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*[English](README.md) ∙ [日本語](README-ja.md) ∙ [简体中文](README-zh-Hans.md) ∙ [繁體中文](README-zh-TW.md) | [العَرَبِيَّة‎](https://github.com/donnemartin/system-design-primer/issues/170) ∙ [বাংলা](https://github.com/donnemartin/system-design-primer/issues/220) ∙ [Português do Brasil](https://github.com/donnemartin/system-design-primer/issues/40) ∙ [Deutsch](https://github.com/donnemartin/system-design-primer/issues/186) ∙ [ελληνικά](https://github.com/donnemartin/system-design-primer/issues/130) ∙ [עברית](https://github.com/donnemartin/system-design-primer/issues/272) ∙ [Italiano](https://github.com/donnemartin/system-design-primer/issues/104) ∙ [한국어](https://github.com/donnemartin/system-design-primer/issues/102) ∙ [فارسی](https://github.com/donnemartin/system-design-primer/issues/110) ∙ [Polski](https://github.com/donnemartin/system-design-primer/issues/68) ∙ [русский язык](https://github.com/donnemartin/system-design-primer/issues/87) ∙ [Español](https://github.com/donnemartin/system-design-primer/issues/136) ∙ [ภาษาไทย](https://github.com/donnemartin/system-design-primer/issues/187) ∙ [Türkçe](https://github.com/donnemartin/system-design-primer/issues/39) ∙ [tiếng Việt](https://github.com/donnemartin/system-design-primer/issues/127) ∙ [Français](https://github.com/donnemartin/system-design-primer/issues/250) | [Add Translation](https://github.com/donnemartin/system-design-primer/issues/28)* _[English](README.md) ∙ [日本語](README-ja.md) ∙ [简体中文](README-zh-Hans.md) ∙ [繁體中文](README-zh-TW.md) | [العَرَبِيَّة‎](https://github.com/donnemartin/system-design-primer/issues/170) ∙ [বাংলা](https://github.com/donnemartin/system-design-primer/issues/220) ∙ [Português do Brasil](https://github.com/donnemartin/system-design-primer/issues/40) ∙ [Deutsch](https://github.com/donnemartin/system-design-primer/issues/186) ∙ [ελληνικά](https://github.com/donnemartin/system-design-primer/issues/130) ∙ [עברית](https://github.com/donnemartin/system-design-primer/issues/272) ∙ [Italiano](https://github.com/donnemartin/system-design-primer/issues/104) ∙ [한국어](https://github.com/donnemartin/system-design-primer/issues/102) ∙ [فارسی](https://github.com/donnemartin/system-design-primer/issues/110) ∙ [Polski](https://github.com/donnemartin/system-design-primer/issues/68) ∙ [русский язык](https://github.com/donnemartin/system-design-primer/issues/87) ∙ [Español](https://github.com/donnemartin/system-design-primer/issues/136) ∙ [ภาษาไทย](https://github.com/donnemartin/system-design-primer/issues/187) ∙ [Türkçe](https://github.com/donnemartin/system-design-primer/issues/39) ∙ [tiếng Việt](https://github.com/donnemartin/system-design-primer/issues/127) ∙ [Français](https://github.com/donnemartin/system-design-primer/issues/250) | [Add Translation](https://github.com/donnemartin/system-design-primer/issues/28)_
**Help [translate](TRANSLATIONS.md) this guide!** **Help [translate](TRANSLATIONS.md) this guide!**
@ -37,11 +37,11 @@ In addition to coding interviews, system design is a **required component** of t
Additional topics for interview prep: Additional topics for interview prep:
* [Study guide](#study-guide) - [Study guide](#study-guide)
* [How to approach a system design interview question](#how-to-approach-a-system-design-interview-question) - [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) - [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) - [Object-oriented design interview questions, **with solutions**](#object-oriented-design-interview-questions-with-solutions)
* [Additional system design interview questions](#additional-system-design-interview-questions) - [Additional system design interview questions](#additional-system-design-interview-questions)
## Anki flashcards ## Anki flashcards
@ -52,9 +52,9 @@ Additional topics for interview prep:
The provided [Anki flashcard decks](https://apps.ankiweb.net/) use spaced repetition to help you retain key system design concepts. 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 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) - [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) - [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. Great for use while on-the-go.
@ -69,7 +69,7 @@ Looking for resources to help you prep for the [**Coding Interview**](https://gi
Check out the sister repo [**Interactive Coding Challenges**](https://github.com/donnemartin/interactive-coding-challenges), which contains an additional Anki deck: 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) - [Coding deck](https://github.com/donnemartin/interactive-coding-challenges/tree/master/anki_cards/Coding.apkg)
## Contributing ## Contributing
@ -77,10 +77,10 @@ Check out the sister repo [**Interactive Coding Challenges**](https://github.com
Feel free to submit pull requests to help: Feel free to submit pull requests to help:
* Fix errors - Fix errors
* Improve sections - Improve sections
* Add new sections - Add new sections
* [Translate](https://github.com/donnemartin/system-design-primer/issues/28) - [Translate](https://github.com/donnemartin/system-design-primer/issues/28)
Content that needs some polishing is placed [under development](#under-development). Content that needs some polishing is placed [under development](#under-development).
@ -97,87 +97,87 @@ Review the [Contributing Guidelines](CONTRIBUTING.md).
<br/> <br/>
</p> </p>
* [System design topics: start here](#system-design-topics-start-here) - [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 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) - [Step 2: Review the scalability article](#step-2-review-the-scalability-article)
* [Next steps](#next-steps) - [Next steps](#next-steps)
* [Performance vs scalability](#performance-vs-scalability) - [Performance vs scalability](#performance-vs-scalability)
* [Latency vs throughput](#latency-vs-throughput) - [Latency vs throughput](#latency-vs-throughput)
* [Availability vs consistency](#availability-vs-consistency) - [Availability vs consistency](#availability-vs-consistency)
* [CAP theorem](#cap-theorem) - [CAP theorem](#cap-theorem)
* [CP - consistency and partition tolerance](#cp---consistency-and-partition-tolerance) - [CP - consistency and partition tolerance](#cp---consistency-and-partition-tolerance)
* [AP - availability and partition tolerance](#ap---availability-and-partition-tolerance) - [AP - availability and partition tolerance](#ap---availability-and-partition-tolerance)
* [Consistency patterns](#consistency-patterns) - [Consistency patterns](#consistency-patterns)
* [Weak consistency](#weak-consistency) - [Weak consistency](#weak-consistency)
* [Eventual consistency](#eventual-consistency) - [Eventual consistency](#eventual-consistency)
* [Strong consistency](#strong-consistency) - [Strong consistency](#strong-consistency)
* [Availability patterns](#availability-patterns) - [Availability patterns](#availability-patterns)
* [Fail-over](#fail-over) - [Fail-over](#fail-over)
* [Replication](#replication) - [Replication](#replication)
* [Availability in numbers](#availability-in-numbers) - [Availability in numbers](#availability-in-numbers)
* [Domain name system](#domain-name-system) - [Domain name system](#domain-name-system)
* [Content delivery network](#content-delivery-network) - [Content delivery network](#content-delivery-network)
* [Push CDNs](#push-cdns) - [Push CDNs](#push-cdns)
* [Pull CDNs](#pull-cdns) - [Pull CDNs](#pull-cdns)
* [Load balancer](#load-balancer) - [Load balancer](#load-balancer)
* [Active-passive](#active-passive) - [Active-passive](#active-passive)
* [Active-active](#active-active) - [Active-active](#active-active)
* [Layer 4 load balancing](#layer-4-load-balancing) - [Layer 4 load balancing](#layer-4-load-balancing)
* [Layer 7 load balancing](#layer-7-load-balancing) - [Layer 7 load balancing](#layer-7-load-balancing)
* [Horizontal scaling](#horizontal-scaling) - [Horizontal scaling](#horizontal-scaling)
* [Reverse proxy (web server)](#reverse-proxy-web-server) - [Reverse proxy (web server)](#reverse-proxy-web-server)
* [Load balancer vs reverse proxy](#load-balancer-vs-reverse-proxy) - [Load balancer vs reverse proxy](#load-balancer-vs-reverse-proxy)
* [Application layer](#application-layer) - [Application layer](#application-layer)
* [Microservices](#microservices) - [Microservices](#microservices)
* [Service discovery](#service-discovery) - [Service discovery](#service-discovery)
* [Database](#database) - [Database](#database)
* [Relational database management system (RDBMS)](#relational-database-management-system-rdbms) - [Relational database management system (RDBMS)](#relational-database-management-system-rdbms)
* [Master-slave replication](#master-slave-replication) - [Master-slave replication](#master-slave-replication)
* [Master-master replication](#master-master-replication) - [Master-master replication](#master-master-replication)
* [Federation](#federation) - [Federation](#federation)
* [Sharding](#sharding) - [Sharding](#sharding)
* [Denormalization](#denormalization) - [Denormalization](#denormalization)
* [SQL tuning](#sql-tuning) - [SQL tuning](#sql-tuning)
* [NoSQL](#nosql) - [NoSQL](#nosql)
* [Key-value store](#key-value-store) - [Key-value store](#key-value-store)
* [Document store](#document-store) - [Document store](#document-store)
* [Wide column store](#wide-column-store) - [Wide column store](#wide-column-store)
* [Graph Database](#graph-database) - [Graph Database](#graph-database)
* [SQL or NoSQL](#sql-or-nosql) - [SQL or NoSQL](#sql-or-nosql)
