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system-design-primer - Readme

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一个Google Drive搜索引擎 https://zhao.pp.ua/
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The repository for high quality TypeScript type definitions.
Ruby on Rails
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Learn Python for free using open-source notebooks in Hebrew.
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Libra’s mission is to enable a simple global payment system and financial infrastructure that empowers billions of people.
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters,

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Creates a .csv file of all players in the English Player League with their respective team and total fantasy points
Repo for counting stars and contributing. Press F to pay respect to glorious developers.
GraalVM: Run Programs Faster Anywhere ?
TensorFlow's Visualization Toolkit
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.
An open source VR headset with SteamVR supports for $200
A custom RPC framework implemented by Netty+Kyro+Zookeeper.(一款基于 Netty+Kyro+Zookeeper 实现的自定义 RPC 框架-附详细实现过程和相关教程。)
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用于在 Heroku 上部署 V2Ray Websocket,本项目不宜做为长期使用之对策。
Becoming 1% better at data science everyday


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A curated list of awesome things related to HarmonyOS. 华为鸿蒙操作系统。
DP^3T Radar COVID fork
Statistical and Algorithmic Investing Strategies for Everyone
Radar COVID Verification Service
Native iOS app using DP^3T iOS sdk to handle Exposure Notification framework from Apple
Native Android app using DP^3T Android sdk to handle Exposure Notifications API from Google

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Bitcoin Core integration/staging tree
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flink learning blog. http://www.flink-learning.com 含 Flink 入门、概念、原理、实战、性能调优、源码解析等内容。涉及 Flink Connector、Metrics、Library、DataStream API、Table API & SQL 等内容的学习案例,还有 Flink 落地应用的大型项目案例(PVUV、日志存储、百亿数据实时去重、监
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To Be Top Javaer - Java工程师成神之路
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htop - an interactive process viewer
Making Docker management easy.
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Here you should find the best power supplies for your low-power projects
Computational Economics Course 2020 by Kenneth Judd
Vimium for macOS.
Script to remove Windows 10 bloatware.
Some Tutorials and Things to Do while Hunting That Vulnerability.
The all-in-one Red Team extension for Web Pentester ?

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WebRTC for the Curious: Go beyond the APIs
Matplot++: A C++ Graphics Library for Data Visualization ??
Learn to Code While Building Apps - The Complete Flutter Development Bootcamp
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2D and 3D physics engines focused on performances.
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Generates LaTeX math description from Python functions.


⚡️ Volt Bootstrap 5 Admin Dashboard Template with vanilla Javascript
Intel Wi-Fi Drivers
Packer is a tool for creating identical machine images for multiple platforms from a single source configuration.
Video discussing this curriculum:
A Google Chrome / Firefox extension that blocks NSFW images from the web pages that you load using TensorFlow JS.
Smart solution to solve sudoku in VR


Study guides for MIT's 15.003 Data Science Tools
This repository contains the code and implementation details of the CascadeTabNet paper "CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents"
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open source training courses about distributed database and distributed systemes
Godot Engine – Multi-platform 2D and 3D game engine

A hyperparameter optimization framework
A new bootable USB solution.
Alternative Factorio Friday Fan Facts, also known as Alt-F4
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Safe interop between Rust and C++

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a fetch written in posix shell without any external commands (linux only)
PanDownload Web, built with CloudFlare Workers
Minimum Viable Study Plan for Machine Learning Interviews from FAAG, Snapchat, LinkedIn.
? Everything is RSSible
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Amplify Framework provides a declarative and easy-to-use interface across different categories of cloud operations.
An entity framework for Go
Tensors and Dynamic neural networks in Python with strong GPU acceleration
(WIP)fork from ElemeFE/element ,A Vue.js 3.0 UI Toolkit for Web
? PostHog is developer-friendly, open-source product analytics.
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Hyperledger Fabric is an enterprise-grade permissioned distributed ledger framework for developing solutions and applications. Its modular and versatile design satisfies a broad range of industry use
Repository for Project Insight: NLP as a Service
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INFO-SPIDER 是一个集众多数据源于一身的爬虫工具箱?,旨在安全快捷的帮助用户拿回自己的数据,工具代码开源,流程透明。支持数据源包括GitHub、QQ邮箱、网易邮箱、阿里邮箱、新浪邮箱、Hotmail邮箱、Outlook邮箱、京东、淘宝、支付宝、中国移动、中国联通、中国电信、知乎、哔哩哔哩、网易云音乐、QQ好友、QQ群、生成朋友圈相册、浏览器浏览历史、12306、博客园、CSDN博客、开源
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Cut and paste your surroundings using AR

