OpenTelemetry
front-end-interview-handbook
? No bullshit answers to the famous h5bp "Front-end Job Interview Questions"
hello-algorithm
?????? 本项目包括:1、我写的 30w 字图解算法题典 2、100 张编程类超清晰思维导图 3、100 篇大厂面经汇总 4、各语言编程电子书 100 本 5、小浩算法网站源代码 ( ?? 国人项目上榜不容易,右上角助力一波!干就对了,奥利给 !??)
awesome-production-machine-learning
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
bypass-paywalls-chrome
Bypass Paywalls web browser extension for Chrome and Firefox.
awesome-mlops
A curated list of references for MLOps
airlines-to-china-covid-19
疫情期间的回国航班汇总
gpt-3-experiments
Test prompts for OpenAI's GPT-3 API and the resulting AI-generated texts.
NativeAlphaForAndroid

elyra
Elyra extends JupyterLab Notebooks with an AI centric approach.
myvision
Computer vision based ML training data generation tool. ?
computer-science
? Path to a free self-taught education in Computer Science!
abp
Open Source Web Application Framework for ASP.NET Core
Best-Flutter-UI-Templates
completely free for everyone. Its build-in Flutter Dart.
docs-next
Vue 3 core documentation
sodium-fabric
A Minecraft mod designed to improve frame rates and reduce micro-stutter
puppeteer-recorder
Puppeteer recorder is a Chrome extension that records your browser interactions and generates a Puppeteer script.
flutter_twitter_clone
Fully functional Twitter clone built in flutter framework using Firebase realtime database and storage
ailab
Experience, Learn and Code the latest breakthrough innovations with Microsoft AI
forem
For empowering community ?
hedgehog-lab
An open source scientific computing environment for JavaScript TOTALLY in your browser, matrix operations with GPU acceleration, TeX support, data visualization and symbolic computation.
Deep-Learning-Papers-Reading-Roadmap
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
QA_bible
Библия QA это 163 страницы смеси ответов на вопросы с реальных собеседований на manual QA, перевода интересного контента с зарубежных ресурсов и агрегации материала с отечественных.
CTF_Hacker-Tools
CTF-渗透测试~工具合集
kong
? The Cloud-Native API Gateway
Pokedex
?️ Android Pokedex using Dagger Hilt, Motion, Coroutines, Flow, Jetpack (Room, ViewModel, LiveData) based on MVVM architecture.
ps4jb
PS4 6.72 jailbreak
PowerShell
PowerShell for every system!
capa
The FLARE team's open-source tool to identify capabilities in executable files.
react-use
React Hooks — ?
NewPipe
A libre lightweight streaming front-end for Android.
terraform-cdk
Define infrastructure resources using programming constructs and provision them using HashiCorp Terraform
developer-roadmap
Roadmap to becoming a web developer in 2020
ds-cheatsheets
List of Data Science Cheatsheets to rule the world
AppAuth-Android
Android client SDK for communicating with OAuth 2.0 and OpenID Connect providers.
data-scientist-roadmap
Toturial coming with "data science roadmap" graphe.
QuantumKatas
Tutorials and programming exercises for learning Q# and quantum computing
pwncat
Fancy reverse and bind shell handler
jquery
jQuery JavaScript Library
incubator-brpc
Industrial-grade RPC framework used throughout Baidu, with 1,000,000+ instances and thousands kinds of services, called "baidu-rpc" inside Baidu.
Rocket
A web framework for Rust.
protobuf
Protocol Buffers - Google's data interchange format
PaddleOCR
Awesome OCR toolkits based on PaddlePaddle (8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices)
Microsoft-threat-protection-Hunting-Queries
Sample queries for Advanced hunting in Microsoft Threat Protection
RxSwift
Reactive Programming in Swift
funds
自选基金助手是一款Chrome扩展,用来快速获取关注基金的实时数据,查看自选基金的实时估值情况
sherloq
An open-source digital image forensic toolset
phaser
Phaser is a fun, free and fast 2D game framework for making HTML5 games for desktop and mobile web browsers, supporting Canvas and WebGL rendering.
awesome-github-profile-readme
A curated list of awesome Github Profile READMEs
Awesome-Hacking
A collection of various awesome lists for hackers, pentesters and security researchers
papers-we-love
Papers from the computer science community to read and discuss.
ZLMediaKit
A lightweight RTSP/RTMP/HTTP/HLS/HTTP-FLV/WebSocket-FLV/GB28181 server and client framework based on C++11
googleapis
Public interface definitions of Google APIs.
PSBits
Simple (relatively) things allowing you to dig a bit deeper than usual.
CodeGuide
? 本代码库是作者小傅哥多年从事一线互联网 Java 开发的学习历程技术汇总,旨在为大家提供一个清晰详细的学习教程,侧重点更倾向编写Java核心内容。如果本仓库能为您提供帮助,请给予支持(关注、点赞、分享)!
Javascript-Essentials

gradio
Rapidly create UIs for prototyping your machine learning model in 3 minutes
snapchat-clone
? A SnapChat clone built with React, Redux and Typescript. Styled with SASS. Tested with Cypress, Jest and Enzyme. Linted with Eslint and formatted with Prettier!
clean-architecture-manga
? Clean Architecture with .NET Core 3.1, C# 8 and React+Redux. Use cases as central organizing structure, completely testable, decoupled from frameworks
flink
Apache Flink
modular-monolith-with-ddd
Full Modular Monolith application with Domain-Driven Design approach.
pandas_exercises
Practice your pandas skills!
awesomefluttertips
❤️Flutter ❤️ tips and tricks ❤️ Awesome Flutter ❤️ tips and tricks ❤️
voila
Voilà turns Jupyter notebooks into standalone web applications
Flutter-Shopping-UI-Kit
I developed this application just for learning purpose. There are 20+ screen variations.
game_control