* [Cache](#cache) - [Cache](#cache)
* [Client caching](#client-caching) - [Client caching](#client-caching)
* [CDN caching](#cdn-caching) - [CDN caching](#cdn-caching)
* [Web server caching](#web-server-caching) - [Web server caching](#web-server-caching)
* [Database caching](#database-caching) - [Database caching](#database-caching)
* [Application caching](#application-caching) - [Application caching](#application-caching)
* [Caching at the database query level](#caching-at-the-database-query-level) - [Caching at the database query level](#caching-at-the-database-query-level)
* [Caching at the object level](#caching-at-the-object-level) - [Caching at the object level](#caching-at-the-object-level)
* [When to update the cache](#when-to-update-the-cache) - [When to update the cache](#when-to-update-the-cache)
* [Cache-aside](#cache-aside) - [Cache-aside](#cache-aside)
* [Write-through](#write-through) - [Write-through](#write-through)
* [Write-behind (write-back)](#write-behind-write-back) - [Write-behind (write-back)](#write-behind-write-back)
* [Refresh-ahead](#refresh-ahead) - [Refresh-ahead](#refresh-ahead)
* [Asynchronism](#asynchronism) - [Asynchronism](#asynchronism)
* [Message queues](#message-queues) - [Message queues](#message-queues)
* [Task queues](#task-queues) - [Task queues](#task-queues)
* [Back pressure](#back-pressure) - [Back pressure](#back-pressure)
* [Communication](#communication) - [Communication](#communication)
* [Transmission control protocol (TCP)](#transmission-control-protocol-tcp) - [Transmission control protocol (TCP)](#transmission-control-protocol-tcp)
* [User datagram protocol (UDP)](#user-datagram-protocol-udp) - [User datagram protocol (UDP)](#user-datagram-protocol-udp)
* [Remote procedure call (RPC)](#remote-procedure-call-rpc) - [Remote procedure call (RPC)](#remote-procedure-call-rpc)
* [Representational state transfer (REST)](#representational-state-transfer-rest) - [Representational state transfer (REST)](#representational-state-transfer-rest)
* [Security](#security) - [Security](#security)
* [Appendix](#appendix) - [Appendix](#appendix)
* [Powers of two table](#powers-of-two-table) - [Powers of two table](#powers-of-two-table)
* [Latency numbers every programmer should know](#latency-numbers-every-programmer-should-know) - [Latency numbers every programmer should know](#latency-numbers-every-programmer-should-know)
* [Additional system design interview questions](#additional-system-design-interview-questions) - [Additional system design interview questions](#additional-system-design-interview-questions)
* [Real world architectures](#real-world-architectures) - [Real world architectures](#real-world-architectures)
* [Company architectures](#company-architectures) - [Company architectures](#company-architectures)
* [Company engineering blogs](#company-engineering-blogs) - [Company engineering blogs](#company-engineering-blogs)
* [Under development](#under-development) - [Under development](#under-development)
* [Credits](#credits) - [Credits](#credits)
* [Contact info](#contact-info) - [Contact info](#contact-info)
* [License](#license) - [License](#license)
## Study guide ## Study guide
@ -191,22 +191,22 @@ Review the [Contributing Guidelines](CONTRIBUTING.md).
What you are asked in an interview depends on variables such as: What you are asked in an interview depends on variables such as:
* How much experience you have - How much experience you have
* What your technical background is - What your technical background is
* What positions you are interviewing for - What positions you are interviewing for
* Which companies you are interviewing with - Which companies you are interviewing with
* Luck - 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. 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. 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. - **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. - **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. - **Long timeline** - Aim for **breadth** and **more depth** with system design topics. Practice by solving **most** interview questions.
| | Short | Medium | Long | | | 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 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 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: | | Read through a few [Real world architectures](#real-world-architectures) | :+1: | :+1: | :+1: |
@ -227,43 +227,43 @@ You can use the following steps to guide the discussion. To help solidify this
Gather requirements and scope the problem. Ask questions to clarify use cases and constraints. Discuss assumptions. Gather requirements and scope the problem. Ask questions to clarify use cases and constraints. Discuss assumptions.
* Who is going to use it? - Who is going to use it?
* How are they going to use it? - How are they going to use it?
* How many users are there? - How many users are there?
* What does the system do? - What does the system do?
* What are the inputs and outputs of the system? - What are the inputs and outputs of the system?
* How much data do we expect to handle? - How much data do we expect to handle?
* How many requests per second do we expect? - How many requests per second do we expect?
* What is the expected read to write ratio? - What is the expected read to write ratio?
### Step 2: Create a high level design ### Step 2: Create a high level design
Outline a high level design with all important components. Outline a high level design with all important components.
* Sketch the main components and connections - Sketch the main components and connections
* Justify your ideas - Justify your ideas
### Step 3: Design core components ### Step 3: Design core components
Dive into details for each core component. For example, if you were asked to [design a url shortening service](solutions/system_design/pastebin/README.md), discuss: 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 - Generating and storing a hash of the full url
* [MD5](solutions/system_design/pastebin/README.md) and [Base62](solutions/system_design/pastebin/README.md) - [MD5](solutions/system_design/pastebin/README.md) and [Base62](solutions/system_design/pastebin/README.md)
* Hash collisions - Hash collisions
* SQL or NoSQL - SQL or NoSQL
* Database schema - Database schema
* Translating a hashed url to the full url - Translating a hashed url to the full url
* Database lookup - Database lookup
* API and object-oriented design - API and object-oriented design
### Step 4: Scale the design ### Step 4: Scale the design
Identify and address bottlenecks, given the constraints. For example, do you need the following to address scalability issues? Identify and address bottlenecks, given the constraints. For example, do you need the following to address scalability issues?
* Load balancer - Load balancer
* Horizontal scaling - Horizontal scaling
* Caching - Caching
* Database sharding - 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). Discuss potential solutions and trade-offs. Everything is a trade-off. Address bottlenecks using [principles of scalable system design](#index-of-system-design-topics).
@ -271,18 +271,18 @@ Discuss potential solutions and trade-offs. Everything is a trade-off. Address
You might be asked to do some estimates by hand. Refer to the [Appendix](#appendix) for the following resources: 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) - [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) - [Powers of two table](#powers-of-two-table)
* [Latency numbers every programmer should know](#latency-numbers-every-programmer-should-know) - [Latency numbers every programmer should know](#latency-numbers-every-programmer-should-know)
### Source(s) and further reading ### Source(s) and further reading
Check out the following links to get a better idea of what to expect: Check out the following links to get a better idea of what to expect:
* [How to ace a systems design interview](https://www.palantir.com/2011/10/how-to-rock-a-systems-design-interview/) - [How to ace a systems design interview](https://www.palantir.com/2011/10/how-to-rock-a-systems-design-interview/)
* [The system design interview](http://www.hiredintech.com/system-design) - [The system design interview](http://www.hiredintech.com/system-design)
* [Intro to Architecture and Systems Design Interviews](https://www.youtube.com/watch?v=ZgdS0EUmn70) - [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) - [System design template](https://leetcode.com/discuss/career/229177/My-System-Design-Template)
## System design interview questions with solutions ## System design interview questions with solutions
@ -291,7 +291,7 @@ Check out the following links to get a better idea of what to expect:
> Solutions linked to content in the `solutions/` folder. > Solutions linked to content in the `solutions/` folder.
| Question | | | Question | |
|---|---| | -------------------------------------------------------------------- | ---------------------------------------------------------- |
| Design Pastebin.com (or Bit.ly) | [Solution](solutions/system_design/pastebin/README.md) | | 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 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 a web crawler | [Solution](solutions/system_design/web_crawler/README.md) |
@ -356,10 +356,10 @@ Check out the following links to get a better idea of what to expect:
> >
> Solutions linked to content in the `solutions/` folder. > Solutions linked to content in the `solutions/` folder.