Network-wide ads & trackers blocking DNS server
Clean Object-oriented & Layered Architecture
? Diagram as Code for prototyping cloud system architectures
Object detection and instance segmentation toolkit based on PaddlePaddle.
Python library for converting Python calculations into rendered latex.
Complete Free Coding Bootcamp 2020 MERN Stack
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Archivy is a self-hosted knowledge repository that allows you to safely preserve useful content that contributes to your knowledge bank.
mall-swarm是一套微服务商城系统,采用了 Spring Cloud Hoxton & Alibaba、Spring Boot 2.3、Oauth2、MyBatis、Docker、Elasticsearch等核心技术,同时提供了基于Vue的管理后台方便快速搭建系统。mall-swarm在电商业务的基础集成了注册中心、配置中心、监控中心、网关等系统功能。文档齐全,附带全套Spring Clou
Umami is a simple, fast, website analytics alternative to Google Analytics.
Android sources for the Dutch Covid19 Notification App
Material del curso IIC2233 Programación Avanzada ?
A very simple script to connect locast to Plex's live tv/dvr feature.
H1st AI solves the critical “cold-start” problem of Industrial AI: encoding human expertise to augment the lack of data, while building a smooth transition toward a machine-learning future. This probl
A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training
A modern, C++-native, header-only, test framework for unit-tests, TDD and BDD - using C++11, C++14, C++17 and later (or C++03 on the Catch1.x branch)
Ergonomic machine learning.
? Fast, simple and clean video downloader
Spot Micro Quadripeg Project
✅ Solutions to LeetCode by Go, 100% test coverage, runtime beats 100% / LeetCode 题解
Repositório contendo todos os desafios dos módulos do Bootcamp Gostack
A static devirtualizer for VMProtect x64 3.x. powered by VTIL.
Full-sized drag & drop event calendar
Generates LaTeX math description from Python functions.
This is the frontend (VueJS) of the Youtube clone called VueTube.
VueTube is a YouTube clone built with nodejs, expressjs & mongodb. This is the RESTful API repository.


A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.


A MongoDB UI built with Electron
Software modular synth


A web browser engine for the space age ?

Draft of the fastai book
Visual localization made easy
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
渗透测试有关的POC、EXP、脚本、提权、小工具等,欢迎补充、完善---About penetration-testing python-script poc getshell csrf xss cms php-getshell domainmod-xss penetration-testing-poc csrf-webshell cobub-razor cve rce sql sql-poc p
A list of commands, scripts, resources, and more that I have gathered and attempted to consolidate for use as OSCP (and more) study material. Commands in 'Usefulcommands' Keepnote. Bookmarks and readi
Drogon: A C++14/17 based HTTP web application framework running on Linux/macOS/Unix/Windows
Open-source live customer chat
Build interactive, publication-quality documents from Jupyter Notebooks
Collection of awesome Java project on Github(Github 上非常棒的 Java 开源项目集合).
A free video streaming service that runs on a ESP32
The Servo Browser Engine
Front-end framework with a built-in dark mode, designed for rapidly building beautiful dashboards and product pages.
EventNative is an open-source data collection framework
Go library for accessing the GitHub API
A stablizing reserve currency protocol
OpenMMLab's next-generation platform for general 3D object detection.
? Hunt down social media accounts by username across social networks
Best Practices, code samples, and documentation for Computer Vision.
? Clean Code concepts adapted for JavaScript
Build a full-featured administrative interface in ten minutes
GeoSn0w's OpenJailbreak Project, an open-source iOS 11 to iOS 13 Jailbreak project & vault.
Azure Quickstart Templates
A new Node.js resource built using Gatsby.js with React.js, TypeScript, Emotion, and Remark.
KOOM is an OOM killer on mobile platform by Kwai.
A refreshingly simple data-driven game engine built in Rust
Pytorch?? is delicious, just eat it! ??
? 2,000,000+ Unsplash images made available for research and machine learning
Malwoverview is a first response tool to perform an initial and quick triage in a directory containing malware samples, specific malware sample, suspect URL and domains. Additionally, it allows to dow
Streisand sets up a new server running your choice of WireGuard, OpenConnect, OpenSSH, OpenVPN, Shadowsocks, sslh, Stunnel, or a Tor bridge. It also generates custom instructions for all of these serv
Intel Owl: analyze files, domains, IPs in multiple ways from a single API at scale
The GitHub Archive Program & Arctic Code Vault
Complete container management platform
Using TLS 1.3 to evade censors, bypass network defenses, and blend in with the noise
? Path to a free self-taught education in Data Science!
Generate responsive pages and apps on Tailwind, Flutter and SwiftUI.