the-art-of-command-line
Master the command line, in one page
fastapi
FastAPI framework, high performance, easy to learn, fast to code, ready for production
paper_collection
Academic papers related to fuzzing, binary analysis and exploit dev, that I want to read or have already read
nndl.github.io
《神经网络与深度学习》 邱锡鹏著 Neural Network and Deep Learning
developer-handbook
An opinionated guide on how to become a professional Web/Mobile App Developer.
Tiny-XSS-Payloads
A collection of tiny XSS Payloads that can be used in different contexts.
100-Days-Of-ML-Code
100 Days of ML Coding
youtubeclone-backend
Youtube Clone Backend (Express + Sequelize)
node-react-ecommerce
Build ECommerce Website Like Amazon By React & Node & MongoDB
gin-vue-admin
基于gin+vue搭建的后台管理系统框架,集成jwt鉴权,权限管理,动态路由,分页封装,多点登录拦截,资源权限,上传下载,代码生成器,表单生成器等基础功能,五分钟一套CURD前后端代码包含数据库的快感你不要体验一下吗~,更多功能正在开发中,欢迎issue和pr~
ant-design-blazor
?A set of enterprise-class UI components based on Ant Design and Blazor WebAssembly.
spleeter
Deezer source separation library including pretrained models.
youtubeclone-frontend
Youtube Clone Frontend (React + Redux)
runtime
.NET is a cross-platform runtime for cloud, mobile, desktop, and IoT apps.
pulsar
Apache Pulsar - distributed pub-sub messaging system
AnotherRedisDesktopManager
???A faster, better and more stable redis desktop manager, compatible with Linux, windows, mac. What's more, it won't crash when loading a large number of keys.
realworld
"The mother of all demo apps" — Exemplary fullstack Medium.com clone powered by React, Angular, Node, Django, and many more ?
checklist
Beyond Accuracy: Behavioral Testing of NLP models with CheckList
Complex-YOLOv4-Pytorch
The PyTorch Implementation based on YOLOv4 of the paper: "Complex-YOLO: Real-time 3D Object Detection on Point Clouds"
yandex-ui
Yandex UI Kit build on React and bem-react
prometheus
The Prometheus monitoring system and time series database.
Awesome-Profile-README-templates
A collection of awesome readme templates to display on your profile
RedditOS
A SwiftUI Reddit client for macOS Big Sur
facebook-ios-sdk
Used to integrate the Facebook Platform with your iOS & tvOS apps.
presto
The official home of the Presto distributed SQL query engine for big data
og-aws
? Amazon Web Services — a practical guide
copilot-cli
The AWS Copilot CLI is a tool for developers to build, release and operate production ready containerized applications on Amazon ECS and AWS Fargate.
How-to-create-a-csgo-cheating-program
CSGO游戏透视自瞄辅助实现教程
moby
Moby Project - a collaborative project for the container ecosystem to assemble container-based systems
torchsde
Differentiable SDE solvers with GPU support and efficient sensitivity analysis.
tsunami-security-scanner-plugins
This project aims to provide a central repository for many useful Tsunami Security Scanner plugins.
metersphere
MeterSphere 是一站式的开源企业级持续测试平台,涵盖测试跟踪、接口测试、性能测试、团队协作等功能
flokk
A fresh and modern Google Contacts manager that integrates with GitHub and Twitter.
free-for-dev
A list of SaaS, PaaS and IaaS offerings that have free tiers of interest to devops and infradev
emscripten
Emscripten: An LLVM-to-Web Compiler
grpc-java
The Java gRPC implementation. HTTP/2 based RPC
Team-Ares
Repository for all TeamARES POC code and tools.
cheatsheets
Official Matplotlib cheat sheets
fast
The adaptive interface system
 for modern web experiences.
azure-sdk-for-python
This repository is for active development of the Azure SDK for Python. For consumers of the SDK we recommend visiting our public developer docs at https://docs.microsoft.com/en-us/python/azure/ or our
PowerJob
新一代分布式任务调度与计算框架,支持CRON、API、固定频率、固定延迟等调度策略,提供工作流来编排任务解决依赖关系,使用简单,功能强大,文档齐全,欢迎各位接入使用!
BottlEye
BottlEye is a usermode emulator for the popular anti-cheat BattlEye
cml
CML - Continuous Machine Learning or CI/CD for ML
tauri
Framework agnostic toolchain for building highly secure native apps that have tiny binaries and are very fast.
dlwpt-code
Code for the book Deep Learning with PyTorch by Eli Stevens, Luca Antiga, and Thomas Viehmann.
RevokeMsgPatcher
A hex editor for WeChat/QQ/TIM - PC版微信/QQ/TIM防撤回补丁(我已经看到了,撤回也没用了)
react-native-firebase
? A well-tested feature-rich modular Firebase implementation for React Native. Supports both iOS & Android platforms for all Firebase services.
dragonwell8
Alibaba Dragonwell8 JDK
RADWIMPS
君の then-then-then 世は Promise で Future
Tiktok
高仿抖音APP
TaBERT
This repository contains source code for the TaBERT model, a pre-trained language model for learning joint representations of natural language utterances and (semi-)structured tables for semantic pars
dask-tutorial
Dask tutorial
HelloGitHub
Find pearls on open-source seashore 分享 GitHub 上有趣、入门级的开源项目
covid-tracker-app
COVID Tracker App Repository
digital-gardeners
Resources, links, projects, and ideas for gardeners tending their digital notes on the public interwebs
hackingtool
ALL IN ONE Hacking Tool For Hackers
ounotes
An Application built for students to access Notes , Question Papers , Syllabus and Resources for all Subjects of O.U (Osmania University) ??‍?
docker-cheat-sheet
Docker Cheat Sheet
blog
gamedev blog
CVE-2020-5902
CVE-2020-5902 BIG-IP
course-content
Summer course content for Neuromatch Academy
texthero
Text preprocessing, representation and visualization from zero to hero.
DeepLearning-500-questions
深度学习500问,以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述,以帮助自己及有需要的读者。 全书分为18个章节,50余万字。由于水平有限,书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作,联系[email protected] 版权所有,违权必究 Tan 2018.06
edex-ui
A cross-platform, customizable science fiction terminal emulator with advanced monitoring & touchscreen support.
BYTEPATH

discord.js
A powerful JavaScript library for interacting with the Discord API
awesome-machine-learning
A curated list of awesome Machine Learning frameworks, libraries and software.
sweetviz
Visualize and compare datasets, target values and associations, with one line of code.
E-commerce-App-UI-Flutter
Nice and clean Online Shop app UI by using #Flutter.
rich
Rich is a Python library for rich text and beautiful formatting in the terminal.
javascript-algorithms
? Algorithms and data structures implemented in JavaScript with explanations and links to further readings
gitqlite
Query git repositories with SQL. Uses SQLite virtual tables and go-git
NonEuclidean
A Non-Euclidean Rendering Engine for 3D scenes.
org-roam
Rudimentary Roam replica with Org-mode
guietta

learngo
1000+ Hand-Crafted Go Examples, Exercises, and Quizzes
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Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.