>**Note: This section is under development** > **Note: This section is under development**
| Question | | | Question | |
|---|---| | -------------------------------------- | ------------------------------------------------------------------------------ |
| Design a hash map | [Solution](solutions/object_oriented_design/hash_table/hash_map.ipynb) | | 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 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 call center | [Solution](solutions/object_oriented_design/call_center/call_center.ipynb) |
@ -379,31 +379,31 @@ First, you'll need a basic understanding of common principles, learning about wh
[Scalability Lecture at Harvard](https://www.youtube.com/watch?v=-W9F__D3oY4) [Scalability Lecture at Harvard](https://www.youtube.com/watch?v=-W9F__D3oY4)
* Topics covered: - Topics covered:
* Vertical scaling - Vertical scaling
* Horizontal scaling - Horizontal scaling
* Caching - Caching
* Load balancing - Load balancing
* Database replication - Database replication
* Database partitioning - Database partitioning
### Step 2: Review the scalability article ### Step 2: Review the scalability article
[Scalability](http://www.lecloud.net/tagged/scalability/chrono) [Scalability](http://www.lecloud.net/tagged/scalability/chrono)
* Topics covered: - Topics covered:
* [Clones](http://www.lecloud.net/post/7295452622/scalability-for-dummies-part-1-clones) - [Clones](http://www.lecloud.net/post/7295452622/scalability-for-dummies-part-1-clones)
* [Databases](http://www.lecloud.net/post/7994751381/scalability-for-dummies-part-2-database) - [Databases](http://www.lecloud.net/post/7994751381/scalability-for-dummies-part-2-database)
* [Caches](http://www.lecloud.net/post/9246290032/scalability-for-dummies-part-3-cache) - [Caches](http://www.lecloud.net/post/9246290032/scalability-for-dummies-part-3-cache)
* [Asynchronism](http://www.lecloud.net/post/9699762917/scalability-for-dummies-part-4-asynchronism) - [Asynchronism](http://www.lecloud.net/post/9699762917/scalability-for-dummies-part-4-asynchronism)
### Next steps ### Next steps
Next, we'll look at high-level trade-offs: Next, we'll look at high-level trade-offs:
* **Performance** vs **scalability** - **Performance** vs **scalability**
* **Latency** vs **throughput** - **Latency** vs **throughput**
* **Availability** vs **consistency** - **Availability** vs **consistency**
Keep in mind that **everything is a trade-off**. Keep in mind that **everything is a trade-off**.
@ -415,13 +415,13 @@ A service is **scalable** if it results in increased **performance** in a manner
Another way to look at performance vs scalability: 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 **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. - If you have a **scalability** problem, your system is fast for a single user but slow under heavy load.
### Source(s) and further reading ### Source(s) and further reading
* [A word on scalability](http://www.allthingsdistributed.com/2006/03/a_word_on_scalability.html) - [A word on scalability](http://www.allthingsdistributed.com/2006/03/a_word_on_scalability.html)
* [Scalability, availability, stability, patterns](http://www.slideshare.net/jboner/scalability-availability-stability-patterns/) - [Scalability, availability, stability, patterns](http://www.slideshare.net/jboner/scalability-availability-stability-patterns/)
## Latency vs throughput ## Latency vs throughput
@ -433,7 +433,7 @@ Generally, you should aim for **maximal throughput** with **acceptable latency**
### Source(s) and further reading ### Source(s) and further reading
* [Understanding latency vs throughput](https://community.cadence.com/cadence_blogs_8/b/sd/archive/2010/09/13/understanding-latency-vs-throughput) - [Understanding latency vs throughput](https://community.cadence.com/cadence_blogs_8/b/sd/archive/2010/09/13/understanding-latency-vs-throughput)
## Availability vs consistency ## Availability vs consistency
@ -447,11 +447,11 @@ Generally, you should aim for **maximal throughput** with **acceptable latency**
In a distributed computer system, you can only support two of the following guarantees: 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 - **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 - **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 - **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.* _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 #### CP - consistency and partition tolerance
@ -465,10 +465,10 @@ AP is a good choice if the business needs allow for [eventual consistency](#even
### Source(s) and further reading ### Source(s) and further reading
* [CAP theorem revisited](http://robertgreiner.com/2014/08/cap-theorem-revisited/) - [CAP theorem revisited](http://robertgreiner.com/2014/08/cap-theorem-revisited/)
* [A plain english introduction to CAP theorem](http://ksat.me/a-plain-english-introduction-to-cap-theorem) - [A plain english introduction to CAP theorem](http://ksat.me/a-plain-english-introduction-to-cap-theorem)
* [CAP FAQ](https://github.com/henryr/cap-faq) - [CAP FAQ](https://github.com/henryr/cap-faq)
* [The CAP theorem](https://www.youtube.com/watch?v=k-Yaq8AHlFA) - [The CAP theorem](https://www.youtube.com/watch?v=k-Yaq8AHlFA)
## Consistency patterns ## Consistency patterns
@ -494,7 +494,7 @@ This approach is seen in file systems and RDBMSes. Strong consistency works wel
### Source(s) and further reading ### Source(s) and further reading
* [Transactions across data centers](http://snarfed.org/transactions_across_datacenters_io.html) - [Transactions across data centers](http://snarfed.org/transactions_across_datacenters_io.html)
## Availability patterns ## Availability patterns
@ -520,8 +520,8 @@ Active-active failover can also be referred to as master-master failover.
### Disadvantage(s): failover ### Disadvantage(s): failover
* Fail-over adds more hardware and additional complexity. - 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. - There is a potential for loss of data if the active system fails before any newly written data can be replicated to the passive.
### Replication ### Replication
@ -529,8 +529,8 @@ Active-active failover can also be referred to as master-master failover.
This topic is further discussed in the [Database](#database) section: This topic is further discussed in the [Database](#database) section:
* [Master-slave replication](#master-slave-replication) - [Master-slave replication](#master-slave-replication)
* [Master-master replication](#master-master-replication) - [Master-master replication](#master-master-replication)
### Availability in numbers ### Availability in numbers
@ -538,8 +538,8 @@ Availability is often quantified by uptime (or downtime) as a percentage of time
#### 99.9% availability - three 9s #### 99.9% availability - three 9s
| Duration | Acceptable downtime| | Duration | Acceptable downtime |
|---------------------|--------------------| | ------------------ | ------------------- |
| Downtime per year | 8h 45min 57s | | Downtime per year | 8h 45min 57s |
| Downtime per month | 43m 49.7s | | Downtime per month | 43m 49.7s |
| Downtime per week | 10m 4.8s | | Downtime per week | 10m 4.8s |
@ -547,8 +547,8 @@ Availability is often quantified by uptime (or downtime) as a percentage of time
#### 99.99% availability - four 9s #### 99.99% availability - four 9s
| Duration | Acceptable downtime| | Duration | Acceptable downtime |
|---------------------|--------------------| | ------------------ | ------------------- |
| Downtime per year | 52min 35.7s | | Downtime per year | 52min 35.7s |
| Downtime per month | 4m 23s | | Downtime per month | 4m 23s |
| Downtime per week | 1m 5s | | Downtime per week | 1m 5s |
@ -590,31 +590,31 @@ A Domain Name System (DNS) translates a domain name such as www.example.com to a
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). 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. - **NS record (name server)** - Specifies the DNS servers for your domain/subdomain.
* **MX record (mail exchange)** - Specifies the mail servers for accepting messages. - **MX record (mail exchange)** - Specifies the mail servers for accepting messages.
* **A record (address)** - Points a name to an IP address. - **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. - **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: 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.g33kinfo.com/info/round-robin-vs-weighted-round-robin-lb) - [Weighted round robin](https://www.g33kinfo.com/info/round-robin-vs-weighted-round-robin-lb)
* Prevent traffic from going to servers under maintenance - Prevent traffic from going to servers under maintenance
* Balance between varying cluster sizes - Balance between varying cluster sizes
* A/B testing - A/B testing
* [Latency-based](https://docs.aws.amazon.com/Route53/latest/DeveloperGuide/routing-policy.html#routing-policy-latency) - [Latency-based](https://docs.aws.amazon.com/Route53/latest/DeveloperGuide/routing-policy.html#routing-policy-latency)
* [Geolocation-based](https://docs.aws.amazon.com/Route53/latest/DeveloperGuide/routing-policy.html#routing-policy-geo) - [Geolocation-based](https://docs.aws.amazon.com/Route53/latest/DeveloperGuide/routing-policy.html#routing-policy-geo)
### Disadvantage(s): DNS ### Disadvantage(s): DNS
* Accessing a DNS server introduces a slight delay, although mitigated by caching described above. - 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 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). - DNS services have recently come under [DDoS attack](https://www.kaspersky.com/resource-center/threats/ddos-attacks), preventing users from accessing websites such as Twitter without knowing Twitter's IP address(es).