List of open source tools for AWS security: defensive, offensive, auditing, DFIR, etc.
Deep neural network to extract intelligent information from invoice documents.
Replacement icons for popular apps in the style of macOS Big Sur
[Open Source]. The improved version of AnimeGAN.
Bluezone - Bảo vệ mình, bảo vệ cộng đồng
A curated list of amazingly awesome open source sysadmin resources inspired by Awesome PHP.
An open-source project includes many scripts with no Access Token needed for Facebook users by directly manipulating the DOM.
MCinaBox - A Minecraft Java Edition Launcher on Android
Foundation is a flexible, modular, and composable framework to model socio-economic behaviors and dynamics with both agents and governments. This framework can be used in conjunction with reinforcemen
Adds TikTok Shares for you.
The easiest way to automate your data
A collection of scripts to flash Tuya IoT devices to alternative firmwares
Crush is an attempt to make a command line shell that is also a powerful modern programming language.
Performant type-checking for python.
Polkadot Node Implementation

Cloud native service mesh for the rest of us.
V2rayU,基于v2ray核心的mac版客户端,用于科学上网,使用swift编写,支持vmess,shadowsocks,socks5等服务协议,支持订阅, 支持二维码,剪贴板导入,手动配置,二维码分享等

A set of free MIT-licensed high-quality SVG icons for UI development.
A framework for building native apps with React.
Console-based user interface toolkit for .NET applications.
Atlas: End-to-End 3D Scene Reconstruction from Posed Images
AWS SDK for the Go programming language.
Curated applications for Kubernetes
Seamless operability between C++11 and Python
MediaPipe is the simplest way for researchers and developers to build world-class ML solutions and applications for mobile, edge, cloud and the web.
A proposta do projeto é uma aplicação que possa ligar quem deseja aprender, com quer ensinar. É possível encontrar alunos para o que você leciona, ou encontrar o professor para aquela matéria que você
Add-on for real-time collaboration in Blender.
This repository holds the device support files for the iOS, and I will update it regularly.
Parsing gigabytes of JSON per second
A declarative JavaScript library for application development using cloud services.
An iOS library to natively render After Effects vector animations
All files for 6 axis robot arm with cycloidal gearboxes .

A repository for All algorithms implemented in Javascript (for educational purposes only)
Show your latest blog posts from any sources or StackOverflow activity on your GitHub profile/project readme automatically using the RSS feed
Questions to ask the company during your interview
An open-source platform for making universal native apps with React. Expo runs on Android, iOS, and the web.
955 不加班的公司名单 - 工作 955,work–life balance (工作与生活的平衡)
✅ Curated list of resources for college students
An open-source big data platform designed and optimized for the Internet of Things (IoT).
Jazzy theme for Django
Full stack, modern web application generator. Using FastAPI, PostgreSQL as database, Docker, automatic HTTPS and more.
? Some useful websites for programmers.
Linux/OSX/FreeBSD resource monitor
Enumerate and disable common sources of telemetry used by AV/EDR.
InstaGrabber, the open-source Instagram client for Android. Originally by @AwaisKing.

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

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

? Instagram Bot - Tool for automated Instagram interactions
A cat(1) clone with wings.
A Deep Learning based project for colorizing and restoring old images (and video!)
this is downloadings of all educative.io free student subscription courses as pdf from GitHub student pack
? Small exercises to get you used to reading and writing Rust code!
Updated list of public BitTorrent trackers
React Native client application for COVID Shield on iOS and Android
A collection of improved binary search algorithms.

Port of the double tap on back of device feature from Android 11 to any armv8 Android device
Starter files, final projects and FAQ for my Complete JavaScript course
Official open source SVG icon library for Bootstrap.
OneFlow is a performance-centered and open-source deep learning framework.
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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


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:


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).


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.

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


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

View exercise and solution


Design a web crawler

View exercise and solution


Design Mint.com

View exercise and solution


Design the data structures for a social network

View exercise and solution


Design a key-value store for a search engine

View exercise and solution


Design Amazon's sales ranking by category feature

View exercise and solution


Design a system that scales to millions of users on AWS

View exercise and solution


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

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


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.



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.


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


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.


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


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


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


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 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 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


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


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.


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


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


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


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


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
  "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


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


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

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
Design a scalable web crawler like Google quora.com
Design Google docs code.google.com
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
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
Design the Facebook news feed function quora.com
Design the Facebook timeline function facebook.com
Design the Facebook chat function erlang-factory.com
Design a graph search function like Facebook's 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
Return the top k requests during a time interval cs.ucsb.edu
Design a system that serves data from multiple data centers highscalability.com
Design an online multiplayer card game indieflashblog.com
Design a garbage collection system stuffwithstuff.com
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 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.


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)