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The System Design Primer


Motivation

Learn how to design large-scale systems.

Prep for the system design interview.

Learn how to design large-scale systems

Learning how to design scalable systems will help you become a better engineer.

System design is a broad topic. There is a vast amount of resources scattered throughout the web on system design principles.

This repo is an organized collection of resources to help you learn how to build systems at scale.

Learn from the open source community

This is a continually updated, open source project.

Contributions are welcome!

Prep for the system design interview

In addition to coding interviews, system design is a required component of the technical interview process at many tech companies.

Practice common system design interview questions and compare your results with sample solutions: discussions, code, and diagrams.

Additional topics for interview prep:

Anki flashcards


The provided Anki flashcard decks use spaced repetition to help you retain key system design concepts.

Great for use while on-the-go.

Coding Resource: Interactive Coding Challenges

Looking for resources to help you prep for the Coding Interview?


Check out the sister repo Interactive Coding Challenges, which contains an additional Anki deck:

Contributing

Learn from the community.

Feel free to submit pull requests to help:

Content that needs some polishing is placed under development.

Review the Contributing Guidelines.

Index of system design topics

Summaries of various system design topics, including pros and cons. Everything is a trade-off.

Each section contains links to more in-depth resources.


Study guide

Suggested topics to review based on your interview timeline (short, medium, long).

Imgur

Q: For interviews, do I need to know everything here?

A: No, you don't need to know everything here to prepare for the interview.

What you are asked in an interview depends on variables such as:

More experienced candidates are generally expected to know more about system design. Architects or team leads might be expected to know more than individual contributors. Top tech companies are likely to have one or more design interview rounds.

Start broad and go deeper in a few areas. It helps to know a little about various key system design topics. Adjust the following guide based on your timeline, experience, what positions you are interviewing for, and which companies you are interviewing with.

Short Medium Long
Read through the System design topics to get a broad understanding of how systems work :+1: :+1: :+1:
Read through a few articles in the Company engineering blogs for the companies you are interviewing with :+1: :+1: :+1:
Read through a few Real world architectures :+1: :+1: :+1:
Review How to approach a system design interview question :+1: :+1: :+1:
Work through System design interview questions with solutions Some Many Most
Work through Object-oriented design interview questions with solutions Some Many Most
Review Additional system design interview questions Some Many Most

How to approach a system design interview question

How to tackle a system design interview question.

The system design interview is an open-ended conversation. You are expected to lead it.

You can use the following steps to guide the discussion. To help solidify this process, work through the System design interview questions with solutions section using the following steps.

Step 1: Outline use cases, constraints, and assumptions

Gather requirements and scope the problem. Ask questions to clarify use cases and constraints. Discuss assumptions.

Step 2: Create a high level design

Outline a high level design with all important components.

Step 3: Design core components

Dive into details for each core component. For example, if you were asked to design a url shortening service, discuss:

Step 4: Scale the design

Identify and address bottlenecks, given the constraints. For example, do you need the following to address scalability issues?

Discuss potential solutions and trade-offs. Everything is a trade-off. Address bottlenecks using principles of scalable system design.

Back-of-the-envelope calculations

You might be asked to do some estimates by hand. Refer to the Appendix for the following resources:

Source(s) and further reading

Check out the following links to get a better idea of what to expect:

System design interview questions with solutions

Common system design interview questions with sample discussions, code, and diagrams.

Solutions linked to content in the solutions/ folder.

Question
Design Pastebin.com (or Bit.ly) Solution
Design the Twitter timeline and search (or Facebook feed and search) Solution
Design a web crawler Solution
Design Mint.com Solution
Design the data structures for a social network Solution
Design a key-value store for a search engine Solution
Design Amazon's sales ranking by category feature Solution
Design a system that scales to millions of users on AWS Solution
Add a system design question Contribute

Design Pastebin.com (or Bit.ly)

View exercise and solution

Imgur

Design the Twitter timeline and search (or Facebook feed and search)

View exercise and solution

Imgur

Design a web crawler

View exercise and solution

Imgur

Design Mint.com

View exercise and solution

Imgur

Design the data structures for a social network

View exercise and solution

Imgur

Design a key-value store for a search engine

View exercise and solution

Imgur

Design Amazon's sales ranking by category feature

View exercise and solution

Imgur

Design a system that scales to millions of users on AWS

View exercise and solution

Imgur

Object-oriented design interview questions with solutions

Common object-oriented design interview questions with sample discussions, code, and diagrams.

Solutions linked to content in the solutions/ folder.

Note: This section is under development

Question
Design a hash map Solution
Design a least recently used cache Solution
Design a call center Solution
Design a deck of cards Solution
Design a parking lot Solution
Design a chat server Solution
Design a circular array Contribute
Add an object-oriented design question Contribute

System design topics: start here

New to system design?

First, you'll need a basic understanding of common principles, learning about what they are, how they are used, and their pros and cons.

Step 1: Review the scalability video lecture

Scalability Lecture at Harvard

Step 2: Review the scalability article

Scalability

Next steps

Next, we'll look at high-level trade-offs:

Keep in mind that everything is a trade-off.

Then we'll dive into more specific topics such as DNS, CDNs, and load balancers.

Performance vs scalability

A service is scalable if it results in increased performance in a manner proportional to resources added. Generally, increasing performance means serving more units of work, but it can also be to handle larger units of work, such as when datasets grow.1

Another way to look at performance vs scalability:

Source(s) and further reading

Latency vs throughput

Latency is the time to perform some action or to produce some result.

Throughput is the number of such actions or results per unit of time.

Generally, you should aim for maximal throughput with acceptable latency.

Source(s) and further reading

Availability vs consistency

CAP theorem


Source: CAP theorem revisited

In a distributed computer system, you can only support two of the following guarantees:

Networks aren't reliable, so you'll need to support partition tolerance. You'll need to make a software tradeoff between consistency and availability.

CP - consistency and partition tolerance

Waiting for a response from the partitioned node might result in a timeout error. CP is a good choice if your business needs require atomic reads and writes.

AP - availability and partition tolerance

Responses return the most readily available version of the data available on any node, which might not be the latest. Writes might take some time to propagate when the partition is resolved.