### Source(s) and further reading ### Source(s) and further reading
* [DNS architecture](https://technet.microsoft.com/en-us/library/dd197427(v=ws.10).aspx) - [DNS architecture](<https://technet.microsoft.com/en-us/library/dd197427(v=ws.10).aspx>)
* [Wikipedia](https://en.wikipedia.org/wiki/Domain_Name_System) - [Wikipedia](https://en.wikipedia.org/wiki/Domain_Name_System)
* [DNS articles](https://support.dnsimple.com/categories/dns/) - [DNS articles](https://support.dnsimple.com/categories/dns/)
## Content delivery network ## Content delivery network
@ -628,8 +628,8 @@ A content delivery network (CDN) is a globally distributed network of proxy serv
Serving content from CDNs can significantly improve performance in two ways: Serving content from CDNs can significantly improve performance in two ways:
* Users receive content from data centers close to them - Users receive content from data centers close to them
* Your servers do not have to serve requests that the CDN fulfills - Your servers do not have to serve requests that the CDN fulfills
### Push CDNs ### Push CDNs
@ -647,15 +647,15 @@ Sites with heavy traffic work well with pull CDNs, as traffic is spread out more
### Disadvantage(s): CDN ### Disadvantage(s): CDN
* CDN costs could be significant depending on traffic, although this should be weighed with additional costs you would incur not using a CDN. - 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. - 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. - CDNs require changing URLs for static content to point to the CDN.
### Source(s) and further reading ### Source(s) and further reading
* [Globally distributed content delivery](https://figshare.com/articles/Globally_distributed_content_delivery/6605972) - [Globally distributed content delivery](https://figshare.com/articles/Globally_distributed_content_delivery/6605972)
* [The differences between push and pull CDNs](http://www.travelblogadvice.com/technical/the-differences-between-push-and-pull-cdns/) - [The differences between push and pull CDNs](http://www.travelblogadvice.com/technical/the-differences-between-push-and-pull-cdns/)
* [Wikipedia](https://en.wikipedia.org/wiki/Content_delivery_network) - [Wikipedia](https://en.wikipedia.org/wiki/Content_delivery_network)
## Load balancer ## Load balancer
@ -667,28 +667,28 @@ Sites with heavy traffic work well with pull CDNs, as traffic is spread out more
Load balancers distribute incoming client requests to computing resources such as application servers and databases. In each case, the load balancer returns the response from the computing resource to the appropriate client. Load balancers are effective at: Load balancers 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 requests from going to unhealthy servers
* Preventing overloading resources - Preventing overloading resources
* Helping to eliminate a single point of failure - Helping to eliminate a single point of failure
Load balancers can be implemented with hardware (expensive) or with software such as HAProxy. Load balancers can be implemented with hardware (expensive) or with software such as HAProxy.
Additional benefits include: Additional benefits include:
* **SSL termination** - Decrypt incoming requests and encrypt server responses so backend servers do not have to perform these potentially expensive operations - **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 - 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 - **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. 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: Load balancers can route traffic based on various metrics, including:
* Random - Random
* Least loaded - Least loaded
* Session/cookies - Session/cookies
* [Round robin or weighted round robin](https://www.g33kinfo.com/info/round-robin-vs-weighted-round-robin-lb) - [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 4](#layer-4-load-balancing)
* [Layer 7](#layer-7-load-balancing) - [Layer 7](#layer-7-load-balancing)
### Layer 4 load balancing ### Layer 4 load balancing
@ -706,26 +706,26 @@ Load balancers can also help with horizontal scaling, improving performance and
#### Disadvantage(s): horizontal scaling #### Disadvantage(s): horizontal scaling
* Scaling horizontally introduces complexity and involves cloning servers - 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 - 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) - 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 - Downstream servers such as caches and databases need to handle more simultaneous connections as upstream servers scale out
### Disadvantage(s): load balancer ### Disadvantage(s): load balancer
* The load balancer can become a performance bottleneck if it does not have enough resources or if it is not configured properly. - 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. - 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. - A single load balancer is a single point of failure, configuring multiple load balancers further increases complexity.
### Source(s) and further reading ### Source(s) and further reading
* [NGINX architecture](https://www.nginx.com/blog/inside-nginx-how-we-designed-for-performance-scale/) - [NGINX architecture](https://www.nginx.com/blog/inside-nginx-how-we-designed-for-performance-scale/)
* [HAProxy architecture guide](http://www.haproxy.org/download/1.2/doc/architecture.txt) - [HAProxy architecture guide](http://www.haproxy.org/download/1.2/doc/architecture.txt)
* [Scalability](http://www.lecloud.net/post/7295452622/scalability-for-dummies-part-1-clones) - [Scalability](http://www.lecloud.net/post/7295452622/scalability-for-dummies-part-1-clones)
* [Wikipedia](https://en.wikipedia.org/wiki/Load_balancing_(computing)) - [Wikipedia](<https://en.wikipedia.org/wiki/Load_balancing_(computing)>)
* [Layer 4 load balancing](https://www.nginx.com/resources/glossary/layer-4-load-balancing/) - [Layer 4 load balancing](https://www.nginx.com/resources/glossary/layer-4-load-balancing/)
* [Layer 7 load balancing](https://www.nginx.com/resources/glossary/layer-7-load-balancing/) - [Layer 7 load balancing](https://www.nginx.com/resources/glossary/layer-7-load-balancing/)
* [ELB listener config](http://docs.aws.amazon.com/elasticloadbalancing/latest/classic/elb-listener-config.html) - [ELB listener config](http://docs.aws.amazon.com/elasticloadbalancing/latest/classic/elb-listener-config.html)
## Reverse proxy (web server) ## Reverse proxy (web server)
@ -740,35 +740,35 @@ A reverse proxy is a web server that centralizes internal services and provides
Additional benefits include: Additional benefits include:
* **Increased security** - Hide information about backend servers, blacklist IPs, limit number of connections per client - **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 - **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 - **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 - Removes the need to install [X.509 certificates](https://en.wikipedia.org/wiki/X.509) on each server
* **Compression** - Compress server responses - **Compression** - Compress server responses
* **Caching** - Return the response for cached requests - **Caching** - Return the response for cached requests
* **Static content** - Serve static content directly - **Static content** - Serve static content directly
* HTML/CSS/JS - HTML/CSS/JS
* Photos - Photos
* Videos - Videos
* Etc - Etc
### Load balancer vs reverse proxy ### Load balancer vs reverse proxy
* 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. - 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. - 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. - Solutions such as NGINX and HAProxy can support both layer 7 reverse proxying and load balancing.
### Disadvantage(s): reverse proxy ### Disadvantage(s): reverse proxy
* Introducing a reverse proxy results in increased complexity. - 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. - 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.
### Source(s) and further reading ### Source(s) and further reading
* [Reverse proxy vs load balancer](https://www.nginx.com/resources/glossary/reverse-proxy-vs-load-balancer/) - [Reverse proxy vs load balancer](https://www.nginx.com/resources/glossary/reverse-proxy-vs-load-balancer/)
* [NGINX architecture](https://www.nginx.com/blog/inside-nginx-how-we-designed-for-performance-scale/) - [NGINX architecture](https://www.nginx.com/blog/inside-nginx-how-we-designed-for-performance-scale/)
* [HAProxy architecture guide](http://www.haproxy.org/download/1.2/doc/architecture.txt) - [HAProxy architecture guide](http://www.haproxy.org/download/1.2/doc/architecture.txt)
* [Wikipedia](https://en.wikipedia.org/wiki/Reverse_proxy) - [Wikipedia](https://en.wikipedia.org/wiki/Reverse_proxy)
## Application layer ## Application layer
@ -794,16 +794,16 @@ Systems such as [Consul](https://www.consul.io/docs/index.html), [Etcd](https://
### Disadvantage(s): application layer ### Disadvantage(s): application layer
* Adding an application layer with loosely coupled services requires a different approach from an architectural, operations, and process viewpoint (vs a monolithic system). - 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. - Microservices can add complexity in terms of deployments and operations.
### Source(s) and further reading ### Source(s) and further reading
* [Intro to architecting systems for scale](http://lethain.com/introduction-to-architecting-systems-for-scale) - [Intro to architecting systems for scale](http://lethain.com/introduction-to-architecting-systems-for-scale)
* [Crack the system design interview](http://www.puncsky.com/blog/2016-02-13-crack-the-system-design-interview) - [Crack the system design interview](http://www.puncsky.com/blog/2016-02-13-crack-the-system-design-interview)
* [Service oriented architecture](https://en.wikipedia.org/wiki/Service-oriented_architecture) - [Service oriented architecture](https://en.wikipedia.org/wiki/Service-oriented_architecture)
* [Introduction to Zookeeper](http://www.slideshare.net/sauravhaloi/introduction-to-apache-zookeeper) - [Introduction to Zookeeper](http://www.slideshare.net/sauravhaloi/introduction-to-apache-zookeeper)
* [Here's what you need to know about building microservices](https://cloudncode.wordpress.com/2016/07/22/msa-getting-started/) - [Here's what you need to know about building microservices](https://cloudncode.wordpress.com/2016/07/22/msa-getting-started/)
## Database ## Database
@ -819,10 +819,10 @@ 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). **ACID** is a set of properties of relational database [transactions](https://en.wikipedia.org/wiki/Database_transaction).
