Question: How to: Monitoring

Answer:
*Monitoring software.*

* [Alerta](https://github.com/guardian/alerta) - Distributed, scaleable and flexible monitoring system.
* [Canopsis](http://www.canopsis.org) - Opensource Hypervision and Data Aggregation Software
* [Cacti](http://www.cacti.net) - Web-based network monitoring and graphing tool.
* [Cabot](http://cabotapp.com/) - Monitoring and alerts, similar to PagerDuty.
* [Centreon](http://www.centreon.com) - IT infrastructure and application monitoring for service performance.
* [check_mk](http://mathias-kettner.com/check_mk.html) - Collection of extensions for Nagios.
* [Flapjack](http://flapjack.io/) - Monitoring notification routing & event processing system.
* [Icinga](https://www.icinga.org/) - Fork of Nagios.
* [LibreNMS](https://github.com/librenms/librenms/) - fork of Observium.
* [Monit](http://mmonit.com/monit/#home) - Small Open Source utility for managing and monitoring Unix systems.
* [Munin](http://munin-monitoring.org/) - Networked resource monitoring tool.
* [Naemon](http://www.naemon.org/) - Network monitoring tool based on the Nagios 4 core with performance enhancements and new features.
* [Nagios](http://www.nagios.org/) - Computer system, network and infrastructure monitoring software application.
* [Node-Bell](https://github.com/eleme/node-bell) - Real-time anomalies detection for periodic time series, metrics monitor.
* [Observium](http://www.observium.org/) - SNMP monitoring for servers and networking devices. Runs on linux.
* [Opsview](http://www.opsview.com/solutions/core) - Based on Nagios 4, Opsview Core is ideal for small IT and test environments.
* [Riemann](http://riemann.io/) - Flexible and fast events processor allowing complex events/metrics analysis.
* [Sensu](http://sensuapp.org/) - Open source monitoring framework.
* [Sentry](https://getsentry.com/) - Application monitoring, event logging and aggregation.
* [Serverstats](https://sourceforge.net/projects/serverstats.berlios/) - A simple tool for creating graphs using rrdtool. ([source on github](https://github.com/ddanier/serverstats))
* [Seyren](https://github.com/scobal/seyren) - An alerting dashboard for Graphite.
* [Shinken](http://www.shinken-monitoring.org/) - Another monitoring framework.
* [Xymon](http://www.xymon.com/) - Network monitoring inspired by Big Brother.
* [Zabbix](http://www.zabbix.com/) - Enterprise-class software for monitoring of networks and applications.
* [Zenoss](http://community.zenoss.org) - Application, server, and network management platform based on Zope.

*Monitoring dashboards.*

* [Adagios](http://adagios.org/) - Web based Nagios configuration interface.
* [Dash](https://github.com/afaqurk/linux-dash) - A low-overhead monitoring web dashboard for a GNU/Linux machine.
* [Thruk](http://www.thruk.org/) - Multibackend monitoring web interface with support for Naemon, Nagios, Icinga and Shinken.
* [Uchiwa](https://uchiwa.io) - Simple dashboard for the Sensu monitoring framework.

*Monitoring distributions.*

* [OMD](http://omdistro.org/) - The Open Monitoring Distribution.


Question: How to: Metric & Metric Collection

Answer:
*Metric gathering and display software.*

* [Collectd](http://collectd.org/) - System statistic collection daemon.
* [Collectl](http://collectl.sourceforge.net/) - High precision system performance metrics collecting tool.
* [~~dashing~~](http://dashing.io/) - __No Longer Maintained__ - Ruby gem that allows for rapid statistical dashboard development. An all HTML5 approach allows for big screen displays in data centers or conference rooms.
* [Smashing](https://github.com/Smashing/smashing) - Ruby gem that allows for rapid statistical dashboard development. An all HTML5 approach allows for big screen displays in data centers or conference rooms. Fork of Dashing.
* [Diamond](https://github.com/BrightcoveOS/Diamond) - Python based statistic collection daemon.
* [Facette](http://facette.io) - Time series data visualization and graphing software written in Go.
* [Freeboard](https://github.com/Freeboard/freeboard) - A damn-sexy front-end real-time dashboard. Transforms raw JSON into delicious UI.
* [Ganglia](http://ganglia.sourceforge.net/) - High performance, scalable RRD based monitoring for grids and/or clusters of servers. Compatible with Graphite using a single collection process.
* [Grafana](http://grafana.org/) - A Graphite & InfluxDB Dashboard and Graph Editor.
* [Graphite](http://graphite.readthedocs.org/en/latest/) - Open source scalable graphing server.
* [InfluxDB](http://influxdb.com/) - Open source distributed time series database with no external dependencies.
* [KairosDB](https://code.google.com/p/kairosdb/) - Fast distributed scalable time series database, fork of OpenTSDB 1.x.
* [NetData](http://my-netdata.io) - Distributed real-time performance and health monitoring.
* [OpenTSDB](http://opentsdb.net/) - Store and server massive amounts of time series data without losing granularity.
* [Packetbeat](http://packetbeat.com/) - Captures network traffic and displays it in a custom Kibana dashboard for easy viewing.
* [Prometheus](http://prometheus.io/) - Service monitoring system and time series database.
* [RRDtool](http://oss.oetiker.ch/rrdtool/) - Open source industry standard, high performance data logging and graphing system for time series data.
* [Statsd](https://github.com/etsy/statsd/) - Application statistic listener.


Question: How to: Network Configuration Management

Answer:
*Network configuration management tools.*

* [GestióIP](http://www.gestioip.net/) - An automated web based IPv4/IPv6 IP Address Management tool.
* [NOC Project](http://nocproject.org/) - Scalable, high-performance and open-source [OSS](http://en.wikipedia.org/wiki/Operations_support_system) system for ISP, service and content providers.
* [Netbox](https://github.com/digitalocean/netbox) - IP address management (IPAM) and data center infrastructure management (DCIM) tool.
* [Oxidized](https://github.com/ytti/oxidized) - A modern take on network device configuration monitoring with web interface and GIT storage.
* [phpIPAM](http://phpipam.net/) - Open source IP address management with [PowerDNS](https://www.powerdns.com/) integration.
* [RANCID](http://www.shrubbery.net/rancid/) - Monitors network device's configuration and maintain history of changes.
* [rConfig](http://www.rconfig.com/) - Another network device configuration management tool.
* [trigger](https://github.com/trigger/trigger) - Robust network automation toolkit written in Python.


Question: How to: Newsletters

Answer:
*Newsletter software.*

* [DadaMail](http://dadamailproject.com/) - Mailing List Manager, written in Perl.
* [phpList](http://www.phplist.com/) - Newsletter manager written in PHP.


Question: How to: Service Discovery

Answer:
* [Consul](http://www.consul.io/) - Consul is a tool for service discovery, monitoring and configuration.
* [Doozerd](https://github.com/ha/doozerd) - Doozer is a highly-available, completely consistent store for small amounts of extremely important data.
* [ZooKeeper](http://zookeeper.apache.org/) - ZooKeeper is a centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services.


Question: How to: Software Containers

Answer:
*Operating system–level virtualization.*

* [Bitnami](https://bitnami.com/) - Produces open source installers or software packages for web applications and development stacks as well as virtual appliances.
* [Docker](http://www.docker.com/) - Open platform for developers and sysadmins to build, ship, and run distributed applications.
* [LXC](https://linuxcontainers.org/lxc/) -  Userspace interface for the Linux kernel containment features.
* [LXD](https://linuxcontainers.org/lxd/) - LXD is a container "hypervisor".
* [OpenVZ](http://openvz.org) - Container-based virtualization for Linux.
* [Docker Compose](https://docs.docker.com/compose/) - Fast, isolated development environments using Docker.
* [Singularity](http://singularity.lbl.gov/) - Flexible containers without root.


Question: How to: Statistics

Answer:
*Analytics software.*

* [Analog](http://www.web42.com/analog/) - Logfile Analyser.
* [AWStats](http://www.awstats.org/) - Generates web, streaming, ftp or mail server statistics graphically.
* [GoAccess](http://goaccess.io/) - Real-time web log analyzer and interactive viewer that runs in a terminal.
* [Open Web Analytics](http://www.openwebanalytics.com/) - Add web analytics to websites using JS, PHP or REST APIs.
* [Piwik](http://piwik.org/) - Web analytics application.
* [Webalizer](http://www.webalizer.org/) - Fast, free web server log file analysis program.


Question: How to: Status Pages

Answer: * [Cachet](https://cachethq.io) - An open source status page system written in PHP.