AP is a good choice if the business needs allow for eventual consistency or when the system needs to continue working despite external errors.

Source(s) and further reading

Consistency patterns

With multiple copies of the same data, we are faced with options on how to synchronize them so clients have a consistent view of the data. Recall the definition of consistency from the CAP theorem - Every read receives the most recent write or an error.

Weak consistency

After a write, reads may or may not see it. A best effort approach is taken.

This approach is seen in systems such as memcached. Weak consistency works well in real time use cases such as VoIP, video chat, and realtime multiplayer games. For example, if you are on a phone call and lose reception for a few seconds, when you regain connection you do not hear what was spoken during connection loss.

Eventual consistency

After a write, reads will eventually see it (typically within milliseconds). Data is replicated asynchronously.

This approach is seen in systems such as DNS and email. Eventual consistency works well in highly available systems.

Strong consistency

After a write, reads will see it. Data is replicated synchronously.

This approach is seen in file systems and RDBMSes. Strong consistency works well in systems that need transactions.

Source(s) and further reading

Availability patterns

There are two main patterns to support high availability: fail-over and replication.

Fail-over

Active-passive

With active-passive fail-over, heartbeats are sent between the active and the passive server on standby. If the heartbeat is interrupted, the passive server takes over the active's IP address and resumes service.

The length of downtime is determined by whether the passive server is already running in 'hot' standby or whether it needs to start up from 'cold' standby. Only the active server handles traffic.

Active-passive failover can also be referred to as master-slave failover.

Active-active

In active-active, both servers are managing traffic, spreading the load between them.

If the servers are public-facing, the DNS would need to know about the public IPs of both servers. If the servers are internal-facing, application logic would need to know about both servers.

Active-active failover can also be referred to as master-master failover.

Disadvantage(s): failover

Replication

Master-slave and master-master

This topic is further discussed in the Database section:

Availability in numbers

Availability is often quantified by uptime (or downtime) as a percentage of time the service is available. Availability is generally measured in number of 9s--a service with 99.99% availability is described as having four 9s.

99.9% availability - three 9s

Duration Acceptable downtime
Downtime per year 8h 45min 57s
Downtime per month 43m 49.7s
Downtime per week 10m 4.8s
Downtime per day 1m 26.4s

99.99% availability - four 9s

Duration Acceptable downtime
Downtime per year 52min 35.7s
Downtime per month 4m 23s
Downtime per week 1m 5s
Downtime per day 8.6s

Availability in parallel vs in sequence

If a service consists of multiple components prone to failure, the service's overall availability depends on whether the components are in sequence or in parallel.

In sequence

Overall availability decreases when two components with availability < 100% are in sequence:

Availability (Total) = Availability (Foo) * Availability (Bar)

If both Foo and Bar each had 99.9% availability, their total availability in sequence would be 99.8%.

In parallel

Overall availability increases when two components with availability < 100% are in parallel:

Availability (Total) = 1 - (1 - Availability (Foo)) * (1 - Availability (Bar))

If both Foo and Bar each had 99.9% availability, their total availability in parallel would be 99.9999%.

Domain name system


Source: DNS security presentation

A Domain Name System (DNS) translates a domain name such as www.example.com to an IP address.

DNS is hierarchical, with a few authoritative servers at the top level. Your router or ISP provides information about which DNS server(s) to contact when doing a lookup. Lower level DNS servers cache mappings, which could become stale due to DNS propagation delays. DNS results can also be cached by your browser or OS for a certain period of time, determined by the time to live (TTL).

Services such as CloudFlare and Route 53 provide managed DNS services. Some DNS services can route traffic through various methods:

Disadvantage(s): DNS

Source(s) and further reading

Content delivery network


Source: Why use a CDN

A content delivery network (CDN) is a globally distributed network of proxy servers, serving content from locations closer to the user. Generally, static files such as HTML/CSS/JS, photos, and videos are served from CDN, although some CDNs such as Amazon's CloudFront support dynamic content. The site's DNS resolution will tell clients which server to contact.

Serving content from CDNs can significantly improve performance in two ways:

Push CDNs

Push CDNs receive new content whenever changes occur on your server. You take full responsibility for providing content, uploading directly to the CDN and rewriting URLs to point to the CDN. You can configure when content expires and when it is updated. Content is uploaded only when it is new or changed, minimizing traffic, but maximizing storage.

Sites with a small amount of traffic or sites with content that isn't often updated work well with push CDNs. Content is placed on the CDNs once, instead of being re-pulled at regular intervals.

Pull CDNs

Pull CDNs grab new content from your server when the first user requests the content. You leave the content on your server and rewrite URLs to point to the CDN. This results in a slower request until the content is cached on the CDN.

A time-to-live (TTL) determines how long content is cached. Pull CDNs minimize storage space on the CDN, but can create redundant traffic if files expire and are pulled before they have actually changed.

Sites with heavy traffic work well with pull CDNs, as traffic is spread out more evenly with only recently-requested content remaining on the CDN.

Disadvantage(s): CDN

Source(s) and further reading

Load balancer


Source: Scalable system design patterns

Load balancers distribute incoming client requests to computing resources such as application servers and databases. In each case, the load balancer returns the response from the computing resource to the appropriate client. Load balancers are effective at:

Load balancers can be implemented with hardware (expensive) or with software such as HAProxy.

Additional benefits include:

To protect against failures, it's common to set up multiple load balancers, either in active-passive or active-active mode.

Load balancers can route traffic based on various metrics, including:

Layer 4 load balancing

Layer 4 load balancers look at info at the transport layer to decide how to distribute requests. Generally, this involves the source, destination IP addresses, and ports in the header, but not the contents of the packet. Layer 4 load balancers forward network packets to and from the upstream server, performing Network Address Translation (NAT).

Layer 7 load balancing

Layer 7 load balancers look at the application layer to decide how to distribute requests. This can involve contents of the header, message, and cookies. Layer 7 load balancers terminate network traffic, reads the message, makes a load-balancing decision, then opens a connection to the selected server. For example, a layer 7 load balancer can direct video traffic to servers that host videos while directing more sensitive user billing traffic to security-hardened servers.

At the cost of flexibility, layer 4 load balancing requires less time and computing resources than Layer 7, although the performance impact can be minimal on modern commodity hardware.