* **Atomicity** - Each transaction is all or nothing - **Atomicity** - Each transaction is all or nothing
* **Consistency** - Any transaction will bring the database from one valid state to another - **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 - **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 - **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**. There are many techniques to scale a relational database: **master-slave replication**, **master-master replication**, **federation**, **sharding**, **denormalization**, and **SQL tuning**.
@ -838,8 +838,8 @@ The master serves reads and writes, replicating writes to one or more slaves, wh
##### Disadvantage(s): master-slave replication ##### Disadvantage(s): master-slave replication
* Additional logic is needed to promote a slave to a master. - 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. - See [Disadvantage(s): replication](#disadvantages-replication) for points related to **both** master-slave and master-master.
#### Master-master replication #### Master-master replication
@ -853,23 +853,23 @@ Both masters serve reads and writes and coordinate with each other on writes. I
##### Disadvantage(s): master-master replication ##### 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. - 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. - 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. - 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. - See [Disadvantage(s): replication](#disadvantages-replication) for points related to **both** master-slave and master-master.
##### Disadvantage(s): replication ##### 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. - 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. - 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. - 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. - 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. - Replication adds more hardware and additional complexity.
##### Source(s) and further reading: replication ##### Source(s) and further reading: replication
* [Scalability, availability, stability, patterns](http://www.slideshare.net/jboner/scalability-availability-stability-patterns/) - [Scalability, availability, stability, patterns](http://www.slideshare.net/jboner/scalability-availability-stability-patterns/)
* [Multi-master replication](https://en.wikipedia.org/wiki/Multi-master_replication) - [Multi-master replication](https://en.wikipedia.org/wiki/Multi-master_replication)
#### Federation #### Federation
@ -883,14 +883,14 @@ Federation (or functional partitioning) splits up databases by function. For ex
##### Disadvantage(s): federation ##### Disadvantage(s): federation
* Federation is not effective if your schema requires huge functions or tables. - 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. - 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). - 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. - Federation adds more hardware and additional complexity.
##### Source(s) and further reading: federation ##### Source(s) and further reading: federation
* [Scaling up to your first 10 million users](https://www.youtube.com/watch?v=kKjm4ehYiMs) - [Scaling up to your first 10 million users](https://www.youtube.com/watch?v=kKjm4ehYiMs)
#### Sharding #### Sharding
@ -908,17 +908,17 @@ Common ways to shard a table of users is either through the user's last name ini
##### Disadvantage(s): sharding ##### Disadvantage(s): sharding
* You'll need to update your application logic to work with shards, which could result in complex SQL queries. - You'll need to update your application logic to work with shards, which could result in complex SQL queries.
* Data distribution can become lopsided in a shard. For example, a set of power users on a shard could result in increased load to that shard compared to others. - Data distribution can become lopsided in a shard. For example, a set of power users on a shard could result in increased load to that shard compared to others.
* Rebalancing adds additional complexity. A sharding function based on [consistent hashing](http://www.paperplanes.de/2011/12/9/the-magic-of-consistent-hashing.html) can reduce the amount of transferred data. - Rebalancing adds additional complexity. A sharding function based on [consistent hashing](http://www.paperplanes.de/2011/12/9/the-magic-of-consistent-hashing.html) can reduce the amount of transferred data.
* Joining data from multiple shards is more complex. - Joining data from multiple shards is more complex.
* Sharding adds more hardware and additional complexity. - Sharding adds more hardware and additional complexity.
##### Source(s) and further reading: sharding ##### Source(s) and further reading: sharding
* [The coming of the shard](http://highscalability.com/blog/2009/8/6/an-unorthodox-approach-to-database-design-the-coming-of-the.html) - [The coming of the shard](http://highscalability.com/blog/2009/8/6/an-unorthodox-approach-to-database-design-the-coming-of-the.html)
* [Shard database architecture](https://en.wikipedia.org/wiki/Shard_(database_architecture)) - [Shard database architecture](<https://en.wikipedia.org/wiki/Shard_(database_architecture)>)
* [Consistent hashing](http://www.paperplanes.de/2011/12/9/the-magic-of-consistent-hashing.html) - [Consistent hashing](http://www.paperplanes.de/2011/12/9/the-magic-of-consistent-hashing.html)
#### Denormalization #### Denormalization
@ -930,13 +930,13 @@ In most systems, reads can heavily outnumber writes 100:1 or even 1000:1. A rea
##### Disadvantage(s): denormalization ##### Disadvantage(s): denormalization
* Data is duplicated. - Data is duplicated.
* Constraints can help redundant copies of information stay in sync, which increases complexity of the database design. - Constraints can help redundant copies of information stay in sync, which increases complexity of the database design.
* A denormalized database under heavy write load might perform worse than its normalized counterpart. - A denormalized database under heavy write load might perform worse than its normalized counterpart.
###### Source(s) and further reading: denormalization ###### Source(s) and further reading: denormalization
* [Denormalization](https://en.wikipedia.org/wiki/Denormalization) - [Denormalization](https://en.wikipedia.org/wiki/Denormalization)
#### SQL tuning #### SQL tuning
@ -944,49 +944,49 @@ SQL tuning is a broad topic and many [books](https://www.amazon.com/s/ref=nb_sb_
It's important to **benchmark** and **profile** to simulate and uncover bottlenecks. It's important to **benchmark** and **profile** to simulate and uncover bottlenecks.
* **Benchmark** - Simulate high-load situations with tools such as [ab](http://httpd.apache.org/docs/2.2/programs/ab.html). - **Benchmark** - Simulate high-load situations with tools such as [ab](http://httpd.apache.org/docs/2.2/programs/ab.html).
* **Profile** - Enable tools such as the [slow query log](http://dev.mysql.com/doc/refman/5.7/en/slow-query-log.html) to help track performance issues. - **Profile** - Enable tools such as the [slow query log](http://dev.mysql.com/doc/refman/5.7/en/slow-query-log.html) to help track performance issues.
Benchmarking and profiling might point you to the following optimizations. Benchmarking and profiling might point you to the following optimizations.
##### Tighten up the schema ##### Tighten up the schema
* MySQL dumps to disk in contiguous blocks for fast access. - MySQL dumps to disk in contiguous blocks for fast access.
* Use `CHAR` instead of `VARCHAR` for fixed-length fields. - Use `CHAR` instead of `VARCHAR` for fixed-length fields.
* `CHAR` effectively allows for fast, random access, whereas with `VARCHAR`, you must find the end of a string before moving onto the next one. - `CHAR` effectively allows for fast, random access, whereas with `VARCHAR`, you must find the end of a string before moving onto the next one.
* Use `TEXT` for large blocks of text such as blog posts. `TEXT` also allows for boolean searches. Using a `TEXT` field results in storing a pointer on disk that is used to locate the text block. - Use `TEXT` for large blocks of text such as blog posts. `TEXT` also allows for boolean searches. Using a `TEXT` field results in storing a pointer on disk that is used to locate the text block.
* Use `INT` for larger numbers up to 2^32 or 4 billion. - Use `INT` for larger numbers up to 2^32 or 4 billion.
* Use `DECIMAL` for currency to avoid floating point representation errors. - Use `DECIMAL` for currency to avoid floating point representation errors.
* Avoid storing large `BLOBS`, store the location of where to get the object instead. - Avoid storing large `BLOBS`, store the location of where to get the object instead.
* `VARCHAR(255)` is the largest number of characters that can be counted in an 8 bit number, often maximizing the use of a byte in some RDBMS. - `VARCHAR(255)` is the largest number of characters that can be counted in an 8 bit number, often maximizing the use of a byte in some RDBMS.
* Set the `NOT NULL` constraint where applicable to [improve search performance](http://stackoverflow.com/questions/1017239/how-do-null-values-affect-performance-in-a-database-search). - Set the `NOT NULL` constraint where applicable to [improve search performance](http://stackoverflow.com/questions/1017239/how-do-null-values-affect-performance-in-a-database-search).
##### Use good indices ##### Use good indices
* Columns that you are querying (`SELECT`, `GROUP BY`, `ORDER BY`, `JOIN`) could be faster with indices. - Columns that you are querying (`SELECT`, `GROUP BY`, `ORDER BY`, `JOIN`) could be faster with indices.
* Indices are usually represented as self-balancing [B-tree](https://en.wikipedia.org/wiki/B-tree) that keeps data sorted and allows searches, sequential access, insertions, and deletions in logarithmic time. - Indices are usually represented as self-balancing [B-tree](https://en.wikipedia.org/wiki/B-tree) that keeps data sorted and allows searches, sequential access, insertions, and deletions in logarithmic time.
* Placing an index can keep the data in memory, requiring more space. - Placing an index can keep the data in memory, requiring more space.
* Writes could also be slower since the index also needs to be updated. - Writes could also be slower since the index also needs to be updated.