Question: How to: Ticketing systems

Answer:
*Web-based ticketing system.*

* [Bugzilla](http://www.bugzilla.org/) - General-purpose bugtracker and testing tool originally developed and used by the Mozilla project.
* [Cerb](http://www.cerberusweb.com/) - Group-based e-mail management project.
* [Flyspray](http://flyspray.org) - Web-based bug tracking system written in PHP.
* [MantisBT](http://www.mantisbt.org/) - Web-based bug tracking system.
* [osTicket](http://osticket.com/) - Simple support ticket system.
* [OTRS](http://www.otrs.com/) - Trouble ticket system for assigning tickets to incoming queries and tracking further communications.
* [Redmine](http://www.redmine.org/) - Open source project management/ticketing web application written in Ruby.
* [Request Tracker](http://www.bestpractical.com/rt/) - Ticket-tracking system written in Perl.
* [TheBugGenie](http://www.thebuggenie.com) - Ticket system with extensive user rights system.


Question: How to: Troubleshooting

Answer:
*Troubleshooting tools.*

* [mitmproxy](http://mitmproxy.org/) - A Python tool used for intercepting, viewing and modifying network traffic. Invaluable in troubleshooting certain problems.
* [Sysdig](http://www.sysdig.org/) - Capture system state and activity from a running Linux instance, then save, filter and analyze.
* [Wireshark](http://www.wireshark.org/) - The world's foremost network protocol analyzer.

*Troubleshooting distributions.*

* [Trinity Rescue Kit](http://trinityhome.org) - Linux Live CD for general computer troubleshooting.


Question: How to: Project Management

Answer:
*Web-based project management and bug tracking systems.*

* [ChiliProject](https://www.chiliproject.org) - Fork of Redmine.
* [GitBucket](https://github.com/takezoe/gitbucket) Clone of GitHub written in Scala; single jar install.
* [GitLab](https://www.gitlab.com/) - Clone of GitHub written in Ruby.
* [Gogs](http://gogs.io/) - Self-hosted Git service written in Go.
* [OpenProject](https://www.openproject.org) - Project collaboration with open source.
* [Phabricator](http://phabricator.org/) Written in PHP.
* [Redmine](http://www.redmine.org/) - Written in ruby on rails.
* [Taiga](https://taiga.io/) - Agile, Free, Open Source Project Management Tool based on the Kanban and Scrum methods.
* [The Bug Genie](http://www.thebuggenie.com/) - Written in PHP.
* [Trac](http://trac.edgewall.org/) - Written in python.


Question: How to: Version control

Answer:
*Software versioning and revision control.*

* [Fossil](http://www.fossil-scm.org/) - Distributed version control with built-in wiki and bug tracking.
* [Git](http://git-scm.com/) - Distributed revision control and source code management (SCM) with an emphasis on speed.
* [GNU Bazaar](http://bazaar.canonical.com/) - Distributed revision control system sponsored by Canonical.
* [Mercurial](http://mercurial.selenic.com/) - Another distributed revision control.
* [Subversion](http://subversion.apache.org/) - Client-server revision control system.


Question: How to: Virtualization

Answer:
*Virtualization software.*

* [Archipel](http://archipelproject.org/) - XMPP based virtualization management platform.
* [Ganeti](https://code.google.com/p/ganeti/) - Cluster virtual server management software tool built on top of KVM and Xen.
* [KVM](http://www.linux-kvm.org) - Linux kernel virtualization infrastructure.
* [OpenNebula](http://opennebula.org/) - Flexible enterprise cloud made simple.
* [oVirt](http://www.ovirt.org/) - Manages virtual machines, storage and virtual networks.
* [Packer](http://www.packer.io/) - A tool for creating identical machine images for multiple platforms from a single source configuration.
* [Proxmox VE](https://www.proxmox.com/proxmox-ve) - Complete open source virtualization management solution.
* [QEMU](http://www.qemu.org/) - QEMU is a generic and open source machine emulator and virtualizer.
* [Vagrant](https://www.vagrantup.com/) - Tool for building complete development environments.
* [VirtualBox](https://www.virtualbox.org/) - Virtualization product from Oracle Corporation.
* [Xen](http://www.xenproject.org/) - Virtual machine monitor for 32/64 bit Intel / AMD (IA 64) and PowerPC 970 architectures.


Question: How to: Repositories

Answer:
*Debian-based distributions.*

* [Dotdeb](http://www.dotdeb.org/) - Repository with LAMP updated packages for Debian.

*RPM-based distributions.*

* [ElRepo](http://elrepo.org/tiki/tiki-index.php) - Community Repo for Enterprise Linux (RHEL, CentOS, etc).
* [EPEL](https://fedoraproject.org/wiki/EPEL) - Repository for RHEL and compatibles (CentOS, Scientific Linux).
* [Remi](http://rpms.famillecollet.com/) - Repository with LAMP updated packages for RHEL/Centos/Fedora.
* [Software Collections](https://www.softwarecollections.org) - Community Release of [Red Hat Software Collections](https://access.redhat.com/documentation/en-US/Red_Hat_Software_Collections/). Provides updated packages of Ruby, Python, etc. for CentOS/Scientific Linux 6.x.


Question: How to: The System Design Primer


Question: How to: Motivation

Answer:
> Learn how to design large-scale systems.
>
> Prep for the system design interview.


Question: How to: Learn how to design large-scale systems

Answer:
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.


Question: How to: Learn from the open source community

Answer:
This is a continually updated, open source project.

[Contributions](#contributing) are welcome!


Question: How to: Prep for the system design interview

Answer:
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:

* [Study guide](#study-guide)
* [How to approach a system design interview question](#how-to-approach-a-system-design-interview-question)
* [System design interview questions, **with solutions**](#system-design-interview-questions-with-solutions)
* [Object-oriented design interview questions, **with solutions**](#object-oriented-design-interview-questions-with-solutions)
* [Additional system design interview questions](#additional-system-design-interview-questions)


Question: How to: Anki flashcards

Answer:
The provided [Anki flashcard decks](https://apps.ankiweb.net/) use spaced repetition to help you retain key system design concepts.

* [System design deck](https://github.com/donnemartin/system-design-primer/tree/master/resources/flash_cards/System%20Design.apkg)
* [System design exercises deck](https://github.com/donnemartin/system-design-primer/tree/master/resources/flash_cards/System%20Design%20Exercises.apkg)
* [Object oriented design exercises deck](https://github.com/donnemartin/system-design-primer/tree/master/resources/flash_cards/OO%20Design.apkg)

Great for use while on-the-go.


Question: How to: Coding Resource: Interactive Coding Challenges

Answer:
Looking for resources to help you prep for the [**Coding Interview**](https://github.com/donnemartin/interactive-coding-challenges)?
Check out the sister repo [**Interactive Coding Challenges**](https://github.com/donnemartin/interactive-coding-challenges), which contains an additional Anki deck:

* [Coding deck](https://github.com/donnemartin/interactive-coding-challenges/tree/master/anki_cards/Coding.apkg)


Question: How to: Contributing

Answer:
> Learn from the community.

Feel free to submit pull requests to help:

* Fix errors
* Improve sections
* Add new sections
* [Translate](https://github.com/donnemartin/system-design-primer/issues/28)

Content that needs some polishing is placed [under development](#under-development).

Review the [Contributing Guidelines](CONTRIBUTING.md).