Horizontal scaling

Load balancers can also help with horizontal scaling, improving performance and availability. Scaling out using commodity machines is more cost efficient and results in higher availability than scaling up a single server on more expensive hardware, called Vertical Scaling. It is also easier to hire for talent working on commodity hardware than it is for specialized enterprise systems.

Disadvantage(s): horizontal scaling

Disadvantage(s): load balancer

Source(s) and further reading

Reverse proxy (web server)


Source: Wikipedia

A reverse proxy is a web server that centralizes internal services and provides unified interfaces to the public. Requests from clients are forwarded to a server that can fulfill it before the reverse proxy returns the server's response to the client.

Additional benefits include:

Load balancer vs reverse proxy

Disadvantage(s): reverse proxy

Source(s) and further reading

Application layer


Source: Intro to architecting systems for scale

Separating out the web layer from the application layer (also known as platform layer) allows you to scale and configure both layers independently. Adding a new API results in adding application servers without necessarily adding additional web servers. The single responsibility principle advocates for small and autonomous services that work together. Small teams with small services can plan more aggressively for rapid growth.

Workers in the application layer also help enable asynchronism.

Microservices

Related to this discussion are microservices, which can be described as a suite of independently deployable, small, modular services. Each service runs a unique process and communicates through a well-defined, lightweight mechanism to serve a business goal. 1

Pinterest, for example, could have the following microservices: user profile, follower, feed, search, photo upload, etc.

Service Discovery

Systems such as Consul, Etcd, and Zookeeper can help services find each other by keeping track of registered names, addresses, and ports. Health checks help verify service integrity and are often done using an HTTP endpoint. Both Consul and Etcd have a built in key-value store that can be useful for storing config values and other shared data.

Disadvantage(s): application layer

Source(s) and further reading

Database


Source: Scaling up to your first 10 million users

Relational database management system (RDBMS)

A relational database like SQL is a collection of data items organized in tables.

ACID is a set of properties of relational database transactions.

There are many techniques to scale a relational database: master-slave replication, master-master replication, federation, sharding, denormalization, and SQL tuning.

Master-slave replication

The master serves reads and writes, replicating writes to one or more slaves, which serve only reads. Slaves can also replicate to additional slaves in a tree-like fashion. If the master goes offline, the system can continue to operate in read-only mode until a slave is promoted to a master or a new master is provisioned.


Source: Scalability, availability, stability, patterns

Disadvantage(s): master-slave replication

Master-master replication

Both masters serve reads and writes and coordinate with each other on writes. If either master goes down, the system can continue to operate with both reads and writes.


Source: Scalability, availability, stability, patterns

Disadvantage(s): master-master replication
Disadvantage(s): replication
Source(s) and further reading: replication

Federation


Source: Scaling up to your first 10 million users

Federation (or functional partitioning) splits up databases by function. For example, instead of a single, monolithic database, you could have three databases: forums, users, and products, resulting in less read and write traffic to each database and therefore less replication lag. Smaller databases result in more data that can fit in memory, which in turn results in more cache hits due to improved cache locality. With no single central master serializing writes you can write in parallel, increasing throughput.

Disadvantage(s): federation
Source(s) and further reading: federation

Sharding


Source: Scalability, availability, stability, patterns

Sharding distributes data across different databases such that each database can only manage a subset of the data. Taking a users database as an example, as the number of users increases, more shards are added to the cluster.

Similar to the advantages of federation, sharding results in less read and write traffic, less replication, and more cache hits. Index size is also reduced, which generally improves performance with faster queries. If one shard goes down, the other shards are still operational, although you'll want to add some form of replication to avoid data loss. Like federation, there is no single central master serializing writes, allowing you to write in parallel with increased throughput.

Common ways to shard a table of users is either through the user's last name initial or the user's geographic location.

Disadvantage(s): sharding
Source(s) and further reading: sharding

Denormalization

Denormalization attempts to improve read performance at the expense of some write performance. Redundant copies of the data are written in multiple tables to avoid expensive joins. Some RDBMS such as PostgreSQL and Oracle support materialized views which handle the work of storing redundant information and keeping redundant copies consistent.

Once data becomes distributed with techniques such as federation and sharding, managing joins across data centers further increases complexity. Denormalization might circumvent the need for such complex joins.

In most systems, reads can heavily outnumber writes 100:1 or even 1000:1. A read resulting in a complex database join can be very expensive, spending a significant amount of time on disk operations.

Disadvantage(s): denormalization
Source(s) and further reading: denormalization

SQL tuning

SQL tuning is a broad topic and many books have been written as reference.

It's important to benchmark and profile to simulate and uncover bottlenecks.

Benchmarking and profiling might point you to the following optimizations.

Tighten up the schema
Use good indices
Avoid expensive joins
Partition tables
Tune the query cache
Source(s) and further reading: SQL tuning

NoSQL

NoSQL is a collection of data items represented in a key-value store, document store, wide column store, or a graph database. Data is denormalized, and joins are generally done in the application code. Most NoSQL stores lack true ACID transactions and favor eventual consistency.

BASE is often used to describe the properties of NoSQL databases. In comparison with the CAP Theorem, BASE chooses availability over consistency.

In addition to choosing between SQL or NoSQL, it is helpful to understand which type of NoSQL database best fits your use case(s). We'll review key-value stores, document stores, wide column stores, and graph databases in the next section.

Key-value store

Abstraction: hash table

A key-value store generally allows for O(1) reads and writes and is often backed by memory or SSD. Data stores can maintain keys in lexicographic order, allowing efficient retrieval of key ranges. Key-value stores can allow for storing of metadata with a value.

Key-value stores provide high performance and are often used for simple data models or for rapidly-changing data, such as an in-memory cache layer. Since they offer only a limited set of operations, complexity is shifted to the application layer if additional operations are needed.

A key-value store is the basis for more complex systems such as a document store, and in some cases, a graph database.

Source(s) and further reading: key-value store

Document store

Abstraction: key-value store with documents stored as values

A document store is centered around documents (XML, JSON, binary, etc), where a document stores all information for a given object. Document stores provide APIs or a query language to query based on the internal structure of the document itself. Note, many key-value stores include features for working with a value's metadata, blurring the lines between these two storage types.

Based on the underlying implementation, documents are organized by collections, tags, metadata, or directories. Although documents can be organized or grouped together, documents may have fields that are completely different from each other.