* When loading large amounts of data, it might be faster to disable indices, load the data, then rebuild the indices. - When loading large amounts of data, it might be faster to disable indices, load the data, then rebuild the indices.
##### Avoid expensive joins ##### Avoid expensive joins
* [Denormalize](#denormalization) where performance demands it. - [Denormalize](#denormalization) where performance demands it.
##### Partition tables ##### Partition tables
* Break up a table by putting hot spots in a separate table to help keep it in memory. - Break up a table by putting hot spots in a separate table to help keep it in memory.
##### Tune the query cache ##### Tune the query cache
* In some cases, the [query cache](https://dev.mysql.com/doc/refman/5.7/en/query-cache.html) could lead to [performance issues](https://www.percona.com/blog/2016/10/12/mysql-5-7-performance-tuning-immediately-after-installation/). - In some cases, the [query cache](https://dev.mysql.com/doc/refman/5.7/en/query-cache.html) could lead to [performance issues](https://www.percona.com/blog/2016/10/12/mysql-5-7-performance-tuning-immediately-after-installation/).
##### Source(s) and further reading: SQL tuning ##### Source(s) and further reading: SQL tuning
* [Tips for optimizing MySQL queries](http://aiddroid.com/10-tips-optimizing-mysql-queries-dont-suck/) - [Tips for optimizing MySQL queries](http://aiddroid.com/10-tips-optimizing-mysql-queries-dont-suck/)
* [Is there a good reason i see VARCHAR(255) used so often?](http://stackoverflow.com/questions/1217466/is-there-a-good-reason-i-see-varchar255-used-so-often-as-opposed-to-another-l) - [Is there a good reason i see VARCHAR(255) used so often?](http://stackoverflow.com/questions/1217466/is-there-a-good-reason-i-see-varchar255-used-so-often-as-opposed-to-another-l)
* [How do null values affect performance?](http://stackoverflow.com/questions/1017239/how-do-null-values-affect-performance-in-a-database-search) - [How do null values affect performance?](http://stackoverflow.com/questions/1017239/how-do-null-values-affect-performance-in-a-database-search)
* [Slow query log](http://dev.mysql.com/doc/refman/5.7/en/slow-query-log.html) - [Slow query log](http://dev.mysql.com/doc/refman/5.7/en/slow-query-log.html)
### NoSQL ### NoSQL
@ -994,9 +994,9 @@ NoSQL is a collection of data items represented in a **key-value store**, **docu
**BASE** is often used to describe the properties of NoSQL databases. In comparison with the [CAP Theorem](#cap-theorem), BASE chooses availability over consistency. **BASE** is often used to describe the properties of NoSQL databases. In comparison with the [CAP Theorem](#cap-theorem), BASE chooses availability over consistency.
* **Basically available** - the system guarantees availability. - **Basically available** - the system guarantees availability.
* **Soft state** - the state of the system may change over time, even without input. - **Soft state** - the state of the system may change over time, even without input.
* **Eventual consistency** - the system will become consistent over a period of time, given that the system doesn't receive input during that period. - **Eventual consistency** - the system will become consistent over a period of time, given that the system doesn't receive input during that period.
In addition to choosing between [SQL or NoSQL](#sql-or-nosql), it is helpful to understand which type of NoSQL database best fits your use case(s). We'll review **key-value stores**, **document stores**, **wide column stores**, and **graph databases** in the next section. In addition to choosing between [SQL or NoSQL](#sql-or-nosql), it is helpful to understand which type of NoSQL database best fits your use case(s). We'll review **key-value stores**, **document stores**, **wide column stores**, and **graph databases** in the next section.
@ -1012,16 +1012,16 @@ A key-value store is the basis for more complex systems such as a document store
##### Source(s) and further reading: key-value store ##### Source(s) and further reading: key-value store
* [Key-value database](https://en.wikipedia.org/wiki/Key-value_database) - [Key-value database](https://en.wikipedia.org/wiki/Key-value_database)
* [Disadvantages of key-value stores](http://stackoverflow.com/questions/4056093/what-are-the-disadvantages-of-using-a-key-value-table-over-nullable-columns-or) - [Disadvantages of key-value stores](http://stackoverflow.com/questions/4056093/what-are-the-disadvantages-of-using-a-key-value-table-over-nullable-columns-or)
* [Redis architecture](http://qnimate.com/overview-of-redis-architecture/) - [Redis architecture](http://qnimate.com/overview-of-redis-architecture/)
* [Memcached architecture](https://www.adayinthelifeof.nl/2011/02/06/memcache-internals/) - [Memcached architecture](https://www.adayinthelifeof.nl/2011/02/06/memcache-internals/)
#### Document store #### Document store
> Abstraction: key-value store with documents stored as values > Abstraction: key-value store with documents stored as values
A document store is centered around documents (XML, JSON, binary, etc), where a document stores all information for a given object. Document stores provide APIs or a query language to query based on the internal structure of the document itself. *Note, many key-value stores include features for working with a value's metadata, blurring the lines between these two storage types.* A document store is centered around documents (XML, JSON, binary, etc), where a document stores all information for a given object. Document stores provide APIs or a query language to query based on the internal structure of the document itself. _Note, many key-value stores include features for working with a value's metadata, blurring the lines between these two storage types._
Based on the underlying implementation, documents are organized by collections, tags, metadata, or directories. Although documents can be organized or grouped together, documents may have fields that are completely different from each other. Based on the underlying implementation, documents are organized by collections, tags, metadata, or directories. Although documents can be organized or grouped together, documents may have fields that are completely different from each other.
@ -1031,10 +1031,10 @@ Document stores provide high flexibility and are often used for working with occ
##### Source(s) and further reading: document store ##### Source(s) and further reading: document store
* [Document-oriented database](https://en.wikipedia.org/wiki/Document-oriented_database) - [Document-oriented database](https://en.wikipedia.org/wiki/Document-oriented_database)
* [MongoDB architecture](https://www.mongodb.com/mongodb-architecture) - [MongoDB architecture](https://www.mongodb.com/mongodb-architecture)
* [CouchDB architecture](https://blog.couchdb.org/2016/08/01/couchdb-2-0-architecture/) - [CouchDB architecture](https://blog.couchdb.org/2016/08/01/couchdb-2-0-architecture/)
* [Elasticsearch architecture](https://www.elastic.co/blog/found-elasticsearch-from-the-bottom-up) - [Elasticsearch architecture](https://www.elastic.co/blog/found-elasticsearch-from-the-bottom-up)
#### Wide column store #### Wide column store
@ -1054,10 +1054,10 @@ Wide column stores offer high availability and high scalability. They are often
##### Source(s) and further reading: wide column store ##### Source(s) and further reading: wide column store
* [SQL & NoSQL, a brief history](http://blog.grio.com/2015/11/sql-nosql-a-brief-history.html) - [SQL & NoSQL, a brief history](http://blog.grio.com/2015/11/sql-nosql-a-brief-history.html)
* [Bigtable architecture](http://www.read.seas.harvard.edu/~kohler/class/cs239-w08/chang06bigtable.pdf) - [Bigtable architecture](http://www.read.seas.harvard.edu/~kohler/class/cs239-w08/chang06bigtable.pdf)
* [HBase architecture](https://www.edureka.co/blog/hbase-architecture/) - [HBase architecture](https://www.edureka.co/blog/hbase-architecture/)
* [Cassandra architecture](http://docs.datastax.com/en/cassandra/3.0/cassandra/architecture/archIntro.html) - [Cassandra architecture](http://docs.datastax.com/en/cassandra/3.0/cassandra/architecture/archIntro.html)
#### Graph database #### Graph database
@ -1075,17 +1075,17 @@ Graphs databases offer high performance for data models with complex relationshi
##### Source(s) and further reading: graph ##### Source(s) and further reading: graph
* [Graph database](https://en.wikipedia.org/wiki/Graph_database) - [Graph database](https://en.wikipedia.org/wiki/Graph_database)
* [Neo4j](https://neo4j.com/) - [Neo4j](https://neo4j.com/)
* [FlockDB](https://blog.twitter.com/2010/introducing-flockdb) - [FlockDB](https://blog.twitter.com/2010/introducing-flockdb)
#### Source(s) and further reading: NoSQL #### Source(s) and further reading: NoSQL
* [Explanation of base terminology](http://stackoverflow.com/questions/3342497/explanation-of-base-terminology) - [Explanation of base terminology](http://stackoverflow.com/questions/3342497/explanation-of-base-terminology)
* [NoSQL databases a survey and decision guidance](https://medium.com/baqend-blog/nosql-databases-a-survey-and-decision-guidance-ea7823a822d#.wskogqenq) - [NoSQL databases a survey and decision guidance](https://medium.com/baqend-blog/nosql-databases-a-survey-and-decision-guidance-ea7823a822d#.wskogqenq)
* [Scalability](http://www.lecloud.net/post/7994751381/scalability-for-dummies-part-2-database) - [Scalability](http://www.lecloud.net/post/7994751381/scalability-for-dummies-part-2-database)
* [Introduction to NoSQL](https://www.youtube.com/watch?v=qI_g07C_Q5I) - [Introduction to NoSQL](https://www.youtube.com/watch?v=qI_g07C_Q5I)
* [NoSQL patterns](http://horicky.blogspot.com/2009/11/nosql-patterns.html) - [NoSQL patterns](http://horicky.blogspot.com/2009/11/nosql-patterns.html)
### SQL or NoSQL ### SQL or NoSQL
@ -1097,37 +1097,37 @@ Graphs databases offer high performance for data models with complex relationshi
Reasons for **SQL**: Reasons for **SQL**:
* Structured data - Structured data
* Strict schema - Strict schema
* Relational data - Relational data
* Need for complex joins - Need for complex joins
* Transactions - Transactions
* Clear patterns for scaling - Clear patterns for scaling
* More established: developers, community, code, tools, etc - More established: developers, community, code, tools, etc
* Lookups by index are very fast - Lookups by index are very fast
Reasons for **NoSQL**: Reasons for **NoSQL**:
* Semi-structured data - Semi-structured data
* Dynamic or flexible schema - Dynamic or flexible schema
* Non-relational data - Non-relational data
* No need for complex joins - No need for complex joins
* Store many TB (or PB) of data - Store many TB (or PB) of data
* Very data intensive workload - Very data intensive workload
* Very high throughput for IOPS - Very high throughput for IOPS
Sample data well-suited for NoSQL: Sample data well-suited for NoSQL:
* Rapid ingest of clickstream and log data - Rapid ingest of clickstream and log data
* Leaderboard or scoring data - Leaderboard or scoring data
* Temporary data, such as a shopping cart - Temporary data, such as a shopping cart
* Frequently accessed ('hot') tables - Frequently accessed ('hot') tables
* Metadata/lookup tables - Metadata/lookup tables
##### Source(s) and further reading: SQL or NoSQL ##### Source(s) and further reading: SQL or NoSQL
* [Scaling up to your first 10 million users](https://www.youtube.com/watch?v=kKjm4ehYiMs) - [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/) - [SQL vs NoSQL differences](https://www.sitepoint.com/sql-vs-nosql-differences/)
## Cache ## Cache
@ -1159,19 +1159,19 @@ Your database usually includes some level of caching in a default configuration,
### Application caching ### Application caching
In-memory caches such as Memcached and Redis are key-value stores between your application and your data storage. Since the data is held in RAM, it is much faster than typical databases where data is stored on disk. RAM is more limited than disk, so [cache invalidation](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. 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: Redis has the following additional features:
* Persistence option - Persistence option
* Built-in data structures such as sorted sets and lists - 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**: There are multiple levels you can cache that fall into two general categories: **database queries** and **objects**:
* Row level - Row level
* Query-level - Query-level
* Fully-formed serializable objects - Fully-formed serializable objects
* Fully-rendered HTML - Fully-rendered HTML
Generally, you should try to avoid file-based caching, as it makes cloning and auto-scaling more difficult. Generally, you should try to avoid file-based caching, as it makes cloning and auto-scaling more difficult.
@ -1179,22 +1179,22 @@ Generally, you should try to avoid file-based caching, as it makes cloning and a
Whenever you query the database, hash the query as a key and store the result to the cache. This approach suffers from expiration issues: 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 - 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 - If one piece of data changes such as a table cell, you need to delete all cached queries that might include the changed cell
### Caching at the object level ### Caching at the object level
See your data as an object, similar to what you do with your application code. Have your application assemble the dataset from the database into a class instance or a data structure(s): 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 - Remove the object from cache if its underlying data has changed
* Allows for asynchronous processing: workers assemble objects by consuming the latest cached object - Allows for asynchronous processing: workers assemble objects by consuming the latest cached object
Suggestions of what to cache: Suggestions of what to cache:
* User sessions - User sessions
* Fully rendered web pages - Fully rendered web pages
* Activity streams - Activity streams
* User graph data - User graph data
### When to update the cache ### When to update the cache
@ -1210,10 +1210,10 @@ Since you can only store a limited amount of data in cache, you'll need to deter
The application is responsible for reading and writing from storage. The cache does not interact with storage directly. The application does the following: 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 - Look for entry in cache, resulting in a cache miss
* Load entry from the database - Load entry from the database
* Add entry to cache - Add entry to cache
* Return entry - Return entry
```python ```python
def get_user(self, user_id): def get_user(self, user_id):
@ -1232,9 +1232,9 @@ Subsequent reads of data added to cache are fast. Cache-aside is also referred
##### Disadvantage(s): cache-aside ##### Disadvantage(s): cache-aside
* Each cache miss results in three trips, which can cause a noticeable delay. - 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. - 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. - When a node fails, it is replaced by a new, empty node, increasing latency.
#### Write-through #### Write-through
@ -1246,9 +1246,9 @@ Subsequent reads of data added to cache are fast. Cache-aside is also referred
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: 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 - Application adds/updates entry in cache
* Cache synchronously writes entry to data store - Cache synchronously writes entry to data store
* Return - Return
Application code: Application code:
@ -1268,8 +1268,8 @@ Write-through is a slow overall operation due to the write operation, but subseq
##### Disadvantage(s): write through ##### 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. - 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. - Most data written might never be read, which can be minimized with a TTL.
#### Write-behind (write-back) #### Write-behind (write-back)
@ -1281,13 +1281,13 @@ Write-through is a slow overall operation due to the write operation, but subseq
In write-behind, the application does the following: In write-behind, the application does the following:
* Add/update entry in cache - Add/update entry in cache
* Asynchronously write entry to the data store, improving write performance - Asynchronously write entry to the data store, improving write performance
##### Disadvantage(s): write-behind ##### Disadvantage(s): write-behind
* There could be data loss if the cache goes down prior to its contents hitting the data store. - 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. - It is more complex to implement write-behind than it is to implement cache-aside or write-through.
#### Refresh-ahead #### Refresh-ahead
@ -1303,23 +1303,23 @@ Refresh-ahead can result in reduced latency vs read-through if the cache can acc
##### Disadvantage(s): refresh-ahead ##### 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. - Not accurately predicting which items are likely to be needed in the future can result in reduced performance than without refresh-ahead.
### Disadvantage(s): cache ### Disadvantage(s): cache
* 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). - 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. - 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. - Need to make application changes such as adding Redis or memcached.
### Source(s) and further reading ### Source(s) and further reading
* [From cache to in-memory data grid](http://www.slideshare.net/tmatyashovsky/from-cache-to-in-memory-data-grid-introduction-to-hazelcast) - [From cache to in-memory data grid](http://www.slideshare.net/tmatyashovsky/from-cache-to-in-memory-data-grid-introduction-to-hazelcast)
* [Scalable system design patterns](http://horicky.blogspot.com/2010/10/scalable-system-design-patterns.html) - [Scalable system design patterns](http://horicky.blogspot.com/2010/10/scalable-system-design-patterns.html)
* [Introduction to architecting systems for scale](http://lethain.com/introduction-to-architecting-systems-for-scale/) - [Introduction to architecting systems for scale](http://lethain.com/introduction-to-architecting-systems-for-scale/)
* [Scalability, availability, stability, patterns](http://www.slideshare.net/jboner/scalability-availability-stability-patterns/) - [Scalability, availability, stability, patterns](http://www.slideshare.net/jboner/scalability-availability-stability-patterns/)
* [Scalability](http://www.lecloud.net/post/9246290032/scalability-for-dummies-part-3-cache) - [Scalability](http://www.lecloud.net/post/9246290032/scalability-for-dummies-part-3-cache)
* [AWS ElastiCache strategies](http://docs.aws.amazon.com/AmazonElastiCache/latest/UserGuide/Strategies.html) - [AWS ElastiCache strategies](http://docs.aws.amazon.com/AmazonElastiCache/latest/UserGuide/Strategies.html)
* [Wikipedia](https://en.wikipedia.org/wiki/Cache_(computing)) - [Wikipedia](<https://en.wikipedia.org/wiki/Cache_(computing)>)
## Asynchronism ## Asynchronism
@ -1335,8 +1335,8 @@ Asynchronous workflows help reduce request times for expensive operations that w
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: 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 - 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 - 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. 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.
@ -1358,14 +1358,14 @@ If queues start to grow significantly, the queue size can become larger than mem
### Disadvantage(s): asynchronism ### Disadvantage(s): asynchronism
* Use cases such as inexpensive calculations and realtime workflows might be better suited for synchronous operations, as introducing queues can add delays and complexity. - Use cases such as inexpensive calculations and realtime workflows might be better suited for synchronous operations, as introducing queues can add delays and complexity.