Question: How to: Index of system design topics

Answer:
> Summaries of various system design topics, including pros and cons.  **Everything is a trade-off**.
>
> Each section contains links to more in-depth resources.
* [System design topics: start here](#system-design-topics-start-here)
    * [Step 1: Review the scalability video lecture](#step-1-review-the-scalability-video-lecture)
    * [Step 2: Review the scalability article](#step-2-review-the-scalability-article)
    * [Next steps](#next-steps)
* [Performance vs scalability](#performance-vs-scalability)
* [Latency vs throughput](#latency-vs-throughput)
* [Availability vs consistency](#availability-vs-consistency)
    * [CAP theorem](#cap-theorem)
        * [CP - consistency and partition tolerance](#cp---consistency-and-partition-tolerance)
        * [AP - availability and partition tolerance](#ap---availability-and-partition-tolerance)
* [Consistency patterns](#consistency-patterns)
    * [Weak consistency](#weak-consistency)
    * [Eventual consistency](#eventual-consistency)
    * [Strong consistency](#strong-consistency)
* [Availability patterns](#availability-patterns)
    * [Fail-over](#fail-over)
    * [Replication](#replication)
    * [Availability in numbers](#availability-in-numbers)
* [Domain name system](#domain-name-system)
* [Content delivery network](#content-delivery-network)
    * [Push CDNs](#push-cdns)
    * [Pull CDNs](#pull-cdns)
* [Load balancer](#load-balancer)
    * [Active-passive](#active-passive)
    * [Active-active](#active-active)
    * [Layer 4 load balancing](#layer-4-load-balancing)
    * [Layer 7 load balancing](#layer-7-load-balancing)
    * [Horizontal scaling](#horizontal-scaling)
* [Reverse proxy (web server)](#reverse-proxy-web-server)
    * [Load balancer vs reverse proxy](#load-balancer-vs-reverse-proxy)
* [Application layer](#application-layer)
    * [Microservices](#microservices)
    * [Service discovery](#service-discovery)
* [Database](#database)
    * [Relational database management system (RDBMS)](#relational-database-management-system-rdbms)
        * [Master-slave replication](#master-slave-replication)
        * [Master-master replication](#master-master-replication)
        * [Federation](#federation)
        * [Sharding](#sharding)
        * [Denormalization](#denormalization)
        * [SQL tuning](#sql-tuning)
    * [NoSQL](#nosql)
        * [Key-value store](#key-value-store)
        * [Document store](#document-store)
        * [Wide column store](#wide-column-store)
        * [Graph Database](#graph-database)
    * [SQL or NoSQL](#sql-or-nosql)
* [Cache](#cache)
    * [Client caching](#client-caching)
    * [CDN caching](#cdn-caching)
    * [Web server caching](#web-server-caching)
    * [Database caching](#database-caching)
    * [Application caching](#application-caching)
    * [Caching at the database query level](#caching-at-the-database-query-level)
    * [Caching at the object level](#caching-at-the-object-level)
    * [When to update the cache](#when-to-update-the-cache)
        * [Cache-aside](#cache-aside)
        * [Write-through](#write-through)
        * [Write-behind (write-back)](#write-behind-write-back)
        * [Refresh-ahead](#refresh-ahead)
* [Asynchronism](#asynchronism)
    * [Message queues](#message-queues)
    * [Task queues](#task-queues)
    * [Back pressure](#back-pressure)
* [Communication](#communication)
    * [Transmission control protocol (TCP)](#transmission-control-protocol-tcp)
    * [User datagram protocol (UDP)](#user-datagram-protocol-udp)
    * [Remote procedure call (RPC)](#remote-procedure-call-rpc)
    * [Representational state transfer (REST)](#representational-state-transfer-rest)
* [Security](#security)
* [Appendix](#appendix)
    * [Powers of two table](#powers-of-two-table)
    * [Latency numbers every programmer should know](#latency-numbers-every-programmer-should-know)
    * [Additional system design interview questions](#additional-system-design-interview-questions)
    * [Real world architectures](#real-world-architectures)
    * [Company architectures](#company-architectures)
    * [Company engineering blogs](#company-engineering-blogs)
* [Under development](#under-development)
* [Credits](#credits)
* [Contact info](#contact-info)
* [License](#license)


Question: How to: Study guide

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

![Imgur](images/OfVllex.png)

**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:

* How much experience you have
* What your technical background is
* What positions you are interviewing for
* Which companies you are interviewing with
* Luck

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 timeline** - Aim for **breadth** with system design topics.  Practice by solving **some** interview questions.
* **Medium timeline** - Aim for **breadth** and **some depth** with system design topics.  Practice by solving **many** interview questions.
* **Long timeline** - Aim for **breadth** and **more depth** with system design topics.  Practice by solving **most** interview questions.

| | Short | Medium | Long |
|---|---|---|---|
| Read through the [System design topics](#index-of-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](#company-engineering-blogs) for the companies you are interviewing with | :+1: | :+1: | :+1: |
| Read through a few [Real world architectures](#real-world-architectures) | :+1: | :+1: | :+1: |
| Review [How to approach a system design interview question](#how-to-approach-a-system-design-interview-question) | :+1: | :+1: | :+1: |
| Work through [System design interview questions with solutions](#system-design-interview-questions-with-solutions) | Some | Many | Most |
| Work through [Object-oriented design interview questions with solutions](#object-oriented-design-interview-questions-with-solutions) | Some | Many | Most |
| Review [Additional system design interview questions](#additional-system-design-interview-questions) | Some | Many | Most |


Question: How to: How to approach a system design interview question

Answer:
> 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](#system-design-interview-questions-with-solutions) section using the following steps.


Question: How to: Step 1: Outline use cases, constraints, and assumptions

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

* Who is going to use it?
* How are they going to use it?
* How many users are there?
* What does the system do?
* What are the inputs and outputs of the system?
* How much data do we expect to handle?
* How many requests per second do we expect?
* What is the expected read to write ratio?


Question: How to: Step 2: Create a high level design

Answer:
Outline a high level design with all important components.

* Sketch the main components and connections
* Justify your ideas


Question: How to: Step 3: Design core components

Answer:
Dive into details for each core component.  For example, if you were asked to [design a url shortening service](solutions/system_design/pastebin/README.md), discuss:

* Generating and storing a hash of the full url
    * [MD5](solutions/system_design/pastebin/README.md) and [Base62](solutions/system_design/pastebin/README.md)
    * Hash collisions
    * SQL or NoSQL
    * Database schema
* Translating a hashed url to the full url
    * Database lookup
* API and object-oriented design


Question: How to: Step 4: Scale the design

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

* Load balancer
* Horizontal scaling
* Caching
* Database sharding

Discuss potential solutions and trade-offs.  Everything is a trade-off.  Address bottlenecks using [principles of scalable system design](#index-of-system-design-topics).


Question: How to: Back-of-the-envelope calculations

Answer:
You might be asked to do some estimates by hand.  Refer to the [Appendix](#appendix) for the following resources:

* [Use back of the envelope calculations](http://highscalability.com/blog/2011/1/26/google-pro-tip-use-back-of-the-envelope-calculations-to-choo.html)
* [Powers of two table](#powers-of-two-table)
* [Latency numbers every programmer should know](#latency-numbers-every-programmer-should-know)


Question: How to: Source(s) and further reading

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

* [How to ace a systems design interview](https://web.archive.org/web/20210505130322/https://www.palantir.com/2011/10/how-to-rock-a-systems-design-interview/)
* [The system design interview](http://www.hiredintech.com/system-design)
* [Intro to Architecture and Systems Design Interviews](https://www.youtube.com/watch?v=ZgdS0EUmn70)
* [System design template](https://leetcode.com/discuss/career/229177/My-System-Design-Template)


Question: How to: System design interview questions with solutions

Answer:
> 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](solutions/system_design/pastebin/README.md) |
| Design the Twitter timeline and search (or Facebook feed and search) | [Solution](solutions/system_design/twitter/README.md) |
| Design a web crawler | [Solution](solutions/system_design/web_crawler/README.md) |
| Design Mint.com | [Solution](solutions/system_design/mint/README.md) |
| Design the data structures for a social network | [Solution](solutions/system_design/social_graph/README.md) |
| Design a key-value store for a search engine | [Solution](solutions/system_design/query_cache/README.md) |
| Design Amazon's sales ranking by category feature | [Solution](solutions/system_design/sales_rank/README.md) |
| Design a system that scales to millions of users on AWS | [Solution](solutions/system_design/scaling_aws/README.md) |
| Add a system design question | [Contribute](#contributing) |


Question: How to: Design Pastebin.com (or Bit.ly)

Answer:
[View exercise and solution](solutions/system_design/pastebin/README.md)

![Imgur](images/4edXG0T.png)


Question: How to: Design the Twitter timeline and search (or Facebook feed and search)

Answer:
[View exercise and solution](solutions/system_design/twitter/README.md)

![Imgur](images/jrUBAF7.png)


Question: How to: Design a web crawler

Answer:
[View exercise and solution](solutions/system_design/web_crawler/README.md)

![Imgur](images/bWxPtQA.png)


Question: How to: Design Mint.com

Answer:
[View exercise and solution](solutions/system_design/mint/README.md)

![Imgur](images/V5q57vU.png)


Question: How to: Design the data structures for a social network

Answer:
[View exercise and solution](solutions/system_design/social_graph/README.md)

![Imgur](images/cdCv5g7.png)


Question: How to: Design a key-value store for a search engine

Answer:
[View exercise and solution](solutions/system_design/query_cache/README.md)

![Imgur](images/4j99mhe.png)


Question: How to: Design Amazon's sales ranking by category feature

Answer:
[View exercise and solution](solutions/system_design/sales_rank/README.md)

![Imgur](images/MzExP06.png)


Question: How to: Design a system that scales to millions of users on AWS

Answer:
[View exercise and solution](solutions/system_design/scaling_aws/README.md)

![Imgur](images/jj3A5N8.png)


Question: How to: Object-oriented design interview questions with solutions

Answer:
> 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](solutions/object_oriented_design/hash_table/hash_map.ipynb)  |
| Design a least recently used cache | [Solution](solutions/object_oriented_design/lru_cache/lru_cache.ipynb)  |
| Design a call center | [Solution](solutions/object_oriented_design/call_center/call_center.ipynb)  |
| Design a deck of cards | [Solution](solutions/object_oriented_design/deck_of_cards/deck_of_cards.ipynb)  |
| Design a parking lot | [Solution](solutions/object_oriented_design/parking_lot/parking_lot.ipynb)  |
| Design a chat server | [Solution](solutions/object_oriented_design/online_chat/online_chat.ipynb)  |
| Design a circular array | [Contribute](#contributing)  |
| Add an object-oriented design question | [Contribute](#contributing) |


Question: How to: System design topics: start here

Answer:
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.