Some document stores like MongoDB and CouchDB also provide a SQL-like language to perform complex queries. DynamoDB supports both key-values and documents.

Document stores provide high flexibility and are often used for working with occasionally changing data.

Source(s) and further reading: document store

Wide column store


Source: SQL & NoSQL, a brief history

Abstraction: nested map ColumnFamily<RowKey, Columns<ColKey, Value, Timestamp>>

A wide column store's basic unit of data is a column (name/value pair). A column can be grouped in column families (analogous to a SQL table). Super column families further group column families. You can access each column independently with a row key, and columns with the same row key form a row. Each value contains a timestamp for versioning and for conflict resolution.

Google introduced Bigtable as the first wide column store, which influenced the open-source HBase often-used in the Hadoop ecosystem, and Cassandra from Facebook. Stores such as BigTable, HBase, and Cassandra maintain keys in lexicographic order, allowing efficient retrieval of selective key ranges.

Wide column stores offer high availability and high scalability. They are often used for very large data sets.

Source(s) and further reading: wide column store

Graph database


Source: Graph database

Abstraction: graph

In a graph database, each node is a record and each arc is a relationship between two nodes. Graph databases are optimized to represent complex relationships with many foreign keys or many-to-many relationships.

Graphs databases offer high performance for data models with complex relationships, such as a social network. They are relatively new and are not yet widely-used; it might be more difficult to find development tools and resources. Many graphs can only be accessed with REST APIs.

Source(s) and further reading: graph

Source(s) and further reading: NoSQL

SQL or NoSQL


Source: Transitioning from RDBMS to NoSQL

Reasons for SQL:

Reasons for NoSQL:

Sample data well-suited for NoSQL:

Source(s) and further reading: SQL or NoSQL

Cache


Source: Scalable system design patterns

Caching improves page load times and can reduce the load on your servers and databases. In this model, the dispatcher will first lookup if the request has been made before and try to find the previous result to return, in order to save the actual execution.

Databases often benefit from a uniform distribution of reads and writes across its partitions. Popular items can skew the distribution, causing bottlenecks. Putting a cache in front of a database can help absorb uneven loads and spikes in traffic.

Client caching

Caches can be located on the client side (OS or browser), server side, or in a distinct cache layer.

CDN caching

CDNs are considered a type of cache.

Web server caching

Reverse proxies and caches such as Varnish can serve static and dynamic content directly. Web servers can also cache requests, returning responses without having to contact application servers.

Database caching

Your database usually includes some level of caching in a default configuration, optimized for a generic use case. Tweaking these settings for specific usage patterns can further boost performance.

Application caching

In-memory caches such as Memcached and Redis are key-value stores between your application and your data storage. Since the data is held in RAM, it is much faster than typical databases where data is stored on disk. RAM is more limited than disk, so cache invalidation algorithms such as least recently used (LRU) can help invalidate 'cold' entries and keep 'hot' data in RAM.

Redis has the following additional features:

There are multiple levels you can cache that fall into two general categories: database queries and objects:

Generally, you should try to avoid file-based caching, as it makes cloning and auto-scaling more difficult.

Caching at the database query level

Whenever you query the database, hash the query as a key and store the result to the cache. This approach suffers from expiration issues:

Caching at the object level

See your data as an object, similar to what you do with your application code. Have your application assemble the dataset from the database into a class instance or a data structure(s):

Suggestions of what to cache:

When to update the cache

Since you can only store a limited amount of data in cache, you'll need to determine which cache update strategy works best for your use case.

Cache-aside


Source: From cache to in-memory data grid

The application is responsible for reading and writing from storage. The cache does not interact with storage directly. The application does the following:

def get_user(self, user_id):
    user = cache.get("user.{0}", user_id)
    if user is None:
        user = db.query("SELECT * FROM users WHERE user_id = {0}", user_id)
        if user is not None:
            key = "user.{0}".format(user_id)
            cache.set(key, json.dumps(user))
    return user

Memcached is generally used in this manner.

Subsequent reads of data added to cache are fast. Cache-aside is also referred to as lazy loading. Only requested data is cached, which avoids filling up the cache with data that isn't requested.

Disadvantage(s): cache-aside

Write-through


Source: Scalability, availability, stability, patterns

The application uses the cache as the main data store, reading and writing data to it, while the cache is responsible for reading and writing to the database:

Application code:

set_user(12345, {"foo":"bar"})

Cache code:

def set_user(user_id, values):
    user = db.query("UPDATE Users WHERE id = {0}", user_id, values)
    cache.set(user_id, user)

Write-through is a slow overall operation due to the write operation, but subsequent reads of just written data are fast. Users are generally more tolerant of latency when updating data than reading data. Data in the cache is not stale.

Disadvantage(s): write through

Write-behind (write-back)


Source: Scalability, availability, stability, patterns

In write-behind, the application does the following:

Disadvantage(s): write-behind

Refresh-ahead


Source: From cache to in-memory data grid

You can configure the cache to automatically refresh any recently accessed cache entry prior to its expiration.

Refresh-ahead can result in reduced latency vs read-through if the cache can accurately predict which items are likely to be needed in the future.

Disadvantage(s): refresh-ahead

Disadvantage(s): cache

Source(s) and further reading

Asynchronism


Source: Intro to architecting systems for scale

Asynchronous workflows help reduce request times for expensive operations that would otherwise be performed in-line. They can also help by doing time-consuming work in advance, such as periodic aggregation of data.

Message queues

Message queues receive, hold, and deliver messages. If an operation is too slow to perform inline, you can use a message queue with the following workflow:

The user is not blocked and the job is processed in the background. During this time, the client might optionally do a small amount of processing to make it seem like the task has completed. For example, if posting a tweet, the tweet could be instantly posted to your timeline, but it could take some time before your tweet is actually delivered to all of your followers.

Redis is useful as a simple message broker but messages can be lost.

RabbitMQ is popular but requires you to adapt to the 'AMQP' protocol and manage your own nodes.

Amazon SQS is hosted but can have high latency and has the possibility of messages being delivered twice.

Task queues

Tasks queues receive tasks and their related data, runs them, then delivers their results. They can support scheduling and can be used to run computationally-intensive jobs in the background.

Celery has support for scheduling and primarily has python support.