Question: How to: Step 1: Review the scalability video lecture

Answer:
[Scalability Lecture at Harvard](https://www.youtube.com/watch?v=-W9F__D3oY4)

* Topics covered:
    * Vertical scaling
    * Horizontal scaling
    * Caching
    * Load balancing
    * Database replication
    * Database partitioning


Question: How to: Step 2: Review the scalability article

Answer:
[Scalability](https://web.archive.org/web/20221030091841/http://www.lecloud.net/tagged/scalability/chrono)

* Topics covered:
    * [Clones](https://web.archive.org/web/20220530193911/https://www.lecloud.net/post/7295452622/scalability-for-dummies-part-1-clones)
    * [Databases](https://web.archive.org/web/20220602114024/https://www.lecloud.net/post/7994751381/scalability-for-dummies-part-2-database)
    * [Caches](https://web.archive.org/web/20230126233752/https://www.lecloud.net/post/9246290032/scalability-for-dummies-part-3-cache)
    * [Asynchronism](https://web.archive.org/web/20220926171507/https://www.lecloud.net/post/9699762917/scalability-for-dummies-part-4-asynchronism)


Question: How to: Next steps

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

* **Performance** vs **scalability**
* **Latency** vs **throughput**
* **Availability** vs **consistency**

Keep in mind that **everything is a trade-off**.

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


Question: How to: Performance vs scalability

Answer:
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:

* If you have a **performance** problem, your system is slow for a single user.
* If you have a **scalability** problem, your system is fast for a single user but slow under heavy load.


Question: How to: Latency vs throughput

Answer:
**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**.


Question: How to: CAP theorem

Answer:
Source: CAP theorem revisited
In a distributed computer system, you can only support two of the following guarantees:

* **Consistency** - Every read receives the most recent write or an error
* **Availability** - Every request receives a response, without guarantee that it contains the most recent version of the information
* **Partition Tolerance** - The system continues to operate despite arbitrary partitioning due to network failures

*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 to allow for [eventual consistency](#eventual-consistency) or when the system needs to continue working despite external errors.


Question: How to: Consistency patterns

Answer: 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](#cap-theorem) - Every read receives the most recent write or an error.


Question: How to: Weak consistency

Answer:
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.


Question: How to: Eventual consistency

Answer:
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.


Question: How to: Strong consistency

Answer:
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.


Question: How to: Availability patterns

Answer: There are two complementary patterns to support high availability: **fail-over** and **replication**.


Question: How to: Disadvantage(s): failover

Answer:
* Fail-over adds more hardware and additional complexity.
* There is a potential for loss of data if the active system fails before any newly written data can be replicated to the passive.


Question: How to: Replication

Answer:
#### Master-slave and master-master

This topic is further discussed in the [Database](#database) section:

* [Master-slave replication](#master-slave-replication)
* [Master-master replication](#master-master-replication)


Question: How to: Availability in numbers

Answer:
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%.


Question: How to: Domain name system

Answer:
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)](https://en.wikipedia.org/wiki/Time_to_live).

* **NS record (name server)** - Specifies the DNS servers for your domain/subdomain.
* **MX record (mail exchange)** - Specifies the mail servers for accepting messages.
* **A record (address)** - Points a name to an IP address.
* **CNAME (canonical)** - Points a name to another name or `CNAME` (example.com to www.example.com) or to an `A` record.

Services such as [CloudFlare](https://www.cloudflare.com/dns/) and [Route 53](https://aws.amazon.com/route53/) provide managed DNS services.  Some DNS services can route traffic through various methods:

* [Weighted round robin](https://www.jscape.com/blog/load-balancing-algorithms)
    * Prevent traffic from going to servers under maintenance
    * Balance between varying cluster sizes
    * A/B testing
* [Latency-based](https://docs.aws.amazon.com/Route53/latest/DeveloperGuide/routing-policy-latency.html)
* [Geolocation-based](https://docs.aws.amazon.com/Route53/latest/DeveloperGuide/routing-policy-geo.html)


Question: How to: Disadvantage(s): DNS

Answer:
* Accessing a DNS server introduces a slight delay, although mitigated by caching described above.
* DNS server management could be complex and is generally managed by [governments, ISPs, and large companies](http://superuser.com/questions/472695/who-controls-the-dns-servers/472729).
* DNS services have recently come under [DDoS attack](http://dyn.com/blog/dyn-analysis-summary-of-friday-october-21-attack/), preventing users from accessing websites such as Twitter without knowing Twitter's IP address(es).


Question: How to: Content delivery network

Answer:
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:

* Users receive content from data centers close to them
* Your servers do not have to serve requests that the CDN fulfills


Question: How to: Disadvantage(s): CDN

Answer:
* CDN costs could be significant depending on traffic, although this should be weighed with additional costs you would incur not using a CDN.
* Content might be stale if it is updated before the TTL expires it.
* CDNs require changing URLs for static content to point to the CDN.


Question: How to: Load balancer

Answer:
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:

* Preventing requests from going to unhealthy servers
* Preventing overloading resources
* Helping to eliminate a single point of failure

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

Additional benefits include:

* **SSL termination** - Decrypt incoming requests and encrypt server responses so backend servers do not have to perform these potentially expensive operations
    * Removes the need to install [X.509 certificates](https://en.wikipedia.org/wiki/X.509) on each server
* **Session persistence** - Issue cookies and route a specific client's requests to same instance if the web apps do not keep track of sessions

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

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

* Random
* Least loaded
* Session/cookies
* [Round robin or weighted round robin](https://www.g33kinfo.com/info/round-robin-vs-weighted-round-robin-lb)
* [Layer 4](#layer-4-load-balancing)
* [Layer 7](#layer-7-load-balancing)


Question: How to: Layer 4 load balancing

Answer: Layer 4 load balancers look at info at the [transport layer](#communication) 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)](https://www.nginx.com/resources/glossary/layer-4-load-balancing/).


Question: How to: Layer 7 load balancing

Answer:
Layer 7 load balancers look at the [application layer](#communication) 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.


Question: How to: Horizontal scaling

Answer:
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

* Scaling horizontally introduces complexity and involves cloning servers
    * Servers should be stateless: they should not contain any user-related data like sessions or profile pictures
    * Sessions can be stored in a centralized data store such as a [database](#database) (SQL, NoSQL) or a persistent [cache](#cache) (Redis, Memcached)
* Downstream servers such as caches and databases need to handle more simultaneous connections as upstream servers scale out


Question: How to: Disadvantage(s): load balancer

Answer:
* The load balancer can become a performance bottleneck if it does not have enough resources or if it is not configured properly.
* Introducing a load balancer to help eliminate a single point of failure results in increased complexity.
* A single load balancer is a single point of failure, configuring multiple load balancers further increases complexity.


Question: How to: Reverse proxy (web server)

Answer:
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:

* **Increased security** - Hide information about backend servers, blacklist IPs, limit number of connections per client
* **Increased scalability and flexibility** - Clients only see the reverse proxy's IP, allowing you to scale servers or change their configuration
* **SSL termination** - Decrypt incoming requests and encrypt server responses so backend servers do not have to perform these potentially expensive operations
    * Removes the need to install [X.509 certificates](https://en.wikipedia.org/wiki/X.509) on each server
* **Compression** - Compress server responses
* **Caching** - Return the response for cached requests
* **Static content** - Serve static content directly
    * HTML/CSS/JS
    * Photos
    * Videos
    * Etc


Question: How to: Load balancer vs reverse proxy

Answer:
* Deploying a load balancer is useful when you have multiple servers.  Often, load balancers  route traffic to a set of servers serving the same function.
* Reverse proxies can be useful even with just one web server or application server, opening up the benefits described in the previous section.
* Solutions such as NGINX and HAProxy can support both layer 7 reverse proxying and load balancing.


Question: How to: Disadvantage(s): reverse proxy

Answer:
* Introducing a reverse proxy results in increased complexity.
* A single reverse proxy is a single point of failure, configuring multiple reverse proxies (ie a [failover](https://en.wikipedia.org/wiki/Failover)) further increases complexity.


Question: How to: Application layer

Answer:
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](#asynchronism).


Question: How to: Microservices

Answer:
Related to this discussion are [microservices](https://en.wikipedia.org/wiki/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.


Question: How to: Service Discovery

Answer: Systems such as [Consul](https://www.consul.io/docs/index.html), [Etcd](https://coreos.com/etcd/docs/latest), and [Zookeeper](http://www.slideshare.net/sauravhaloi/introduction-to-apache-zookeeper) can help services find each other by keeping track of registered names, addresses, and ports.  [Health checks](https://www.consul.io/intro/getting-started/checks.html) help verify service integrity and are often done using an [HTTP](#hypertext-transfer-protocol-http) endpoint.  Both Consul and Etcd have a built in [key-value store](#key-value-store) that can be useful for storing config values and other shared data.


Question: How to: Disadvantage(s): application layer

Answer:
* Adding an application layer with loosely coupled services requires a different approach from an architectural, operations, and process viewpoint (vs a monolithic system).
* Microservices can add complexity in terms of deployments and operations.


Question: How to: Relational database management system (RDBMS)

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

**ACID** is a set of properties of relational database [transactions](https://en.wikipedia.org/wiki/Database_transaction).

* **Atomicity** - Each transaction is all or nothing
* **Consistency** - Any transaction will bring the database from one valid state to another
* **Isolation** - Executing transactions concurrently has the same results as if the transactions were executed serially
* **Durability** - Once a transaction has been committed, it will remain so

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

* Additional logic is needed to promote a slave to a master.
* See [Disadvantage(s): replication](#disadvantages-replication) for points related to **both** master-slave and master-master.

#### 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

* You'll need a load balancer or you'll need to make changes to your application logic to determine where to write.
* Most master-master systems are either loosely consistent (violating ACID) or have increased write latency due to synchronization.
* Conflict resolution comes more into play as more write nodes are added and as latency increases.
* See [Disadvantage(s): replication](#disadvantages-replication) for points related to **both** master-slave and master-master.

##### Disadvantage(s): replication

* There is a potential for loss of data if the master fails before any newly written data can be replicated to other nodes.
* Writes are replayed to the read replicas.  If there are a lot of writes, the read replicas can get bogged down with replaying writes and can't do as many reads.
* The more read slaves, the more you have to replicate, which leads to greater replication lag.
* On some systems, writing to the master can spawn multiple threads to write in parallel, whereas read replicas only support writing sequentially with a single thread.
* Replication adds more hardware and additional complexity.

##### Source(s) and further reading: replication

* [Scalability, availability, stability, patterns](http://www.slideshare.net/jboner/scalability-availability-stability-patterns/)
* [Multi-master replication](https://en.wikipedia.org/wiki/Multi-master_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

* Federation is not effective if your schema requires huge functions or tables.
* You'll need to update your application logic to determine which database to read and write.
* Joining data from two databases is more complex with a [server link](http://stackoverflow.com/questions/5145637/querying-data-by-joining-two-tables-in-two-database-on-different-servers).
* Federation adds more hardware and additional complexity.

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

* [Scaling up to your first 10 million users](https://www.youtube.com/watch?v=kKjm4ehYiMs)

#### Sharding
Source: Scalability, availability, stability, patterns
Sharding distr


Question: How to: SQL or NoSQL

Answer:
Source: Transitioning from RDBMS to NoSQL
Reasons for **SQL**:

* Structured data
* Strict schema
* Relational data
* Need for complex joins
* Transactions
* Clear patterns for scaling
* More established: developers, community, code, tools, etc
* Lookups by index are very fast

Reasons for **NoSQL**:

* Semi-structured data
* Dynamic or flexible schema
* Non-relational data
* No need for complex joins
* Store many TB (or PB) of data
* Very data intensive workload
* Very high throughput for IOPS

Sample data well-suited for NoSQL:

* Rapid ingest of clickstream and log data
* Leaderboard or scoring data
* Temporary data, such as a shopping cart
* Frequently accessed ('hot') tables
* Metadata/lookup tables

##### Source(s) and further reading: SQL or NoSQL

* [Scaling up to your first 10 million users](https://www.youtube.com/watch?v=kKjm4ehYiMs)
* [SQL vs NoSQL differences](https://www.sitepoint.com/sql-vs-nosql-differences/)


Question: How to: Client caching

Answer: Caches can be located on the client side (OS or browser), [server side](#reverse-proxy-web-server), or in a distinct cache layer.


Question: How to: CDN caching

Answer: [CDNs](#content-delivery-network) are considered a type of cache.


Question: How to: Web server caching

Answer: [Reverse proxies](#reverse-proxy-web-server) and caches such as [Varnish](https://www.varnish-cache.org/) can serve static and dynamic content directly.  Web servers can also cache requests, returning responses without having to contact application servers.


Question: How to: Database caching

Answer: 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.


Question: How to: Application caching

Answer:
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](https://en.wikipedia.org/wiki/Cache_algorithms) algorithms such as [least recently used (LRU)](https://en.wikipedia.org/wiki/Cache_replacement_policies#Least_recently_used_(LRU)) can help invalidate 'cold' entries and keep 'hot' data in RAM.

Redis has the following additional features:

* Persistence option
* Built-in data structures such as sorted sets and lists

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

* Row level
* Query-level
* Fully-formed serializable objects
* Fully-rendered HTML

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


Question: How to: Caching at the database query level

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

* Hard to delete a cached result with complex queries
* If one piece of data changes such as a table cell, you need to delete all cached queries that might include the changed cell


Question: How to: Caching at the object level

Answer:
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):

* Remove the object from cache if its underlying data has changed
* Allows for asynchronous processing: workers assemble objects by consuming the latest cached object

Suggestions of what to cache:

* User sessions
* Fully rendered web pages
* Activity streams
* User graph data


Question: How to: When to update the cache

Answer:
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:

* Look for entry in cache, resulting in a cache miss
* Load entry from the database
* Add entry to cache
* Return entry

```python
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](https://memcached.org/) 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

* Each cache miss results in three trips, which can cause a noticeable delay.
* Data can become stale if it is updated in the database.  This issue is mitigated by setting a time-to-live (TTL) which forces an update of the cache entry, or by using write-through.
* When a node fails, it is replaced by a new, empty node, increasing latency.

#### 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 adds/updates entry in cache
* Cache synchronously writes entry to data store
* Return

Application code:

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

Cache code:

```python
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

* When a new node is created due to failure or scaling, the new node will not cache entries until the entry is updated in the database.  Cache-aside in conjunction with write through can mitigate this issue.
* Most data written might never be read, which can be minimized with a TTL.

#### Write-behind (write-back)
Source: Scalability, availability, stability, patterns
In write-behind, the application does the following:

* Add/update entry in cache
* Asynchronously write entry to the data store, improving write performance

##### Disadvantage(s): write-behind

* There could be data loss if the cache goes down prior to its contents hitting the data store.
* It is more complex to implement write-behind than it is to implement cache-aside or write-through.

#### 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

* Not accurately predicting which items are likely to be needed in the future can result in reduced performance than without refresh-ahead.


Question: How to: Disadvantage(s): cache

Answer:
* Need to maintain consistency between caches and the source of truth such as the database through [cache invalidation](https://en.wikipedia.org/wiki/Cache_algorithms).
* Cache invalidation is a difficult problem, there is additional complexity associated with when to update the cache.
* Need to make application changes such as adding Redis or memcached.


Question: How to: Asynchronism

Answer:
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.


Question: How to: Message queues

Answer:
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:

* An application publishes a job to the queue, then notifies the user of job status
* A worker picks up the job from the queue, processes it, then signals the job is complete

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](https://redis.io/)** is useful as a simple message broker but messages can be lost.

**[RabbitMQ](https://www.rabbitmq.com/)** is popular but requires you to adapt to the 'AMQP' protocol and manage your own nodes.

**[Amazon SQS](https://aws.amazon.com/sqs/)** is hosted but can have high latency and has the possibility of messages being delivered twice.


Question: How to: Task queues

Answer:
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](https://docs.celeryproject.org/en/stable/)** has support for scheduling and primarily has python support.


Question: How to: Back pressure

Answer: 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](http://mechanical-sympathy.blogspot.com/2012/05/apply-back-pressure-when-overloaded.html) 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](https://en.wikipedia.org/wiki/Exponential_backoff).


Question: How to: Disadvantage(s): asynchronism

Answer: * Use cases such as inexpensive calculations and realtime workflows might be better suited for synchronous operations, as introducing queues can add delays and complexity.