Back pressure

If queues start to grow significantly, the queue size can become larger than memory, resulting in cache misses, disk reads, and even slower performance. Back pressure can help by limiting the queue size, thereby maintaining a high throughput rate and good response times for jobs already in the queue. Once the queue fills up, clients get a server busy or HTTP 503 status code to try again later. Clients can retry the request at a later time, perhaps with exponential backoff.

Disadvantage(s): asynchronism

Source(s) and further reading

Communication


Source: OSI 7 layer model

Hypertext transfer protocol (HTTP)

HTTP is a method for encoding and transporting data between a client and a server. It is a request/response protocol: clients issue requests and servers issue responses with relevant content and completion status info about the request. HTTP is self-contained, allowing requests and responses to flow through many intermediate routers and servers that perform load balancing, caching, encryption, and compression.

A basic HTTP request consists of a verb (method) and a resource (endpoint). Below are common HTTP verbs:

Verb Description Idempotent* Safe Cacheable
GET Reads a resource Yes Yes Yes
POST Creates a resource or trigger a process that handles data No No Yes if response contains freshness info
PUT Creates or replace a resource Yes No No
PATCH Partially updates a resource No No Yes if response contains freshness info
DELETE Deletes a resource Yes No No

*Can be called many times without different outcomes.

HTTP is an application layer protocol relying on lower-level protocols such as TCP and UDP.

Source(s) and further reading: HTTP

Transmission control protocol (TCP)


Source: How to make a multiplayer game

TCP is a connection-oriented protocol over an IP network. Connection is established and terminated using a handshake. All packets sent are guaranteed to reach the destination in the original order and without corruption through:

If the sender does not receive a correct response, it will resend the packets. If there are multiple timeouts, the connection is dropped. TCP also implements flow control and congestion control. These guarantees cause delays and generally result in less efficient transmission than UDP.

To ensure high throughput, web servers can keep a large number of TCP connections open, resulting in high memory usage. It can be expensive to have a large number of open connections between web server threads and say, a memcached server. Connection pooling can help in addition to switching to UDP where applicable.

TCP is useful for applications that require high reliability but are less time critical. Some examples include web servers, database info, SMTP, FTP, and SSH.

Use TCP over UDP when:

User datagram protocol (UDP)


Source: How to make a multiplayer game

UDP is connectionless. Datagrams (analogous to packets) are guaranteed only at the datagram level. Datagrams might reach their destination out of order or not at all. UDP does not support congestion control. Without the guarantees that TCP support, UDP is generally more efficient.

UDP can broadcast, sending datagrams to all devices on the subnet. This is useful with DHCP because the client has not yet received an IP address, thus preventing a way for TCP to stream without the IP address.

UDP is less reliable but works well in real time use cases such as VoIP, video chat, streaming, and realtime multiplayer games.

Use UDP over TCP when:

Source(s) and further reading: TCP and UDP

Remote procedure call (RPC)


Source: Crack the system design interview

In an RPC, a client causes a procedure to execute on a different address space, usually a remote server. The procedure is coded as if it were a local procedure call, abstracting away the details of how to communicate with the server from the client program. Remote calls are usually slower and less reliable than local calls so it is helpful to distinguish RPC calls from local calls. Popular RPC frameworks include Protobuf, Thrift, and Avro.

RPC is a request-response protocol:

Sample RPC calls:

GET /someoperation?data=anId

POST /anotheroperation
{
  "data":"anId";
  "anotherdata": "another value"
}

RPC is focused on exposing behaviors. RPCs are often used for performance reasons with internal communications, as you can hand-craft native calls to better fit your use cases.

Choose a native library (aka SDK) when:

HTTP APIs following REST tend to be used more often for public APIs.

Disadvantage(s): RPC

Representational state transfer (REST)

REST is an architectural style enforcing a client/server model where the client acts on a set of resources managed by the server. The server provides a representation of resources and actions that can either manipulate or get a new representation of resources. All communication must be stateless and cacheable.

There are four qualities of a RESTful interface:

Sample REST calls:

GET /someresources/anId

PUT /someresources/anId
{"anotherdata": "another value"}

REST is focused on exposing data. It minimizes the coupling between client/server and is often used for public HTTP APIs. REST uses a more generic and uniform method of exposing resources through URIs, representation through headers, and actions through verbs such as GET, POST, PUT, DELETE, and PATCH. Being stateless, REST is great for horizontal scaling and partitioning.

Disadvantage(s): REST

RPC and REST calls comparison

Operation RPC REST
Signup POST /signup POST /persons
Resign POST /resign
{
"personid": "1234"
}
DELETE /persons/1234
Read a person GET /readPerson?personid=1234 GET /persons/1234
Read a person’s items list GET /readUsersItemsList?personid=1234 GET /persons/1234/items
Add an item to a person’s items POST /addItemToUsersItemsList
{
"personid": "1234";
"itemid": "456"
}
POST /persons/1234/items
{
"itemid": "456"
}
Update an item POST /modifyItem
{
"itemid": "456";
"key": "value"
}
PUT /items/456
{
"key": "value"
}
Delete an item POST /removeItem
{
"itemid": "456"
}
DELETE /items/456

Source: Do you really know why you prefer REST over RPC

Source(s) and further reading: REST and RPC

Security

This section could use some updates. Consider contributing!

Security is a broad topic. Unless you have considerable experience, a security background, or are applying for a position that requires knowledge of security, you probably won't need to know more than the basics:

Source(s) and further reading

Appendix

You'll sometimes be asked to do 'back-of-the-envelope' estimates. For example, you might need to determine how long it will take to generate 100 image thumbnails from disk or how much memory a data structure will take. The Powers of two table and Latency numbers every programmer should know are handy references.