Question: How to: Communication

Answer: Source: OSI 7 layer model


Question: How to: Hypertext transfer protocol (HTTP)

Answer:
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

* [What is HTTP?](https://www.nginx.com/resources/glossary/http/)
* [Difference between HTTP and TCP](https://www.quora.com/What-is-the-difference-between-HTTP-protocol-and-TCP-protocol)
* [Difference between PUT and PATCH](https://laracasts.com/discuss/channels/general-discussion/whats-the-differences-between-put-and-patch?page=1)


Question: How to: Transmission control protocol (TCP)

Answer:
Source: How to make a multiplayer game
TCP is a connection-oriented protocol over an [IP network](https://en.wikipedia.org/wiki/Internet_Protocol).  Connection is established and terminated using a [handshake](https://en.wikipedia.org/wiki/Handshaking).  All packets sent are guaranteed to reach the destination in the original order and without corruption through:

* Sequence numbers and [checksum fields](https://en.wikipedia.org/wiki/Transmission_Control_Protocol#Checksum_computation) for each packet
* [Acknowledgement](https://en.wikipedia.org/wiki/Acknowledgement_(data_networks)) packets and automatic retransmission

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](https://en.wikipedia.org/wiki/Flow_control_(data)) and [congestion control](https://en.wikipedia.org/wiki/Network_congestion#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](https://memcached.org/) server.  [Connection pooling](https://en.wikipedia.org/wiki/Connection_pool) 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:

* You need all of the data to arrive intact
* You want to automatically make a best estimate use of the network throughput


Question: How to: User datagram protocol (UDP)

Answer:
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](https://en.wikipedia.org/wiki/Dynamic_Host_Configuration_Protocol) 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:

* You need the lowest latency
* Late data is worse than loss of data
* You want to implement your own error correction

#### Source(s) and further reading: TCP and UDP

* [Networking for game programming](http://gafferongames.com/networking-for-game-programmers/udp-vs-tcp/)
* [Key differences between TCP and UDP protocols](http://www.cyberciti.biz/faq/key-differences-between-tcp-and-udp-protocols/)
* [Difference between TCP and UDP](http://stackoverflow.com/questions/5970383/difference-between-tcp-and-udp)
* [Transmission control protocol](https://en.wikipedia.org/wiki/Transmission_Control_Protocol)
* [User datagram protocol](https://en.wikipedia.org/wiki/User_Datagram_Protocol)
* [Scaling memcache at Facebook](http://www.cs.bu.edu/~jappavoo/jappavoo.github.com/451/papers/memcache-fb.pdf)


Question: How to: Remote procedure call (RPC)

Answer:
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](https://developers.google.com/protocol-buffers/), [Thrift](https://thrift.apache.org/), and [Avro](https://avro.apache.org/docs/current/).

RPC is a request-response protocol:

* **Client program** - Calls the client stub procedure.  The parameters are pushed onto the stack like a local procedure call.
* **Client stub procedure** - Marshals (packs) procedure id and arguments into a request message.
* **Client communication module** - OS sends the message from the client to the server.
* **Server communication module** - OS passes the incoming packets to the server stub procedure.
* **Server stub procedure** -  Unmarshalls the results, calls the server procedure matching the procedure id and passes the given arguments.
* The server response repeats the steps above in reverse order.

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:

* You know your target platform.
* You want to control how your "logic" is accessed.
* You want to control how error control happens off your library.
* Performance and end user experience is your primary concern.

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

#### Disadvantage(s): RPC

* RPC clients become tightly coupled to the service implementation.
* A new API must be defined for every new operation or use case.
* It can be difficult to debug RPC.
* You might not be able to leverage existing technologies out of the box.  For example, it might require additional effort to ensure [RPC calls are properly cached](https://web.archive.org/web/20170608193645/http://etherealbits.com/2012/12/debunking-the-myths-of-rpc-rest/) on caching servers such as [Squid](http://www.squid-cache.org/).


Question: How to: Representational state transfer (REST)

Answer:
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:

* **Identify resources (URI in HTTP)** - use the same URI regardless of any operation.
* **Change with representations (Verbs in HTTP)** - use verbs, headers, and body.
* **Self-descriptive error message (status response in HTTP)** - Use status codes, don't reinvent the wheel.
* **[HATEOAS](http://restcookbook.com/Basics/hateoas/) (HTML interface for HTTP)** - your web service should be fully accessible in a browser.

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](https://github.com/for-GET/know-your-http-well/blob/master/headers.md), 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

* With REST being focused on exposing data, it might not be a good fit if resources are not naturally organized or accessed in a simple hierarchy.  For example, returning all updated records from the past hour matching a particular set of events is not easily expressed as a path.  With REST, it is likely to be implemented with a combination of URI path, query parameters, and possibly the request body.
* REST typically relies on a few verbs (GET, POST, PUT, DELETE, and PATCH) which sometimes doesn't fit your use case.  For example, moving expired documents to the archive folder might not cleanly fit within these verbs.
* Fetching complicated resources with nested hierarchies requires multiple round trips between the client and server to render single views, e.g. fetching content of a blog entry and the comments on that entry. For mobile applications operating in variable network conditions, these multiple roundtrips are highly undesirable.
* Over time, more fields might be added to an API response and older clients will receive all new data fields, even those that they do not need, as a result, it bloats the payload size and leads to larger latencies.


Question: How to: RPC and REST calls comparison

Answer:
| 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

* [Do you really know why you prefer REST over RPC](https://apihandyman.io/do-you-really-know-why-you-prefer-rest-over-rpc/)
* [When are RPC-ish approaches more appropriate than REST?](http://programmers.stackexchange.com/a/181186)
* [REST vs JSON-RPC](http://stackoverflow.com/questions/15056878/rest-vs-json-rpc)
* [Debunking the myths of RPC and REST](https://web.archive.org/web/20170608193645/http://etherealbits.com/2012/12/debunking-the-myths-of-rpc-rest/)
* [What are the drawbacks of using REST](https://www.quora.com/What-are-the-drawbacks-of-using-RESTful-APIs)
* [Crack the system design interview](http://www.puncsky.com/blog/2016-02-13-crack-the-system-design-interview)
* [Thrift](https://code.facebook.com/posts/1468950976659943/)
* [Why REST for internal use and not RPC](http://arstechnica.com/civis/viewtopic.php?t=1190508)


Question: How to: Powers of two table

Answer:
```
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

* [Powers of two](https://en.wikipedia.org/wiki/Power_of_two)


Question: How to: Latency numbers every programmer should know

Answer:
```
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
HDD 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 HDD     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:

* Read sequentially from HDD at 30 MB/s
* Read sequentially from 1 Gbps Ethernet at 100 MB/s
* Read sequentially from SSD at 1 GB/s
* Read sequentially from main memory at 4 GB/s
* 6-7 world-wide round trips per second
* 2,000 round trips per second within a data center

#### Latency numbers visualized

![](https://camo.githubusercontent.com/77f72259e1eb58596b564d1ad823af1853bc60a3/687474703a2f2f692e696d6775722e636f6d2f6b307431652e706e67)

#### Source(s) and further reading

* [Latency numbers every programmer should know - 1](https://gist.github.com/jboner/2841832)
* [Latency numbers every programmer should know - 2](https://gist.github.com/hellerbarde/2843375)
* [Designs, lessons, and advice from building large distributed systems](http://www.cs.cornell.edu/projects/ladis2009/talks/dean-keynote-ladis2009.pdf)
* [Software Engineering Advice from Building Large-Scale Distributed Systems](https://static.googleusercontent.com/media/research.google.com/en//people/jeff/stanford-295-talk.pdf)


Question: How to: Additional system design interview questions

Answer:
> 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](https://www.youtube.com/watch?v=PE4gwstWhmc) |
| Design a search engine like Google | [queue.acm.org](http://queue.acm.org/detail.cfm?id=988407)
[stackexchange.com](http://programmers.stackexchange.com/questions/38324/interview-question-how-would-you-implement-google-search)
[ardendertat.com](http://www.ardendertat.com/2012/01/11/implementing-search-engines/)
[stanford.edu](http://infolab.stanford.edu/~backrub/google.html) |
| Design a scalable web crawler like Google | [quora.com](https://www.quora.com/How-can-I-build-a-web-crawler-from-scratch) |
| Design Google docs | [code.google.com](https://code.google.com/p/google-mobwrite/)
[neil.fraser.name](https://neil.fraser.name/writing/sync/) |
| Design a key-value store like Redis | [slideshare.net](http://www.slideshare.net/dvirsky/introduction-to-redis) |
| Design a cache system like Memcached | [slideshare.net](http://www.slideshare.net/oemebamo/introduction-to-memcached) |
| Design a recommendation system like Amazon's | [hulu.com](https://web.archive.org/web/20170406065247/http://tech.hulu.com/blog/2011/09/19/recommendation-system.html)
[ijcai13.org](http://ijcai13.org/files/tutorial_slides/td3.pdf) |
| Design a tinyurl system like Bitly | [n00tc0d3r.blogspot.com](http://n00tc0d3r.blogspot.com/) |
| Design a chat app like WhatsApp | [highscalability.com](http://highscalability.com/blog/2014/2/26/the-whatsapp-architecture-facebook-bought-for-19-billion.html)
| Design a picture sharing system like Instagram | [highscalability.com](http://highscalability.com/flickr-architecture)
[highscalability.com](http://highscalability.com/blog/2011/12/6/instagram-architecture-14-million-users-terabytes-of-photos.html) |
| Design the Facebook news feed function | [quora.com](http://www.quora.com/What-are-best-practices-for-building-something-like-a-News-Feed)
[quora.com](http://www.quora.com/Activity-Streams/What-are-the-scaling-issues-to-keep-in-mind-while-developing-a-social-network-feed)
[slideshare.net](http://www.slideshare.net/danmckinley/etsy-activity-feeds-architecture) |
| Design the Facebook timeline function | [facebook.com](https://www.facebook.com/note.php?note_id=10150468255628920)
[highscalability.com](http://highscalability.com/blog/2012/1/23/facebook-timeline-brought-to-you-by-the-power-of-denormaliza.html) |
| Design the Facebook chat function | [erlang-factory.com](http://www.erlang-factory.com/upload/presentations/31/EugeneLetuchy-ErlangatFacebook.pdf)
[facebook.com](https://www.facebook.com/note.php?note_id=14218138919&id=9445547199&index=0) |
| Design a graph search function like Facebook's | [facebook.com](https://www.facebook.com/notes/facebook-engineering/under-the-hood-building-out-the-infrastructure-for-graph-search/10151347573598920)
[facebook.com](https://www.facebook.com/notes/facebook-engineering/under-the-hood-indexing-and-ranking-in-graph-search/10151361720763920)
[facebook.com](https://www.facebook.com/notes/facebook-engineering/under-the-hood-the-natural-language-interface-of-graph-search/10151432733048920) |
| Design a content delivery network like CloudFlare | [figshare.com](https://figshare.com/articles/Globally_distributed_content_delivery/6605972) |
| Design a trending topic system like Twitter's | [michael-noll.com](http://www.michael-noll.com/blog/2013/01/18/implementing-real-time-trending-topics-in-storm/)
[snikolov .wordpress.com](http://snikolov.wordpress.com/2012/11/14/early-detection-of-twitter-trends/) |
| Design a random ID generation system | [blog.twitter.com](https://blog.twitter.com/2010/announcing-snowflake)
[github.com](https://github.com/twitter/snowflake/) |
| Return the top k requests during a time interval | [cs.ucsb.edu](https://www.cs.ucsb.edu/sites/default/files/documents/2005-23.pdf)
[wpi.edu](http://davis.wpi.edu/xmdv/docs/EDBT11-diyang.pdf) |
| Design a system that serves data from multiple data centers | [highscalability.com](http://highscalability.com/blog/2009/8/24/how-google-serves-data-from-multiple-datacenters.html) |
| Design an online multiplayer card game | [indieflashblog.com](https://web.archive.org/web/20180929181117/http://www.indieflashblog.com/how-to-create-an-asynchronous-multiplayer-game.html)
[buildnewgames.com](http://buildnewgames.com/real-time-multiplayer/) |
| Design a garbage collection system | [stuffwithstuff.com](http://journal.stuffwithstuff.com/2013/12/08/babys-first-garbage-collector/)
[washington.edu](http://courses.cs.washington.edu/courses/csep521/07wi/prj/rick.pdf) |
| Design an API rate limiter | [https://stripe.com/blog/](https://stripe.com/blog/rate-limiters) |
| Design a Stock Exchange (like NASDAQ or Binance) | [Jane Street](https://youtu.be/b1e4t2k2KJY)
[Golang Implementation](https://around25.com/blog/building-a-


Question: How to: Real world architectures

Answer:
> 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:**

* Identify shared principles, common technologies, and patterns within these articles
* Study what problems are solved by each component, where it works, where it doesn't
* Review the lessons learned

|Type | System | Reference(s) |
|---|---|---|
| Data processing | **MapReduce** - Distributed data processing from Google | [research.google.com](http://static.googleusercontent.com/media/research.google.com/zh-CN/us/archive/mapreduce-osdi04.pdf) |
| Data processing | **Spark** - Distributed data processing from Databricks | [slideshare.net](http://www.slideshare.net/AGrishchenko/apache-spark-architecture) |
| Data processing | **Storm** - Distributed data processing from Twitter | [slideshare.net](http://www.slideshare.net/previa/storm-16094009) |
| | | |
| Data store | **Bigtable** - Distributed column-oriented database from Google | [harvard.edu](http://www.read.seas.harvard.edu/~kohler/class/cs239-w08/chang06bigtable.pdf) |
| Data store | **HBase** - Open source implementation of Bigtable | [slideshare.net](http://www.slideshare.net/alexbaranau/intro-to-hbase) |
| Data store | **Cassandra** - Distributed column-oriented database from Facebook | [slideshare.net](http://www.slideshare.net/planetcassandra/cassandra-introduction-features-30103666)
| Data store | **DynamoDB** - Document-oriented database from Amazon | [harvard.edu](http://www.read.seas.harvard.edu/~kohler/class/cs239-w08/decandia07dynamo.pdf) |
| Data store | **MongoDB** - Document-oriented database | [slideshare.net](http://www.slideshare.net/mdirolf/introduction-to-mongodb) |
| Data store | **Spanner** - Globally-distributed database from Google | [research.google.com](http://research.google.com/archive/spanner-osdi2012.pdf) |
| Data store | **Memcached** - Distributed memory caching system | [slideshare.net](http://www.slideshare.net/oemebamo/introduction-to-memcached) |
| Data store | **Redis** - Distributed memory caching system with persistence and value types | [slideshare.net](http://www.slideshare.net/dvirsky/introduction-to-redis) |
| | | |
| File system | **Google File System (GFS)** - Distributed file system | [research.google.com](http://static.googleusercontent.com/media/research.google.com/zh-CN/us/archive/gfs-sosp2003.pdf) |
| File system | **Hadoop File System (HDFS)** - Open source implementation of GFS | [apache.org](http://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-hdfs/HdfsDesign.html) |
| | | |
| Misc | **Chubby** - Lock service for loosely-coupled distributed systems from Google | [research.google.com](http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en/us/archive/chubby-osdi06.pdf) |
| Misc | **Dapper** - Distributed systems tracing infrastructure | [research.google.com](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/36356.pdf)
| Misc | **Kafka** - Pub/sub message queue from LinkedIn | [slideshare.net](http://www.slideshare.net/mumrah/kafka-talk-tri-hug) |
| Misc | **Zookeeper** - Centralized infrastructure and services enabling synchronization | [slideshare.net](http://www.slideshare.net/sauravhaloi/introduction-to-apache-zookeeper) |
| | Add an architecture | [Contribute](#contributing) |