Powers of two table

Power           Exact Value         Approx Value        Bytes
---------------------------------------------------------------
7                             128
8                             256
10                           1024   1 thousand           1 KB
16                         65,536                       64 KB
20                      1,048,576   1 million            1 MB
30                  1,073,741,824   1 billion            1 GB
32                  4,294,967,296                        4 GB
40              1,099,511,627,776   1 trillion           1 TB

Source(s) and further reading

Latency numbers every programmer should know

Latency Comparison Numbers
--------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy            10,000   ns       10 us
Send 1 KB bytes over 1 Gbps network     10,000   ns       10 us
Read 4 KB randomly from SSD*           150,000   ns      150 us          ~1GB/sec SSD
Read 1 MB sequentially from memory     250,000   ns      250 us
Round trip within same datacenter      500,000   ns      500 us
Read 1 MB sequentially from SSD*     1,000,000   ns    1,000 us    1 ms  ~1GB/sec SSD, 4X memory
Disk seek                           10,000,000   ns   10,000 us   10 ms  20x datacenter roundtrip
Read 1 MB sequentially from 1 Gbps  10,000,000   ns   10,000 us   10 ms  40x memory, 10X SSD
Read 1 MB sequentially from disk    30,000,000   ns   30,000 us   30 ms 120x memory, 30X SSD
Send packet CA->Netherlands->CA    150,000,000   ns  150,000 us  150 ms

Notes
-----
1 ns = 10^-9 seconds
1 us = 10^-6 seconds = 1,000 ns
1 ms = 10^-3 seconds = 1,000 us = 1,000,000 ns

Handy metrics based on numbers above:

Latency numbers visualized

Source(s) and further reading

Additional system design interview questions

Common system design interview questions, with links to resources on how to solve each.

Question Reference(s)
Design a file sync service like Dropbox youtube.com
Design a search engine like Google queue.acm.org
stackexchange.com
ardendertat.com
stanford.edu
Design a scalable web crawler like Google quora.com
Design Google docs code.google.com
neil.fraser.name
Design a key-value store like Redis slideshare.net
Design a cache system like Memcached slideshare.net
Design a recommendation system like Amazon's hulu.com
ijcai13.org
Design a tinyurl system like Bitly n00tc0d3r.blogspot.com
Design a chat app like WhatsApp highscalability.com
Design a picture sharing system like Instagram highscalability.com
highscalability.com
Design the Facebook news feed function quora.com
quora.com
slideshare.net
Design the Facebook timeline function facebook.com
highscalability.com
Design the Facebook chat function erlang-factory.com
facebook.com
Design a graph search function like Facebook's facebook.com
facebook.com
facebook.com
Design a content delivery network like CloudFlare figshare.com
Design a trending topic system like Twitter's michael-noll.com
snikolov .wordpress.com
Design a random ID generation system blog.twitter.com
github.com
Return the top k requests during a time interval cs.ucsb.edu
wpi.edu
Design a system that serves data from multiple data centers highscalability.com
Design an online multiplayer card game indieflashblog.com
buildnewgames.com
Design a garbage collection system stuffwithstuff.com
washington.edu
Design an API rate limiter https://stripe.com/blog/
Add a system design question Contribute

Real world architectures

Articles on how real world systems are designed.


Source: Twitter timelines at scale

Don't focus on nitty gritty details for the following articles, instead:

Type System Reference(s)
Data processing MapReduce - Distributed data processing from Google research.google.com
Data processing Spark - Distributed data processing from Databricks slideshare.net
Data processing Storm - Distributed data processing from Twitter slideshare.net
Data store Bigtable - Distributed column-oriented database from Google harvard.edu
Data store HBase - Open source implementation of Bigtable slideshare.net
Data store Cassandra - Distributed column-oriented database from Facebook slideshare.net
Data store DynamoDB - Document-oriented database from Amazon harvard.edu
Data store MongoDB - Document-oriented database slideshare.net
Data store Spanner - Globally-distributed database from Google research.google.com
Data store Memcached - Distributed memory caching system slideshare.net
Data store Redis - Distributed memory caching system with persistence and value types slideshare.net
File system Google File System (GFS) - Distributed file system research.google.com
File system Hadoop File System (HDFS) - Open source implementation of GFS apache.org
Misc Chubby - Lock service for loosely-coupled distributed systems from Google research.google.com
Misc Dapper - Distributed systems tracing infrastructure research.google.com
Misc Kafka - Pub/sub message queue from LinkedIn slideshare.net
Misc Zookeeper - Centralized infrastructure and services enabling synchronization slideshare.net
Add an architecture Contribute

Company architectures

Company Reference(s)
Amazon Amazon architecture
Cinchcast Producing 1,500 hours of audio every day
DataSift Realtime datamining At 120,000 tweets per second
DropBox How we've scaled Dropbox
ESPN Operating At 100,000 duh nuh nuhs per second
Google Google architecture
Instagram 14 million users, terabytes of photos
What powers Instagram
Justin.tv Justin.Tv's live video broadcasting architecture
Facebook Scaling memcached at Facebook
TAO: Facebook’s distributed data store for the social graph
Facebook’s photo storage
How Facebook Live Streams To 800,000 Simultaneous Viewers
Flickr Flickr architecture
Mailbox From 0 to one million users in 6 weeks
Netflix A 360 Degree View Of The Entire Netflix Stack
Netflix: What Happens When You Press Play?
Pinterest From 0 To 10s of billions of page views a month
18 million visitors, 10x growth, 12 employees
Playfish 50 million monthly users and growing
PlentyOfFish PlentyOfFish architecture
Salesforce How they handle 1.3 billion transactions a day
Stack Overflow Stack Overflow architecture
TripAdvisor 40M visitors, 200M dynamic page views, 30TB data
Tumblr 15 billion page views a month
Twitter Making Twitter 10000 percent faster
Storing 250 million tweets a day using MySQL
150M active users, 300K QPS, a 22 MB/S firehose
Timelines at scale
Big and small data at Twitter
Operations at Twitter: scaling beyond 100 million users
How Twitter Handles 3,000 Images Per Second
Uber How Uber scales their real-time market platform
Lessons Learned From Scaling Uber To 2000 Engineers, 1000 Services, And 8000 Git Repositories
WhatsApp The WhatsApp architecture Facebook bought for $19 billion
YouTube YouTube scalability
YouTube architecture

Company engineering blogs

Architectures for companies you are interviewing with.

Questions you encounter might be from the same domain.

Source(s) and further reading

Looking to add a blog? To avoid duplicating work, consider adding your company blog to the following repo:

Under development

Interested in adding a section or helping complete one in-progress? Contribute!

Credits

Credits and sources are provided throughout this repo.

Special thanks to:

Contact info

Feel free to contact me to discuss any issues, questions, or comments.

My contact info can be found on my GitHub page.

License

I am providing code and resources in this repository to you under an open source license. Because this is my personal repository, the license you receive to my code and resources is from me and not my employer (Facebook).

Copyright 2017 Donne Martin

Creative Commons Attribution 4.0 International License (CC BY 4.0)

http://creativecommons.org/licenses/by/4.0/