Question: How to: Company architectures

Answer:
| Company | Reference(s) |
|---|---|
| Amazon | [Amazon architecture](http://highscalability.com/amazon-architecture) |
| Cinchcast | [Producing 1,500 hours of audio every day](http://highscalability.com/blog/2012/7/16/cinchcast-architecture-producing-1500-hours-of-audio-every-d.html) |
| DataSift | [Realtime datamining At 120,000 tweets per second](http://highscalability.com/blog/2011/11/29/datasift-architecture-realtime-datamining-at-120000-tweets-p.html) |
| Dropbox | [How we've scaled Dropbox](https://www.youtube.com/watch?v=PE4gwstWhmc) |
| ESPN | [Operating At 100,000 duh nuh nuhs per second](http://highscalability.com/blog/2013/11/4/espns-architecture-at-scale-operating-at-100000-duh-nuh-nuhs.html) |
| Google | [Google architecture](http://highscalability.com/google-architecture) |
| Instagram | [14 million users, terabytes of photos](http://highscalability.com/blog/2011/12/6/instagram-architecture-14-million-users-terabytes-of-photos.html)
[What powers Instagram](http://instagram-engineering.tumblr.com/post/13649370142/what-powers-instagram-hundreds-of-instances) |
| Justin.tv | [Justin.Tv's live video broadcasting architecture](http://highscalability.com/blog/2010/3/16/justintvs-live-video-broadcasting-architecture.html) |
| Facebook | [Scaling memcached at Facebook](https://cs.uwaterloo.ca/~brecht/courses/854-Emerging-2014/readings/key-value/fb-memcached-nsdi-2013.pdf)
[TAO: Facebook’s distributed data store for the social graph](https://cs.uwaterloo.ca/~brecht/courses/854-Emerging-2014/readings/data-store/tao-facebook-distributed-datastore-atc-2013.pdf)
[Facebook’s photo storage](https://www.usenix.org/legacy/event/osdi10/tech/full_papers/Beaver.pdf)
[How Facebook Live Streams To 800,000 Simultaneous Viewers](http://highscalability.com/blog/2016/6/27/how-facebook-live-streams-to-800000-simultaneous-viewers.html) |
| Flickr | [Flickr architecture](http://highscalability.com/flickr-architecture) |
| Mailbox | [From 0 to one million users in 6 weeks](http://highscalability.com/blog/2013/6/18/scaling-mailbox-from-0-to-one-million-users-in-6-weeks-and-1.html) |
| Netflix | [A 360 Degree View Of The Entire Netflix Stack](http://highscalability.com/blog/2015/11/9/a-360-degree-view-of-the-entire-netflix-stack.html)
[Netflix: What Happens When You Press Play?](http://highscalability.com/blog/2017/12/11/netflix-what-happens-when-you-press-play.html) |
| Pinterest | [From 0 To 10s of billions of page views a month](http://highscalability.com/blog/2013/4/15/scaling-pinterest-from-0-to-10s-of-billions-of-page-views-a.html)
[18 million visitors, 10x growth, 12 employees](http://highscalability.com/blog/2012/5/21/pinterest-architecture-update-18-million-visitors-10x-growth.html) |
| Playfish | [50 million monthly users and growing](http://highscalability.com/blog/2010/9/21/playfishs-social-gaming-architecture-50-million-monthly-user.html) |
| PlentyOfFish | [PlentyOfFish architecture](http://highscalability.com/plentyoffish-architecture) |
| Salesforce | [How they handle 1.3 billion transactions a day](http://highscalability.com/blog/2013/9/23/salesforce-architecture-how-they-handle-13-billion-transacti.html) |
| Stack Overflow | [Stack Overflow architecture](http://highscalability.com/blog/2009/8/5/stack-overflow-architecture.html) |
| TripAdvisor | [40M visitors, 200M dynamic page views, 30TB data](http://highscalability.com/blog/2011/6/27/tripadvisor-architecture-40m-visitors-200m-dynamic-page-view.html) |
| Tumblr | [15 billion page views a month](http://highscalability.com/blog/2012/2/13/tumblr-architecture-15-billion-page-views-a-month-and-harder.html) |
| Twitter | [Making Twitter 10000 percent faster](http://highscalability.com/scaling-twitter-making-twitter-10000-percent-faster)
[Storing 250 million tweets a day using MySQL](http://highscalability.com/blog/2011/12/19/how-twitter-stores-250-million-tweets-a-day-using-mysql.html)
[150M active users, 300K QPS, a 22 MB/S firehose](http://highscalability.com/blog/2013/7/8/the-architecture-twitter-uses-to-deal-with-150m-active-users.html)
[Timelines at scale](https://www.infoq.com/presentations/Twitter-Timeline-Scalability)
[Big and small data at Twitter](https://www.youtube.com/watch?v=5cKTP36HVgI)
[Operations at Twitter: scaling beyond 100 million users](https://www.youtube.com/watch?v=z8LU0Cj6BOU)
[How Twitter Handles 3,000 Images Per Second](http://highscalability.com/blog/2016/4/20/how-twitter-handles-3000-images-per-second.html) |
| Uber | [How Uber scales their real-time market platform](http://highscalability.com/blog/2015/9/14/how-uber-scales-their-real-time-market-platform.html)
[Lessons Learned From Scaling Uber To 2000 Engineers, 1000 Services, And 8000 Git Repositories](http://highscalability.com/blog/2016/10/12/lessons-learned-from-scaling-uber-to-2000-engineers-1000-ser.html) |
| WhatsApp | [The WhatsApp architecture Facebook bought for $19 billion](http://highscalability.com/blog/2014/2/26/t


Question: How to: Company engineering blogs

Answer:
> Architectures for companies you are interviewing with.
>
> Questions you encounter might be from the same domain.

* [Airbnb Engineering](http://nerds.airbnb.com/)
* [Atlassian Developers](https://developer.atlassian.com/blog/)
* [AWS Blog](https://aws.amazon.com/blogs/aws/)
* [Bitly Engineering Blog](http://word.bitly.com/)
* [Box Blogs](https://blog.box.com/blog/category/engineering)
* [Cloudera Developer Blog](http://blog.cloudera.com/)
* [Dropbox Tech Blog](https://tech.dropbox.com/)
* [Engineering at Quora](https://www.quora.com/q/quoraengineering)
* [Ebay Tech Blog](http://www.ebaytechblog.com/)
* [Evernote Tech Blog](https://blog.evernote.com/tech/)
* [Etsy Code as Craft](http://codeascraft.com/)
* [Facebook Engineering](https://www.facebook.com/Engineering)
* [Flickr Code](http://code.flickr.net/)
* [Foursquare Engineering Blog](http://engineering.foursquare.com/)
* [GitHub Engineering Blog](https://github.blog/category/engineering)
* [Google Research Blog](http://googleresearch.blogspot.com/)
* [Groupon Engineering Blog](https://engineering.groupon.com/)
* [Heroku Engineering Blog](https://engineering.heroku.com/)
* [Hubspot Engineering Blog](http://product.hubspot.com/blog/topic/engineering)
* [High Scalability](http://highscalability.com/)
* [Instagram Engineering](http://instagram-engineering.tumblr.com/)
* [Intel Software Blog](https://software.intel.com/en-us/blogs/)
* [Jane Street Tech Blog](https://blogs.janestreet.com/category/ocaml/)
* [LinkedIn Engineering](http://engineering.linkedin.com/blog)
* [Microsoft Engineering](https://engineering.microsoft.com/)
* [Microsoft Python Engineering](https://blogs.msdn.microsoft.com/pythonengineering/)
* [Netflix Tech Blog](http://techblog.netflix.com/)
* [Paypal Developer Blog](https://medium.com/paypal-engineering)
* [Pinterest Engineering Blog](https://medium.com/@Pinterest_Engineering)
* [Reddit Blog](http://www.redditblog.com/)
* [Salesforce Engineering Blog](https://developer.salesforce.com/blogs/engineering/)
* [Slack Engineering Blog](https://slack.engineering/)
* [Spotify Labs](https://labs.spotify.com/)
* [Stripe Engineering Blog](https://stripe.com/blog/engineering)
* [Twilio Engineering Blog](http://www.twilio.com/engineering)
* [Twitter Engineering](https://blog.twitter.com/engineering/)
* [Uber Engineering Blog](http://eng.uber.com/)
* [Yahoo Engineering Blog](http://yahooeng.tumblr.com/)
* [Yelp Engineering Blog](http://engineeringblog.yelp.com/)
* [Zynga Engineering Blog](https://www.zynga.com/blogs/engineering)

#### Source(s) and further reading

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

* [kilimchoi/engineering-blogs](https://github.com/kilimchoi/engineering-blogs)


Question: How to: Under development

Answer:
Interested in adding a section or helping complete one in-progress?  [Contribute](#contributing)!

* Distributed computing with MapReduce
* Consistent hashing
* Scatter gather
* [Contribute](#contributing)


Question: How to: Contact info

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

My contact info can be found on my [GitHub page](https://github.com/donnemartin).


Question: How to: INSTALLATION

Answer:
[![Windows](https://img.shields.io/badge/-Windows_x64-blue.svg?style=for-the-badge&logo=windows)](https://github.com/yt-dlp/yt-dlp/releases/latest/download/yt-dlp.exe)
[![Unix](https://img.shields.io/badge/-Linux/BSD-red.svg?style=for-the-badge&logo=linux)](https://github.com/yt-dlp/yt-dlp/releases/latest/download/yt-dlp)
[![MacOS](https://img.shields.io/badge/-MacOS-lightblue.svg?style=for-the-badge&logo=apple)](https://github.com/yt-dlp/yt-dlp/releases/latest/download/yt-dlp_macos)
[![PyPI](https://img.shields.io/badge/-PyPI-blue.svg?logo=pypi&labelColor=555555&style=for-the-badge)](https://pypi.org/project/yt-dlp)
[![Source Tarball](https://img.shields.io/badge/-Source_tar-green.svg?style=for-the-badge)](https://github.com/yt-dlp/yt-dlp/releases/latest/download/yt-dlp.tar.gz)
[![Other variants](https://img.shields.io/badge/-Other-grey.svg?style=for-the-badge)](#release-files)
[![All versions](https://img.shields.io/badge/-All_Versions-lightgrey.svg?style=for-the-badge)](https://github.com/yt-dlp/yt-dlp/releases)
You can install yt-dlp using [the binaries](#release-files), [pip](https://pypi.org/project/yt-dlp) or one using a third-party package manager. See [the wiki](https://github.com/yt-dlp/yt-dlp/wiki/Installation) for detailed instructions