### Source(s) and further reading ### Source(s) and further reading
* [It's all a numbers game](https://www.youtube.com/watch?v=1KRYH75wgy4) - [It's all a numbers game](https://www.youtube.com/watch?v=1KRYH75wgy4)
* [Applying back pressure when overloaded](http://mechanical-sympathy.blogspot.com/2012/05/apply-back-pressure-when-overloaded.html) - [Applying back pressure when overloaded](http://mechanical-sympathy.blogspot.com/2012/05/apply-back-pressure-when-overloaded.html)
* [Little's law](https://en.wikipedia.org/wiki/Little%27s_law) - [Little's law](https://en.wikipedia.org/wiki/Little%27s_law)
* [What is the difference between a message queue and a task queue?](https://www.quora.com/What-is-the-difference-between-a-message-queue-and-a-task-queue-Why-would-a-task-queue-require-a-message-broker-like-RabbitMQ-Redis-Celery-or-IronMQ-to-function) - [What is the difference between a message queue and a task queue?](https://www.quora.com/What-is-the-difference-between-a-message-queue-and-a-task-queue-Why-would-a-task-queue-require-a-message-broker-like-RabbitMQ-Redis-Celery-or-IronMQ-to-function)
## Communication ## Communication
@ -1381,23 +1381,23 @@ HTTP is a method for encoding and transporting data between a client and a serve
A basic HTTP request consists of a verb (method) and a resource (endpoint). Below are common HTTP verbs: A basic HTTP request consists of a verb (method) and a resource (endpoint). Below are common HTTP verbs:
| Verb | Description | Idempotent* | Safe | Cacheable | | Verb | Description | Idempotent\* | Safe | Cacheable |
|---|---|---|---|---| | ------ | --------------------------------------------------------- | ------------ | ---- | --------------------------------------- |
| GET | Reads a resource | Yes | Yes | Yes | | 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 | | 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 | | PUT | Creates or replace a resource | Yes | No | No |
| PATCH | Partially updates a resource | No | No | Yes if response contains freshness info | | PATCH | Partially updates a resource | No | No | Yes if response contains freshness info |
| DELETE | Deletes a resource | Yes | No | No | | DELETE | Deletes a resource | Yes | No | No |
*Can be called many times without different outcomes. \*Can be called many times without different outcomes.
HTTP is an application layer protocol relying on lower-level protocols such as **TCP** and **UDP**. HTTP is an application layer protocol relying on lower-level protocols such as **TCP** and **UDP**.
#### Source(s) and further reading: HTTP #### Source(s) and further reading: HTTP
* [What is HTTP?](https://www.nginx.com/resources/glossary/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 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) - [Difference between PUT and PATCH](https://laracasts.com/discuss/channels/general-discussion/whats-the-differences-between-put-and-patch?page=1)
### Transmission control protocol (TCP) ### Transmission control protocol (TCP)
@ -1409,10 +1409,10 @@ HTTP is an application layer protocol relying on lower-level protocols such as *
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: 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 - 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 - [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. 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. 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.
@ -1420,8 +1420,8 @@ TCP is useful for applications that require high reliability but are less time c
Use TCP over UDP when: Use TCP over UDP when:
* You need all of the data to arrive intact - You need all of the data to arrive intact
* You want to automatically make a best estimate use of the network throughput - You want to automatically make a best estimate use of the network throughput
### User datagram protocol (UDP) ### User datagram protocol (UDP)
@ -1439,18 +1439,18 @@ UDP is less reliable but works well in real time use cases such as VoIP, video c
Use UDP over TCP when: Use UDP over TCP when:
* You need the lowest latency - You need the lowest latency
* Late data is worse than loss of data - Late data is worse than loss of data
* You want to implement your own error correction - You want to implement your own error correction
#### Source(s) and further reading: TCP and UDP #### Source(s) and further reading: TCP and UDP
* [Networking for game programming](http://gafferongames.com/networking-for-game-programmers/udp-vs-tcp/) - [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/) - [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) - [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) - [Transmission control protocol](https://en.wikipedia.org/wiki/Transmission_Control_Protocol)
* [User datagram protocol](https://en.wikipedia.org/wiki/User_Datagram_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) - [Scaling memcache at Facebook](http://www.cs.bu.edu/~jappavoo/jappavoo.github.com/451/papers/memcache-fb.pdf)
### Remote procedure call (RPC) ### Remote procedure call (RPC)
@ -1464,12 +1464,12 @@ In an RPC, a client causes a procedure to execute on a different address space,
RPC is a request-response protocol: 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 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 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. - **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 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. - **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. - The server response repeats the steps above in reverse order.
Sample RPC calls: Sample RPC calls:
@ -1487,19 +1487,19 @@ RPC is focused on exposing behaviors. RPCs are often used for performance reaso
Choose a native library (aka SDK) when: Choose a native library (aka SDK) when:
* You know your target platform. - You know your target platform.
* You want to control how your "logic" is accessed. - You want to control how your "logic" is accessed.
* You want to control how error control happens off your library. - You want to control how error control happens off your library.
* Performance and end user experience is your primary concern. - Performance and end user experience is your primary concern.
HTTP APIs following **REST** tend to be used more often for public APIs. HTTP APIs following **REST** tend to be used more often for public APIs.
#### Disadvantage(s): RPC #### Disadvantage(s): RPC
* RPC clients become tightly coupled to the service implementation. - RPC clients become tightly coupled to the service implementation.
* A new API must be defined for every new operation or use case. - A new API must be defined for every new operation or use case.
* It can be difficult to debug RPC. - 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](http://etherealbits.com/2012/12/debunking-the-myths-of-rpc-rest/) on caching servers such as [Squid](http://www.squid-cache.org/). - 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](http://etherealbits.com/2012/12/debunking-the-myths-of-rpc-rest/) on caching servers such as [Squid](http://www.squid-cache.org/).
### Representational state transfer (REST) ### Representational state transfer (REST)
@ -1507,10 +1507,10 @@ REST is an architectural style enforcing a client/server model where the client
There are four qualities of a RESTful interface: There are four qualities of a RESTful interface:
* **Identify resources (URI in HTTP)** - use the same URI regardless of any operation. - **Identify resources (URI in HTTP)** - use the same URI regardless of any operation.
* **Change with representations (Verbs in HTTP)** - use verbs, headers, and body. - **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. - **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. - **[HATEOAS](http://restcookbook.com/Basics/hateoas/) (HTML interface for HTTP)** - your web service should be fully accessible in a browser.
Sample REST calls: Sample REST calls:
@ -1525,15 +1525,15 @@ REST is focused on exposing data. It minimizes the coupling between client/serv
#### Disadvantage(s): REST #### 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. - 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. - 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. - 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. - 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.
### RPC and REST calls comparison ### RPC and REST calls comparison
| Operation | RPC | REST | | Operation | RPC | REST |
|---|---|---| | ------------------------------- | ----------------------------------------------------------------------------------------- | ------------------------------------------------------------ |
| Signup | **POST** /signup | **POST** /persons | | Signup | **POST** /signup | **POST** /persons |
| Resign | **POST** /resign<br/>{<br/>"personid": "1234"<br/>} | **DELETE** /persons/1234 | | Resign | **POST** /resign<br/>{<br/>"personid": "1234"<br/>} | **DELETE** /persons/1234 |
| Read a person | **GET** /readPerson?personid=1234 | **GET** /persons/1234 | | Read a person | **GET** /readPerson?personid=1234 | **GET** /persons/1234 |
@ -1548,14 +1548,14 @@ REST is focused on exposing data. It minimizes the coupling between client/serv
#### Source(s) and further reading: REST and 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/) - [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) - [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) - [REST vs JSON-RPC](http://stackoverflow.com/questions/15056878/rest-vs-json-rpc)
* [Debunking the myths of RPC and REST](http://etherealbits.com/2012/12/debunking-the-myths-of-rpc-rest/) - [Debunking the myths of RPC and REST](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) - [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) - [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/) - [Thrift](https://code.facebook.com/posts/1468950976659943/)
* [Why REST for internal use and not RPC](http://arstechnica.com/civis/viewtopic.php?t=1190508) - [Why REST for internal use and not RPC](http://arstechnica.com/civis/viewtopic.php?t=1190508)
## Security ## Security
@ -1563,16 +1563,16 @@ This section could use some updates. Consider [contributing](#contributing)!
Security is a broad topic. Unless you have considerable experience, a security background, or are applying for a position that requires knowledge of security, you probably won't need to know more than the basics: Security is a broad topic. Unless you have considerable experience, a security background, or are applying for a position that requires knowledge of security, you probably won't need to know more than the basics:
* Encrypt in transit and at rest. - Encrypt in transit and at rest.
* Sanitize all user inputs or any input parameters exposed to user to prevent [XSS](https://en.wikipedia.org/wiki/Cross-site_scripting) and [SQL injection](https://en.wikipedia.org/wiki/SQL_injection). - Sanitize all user inputs or any input parameters exposed to user to prevent [XSS](https://en.wikipedia.org/wiki/Cross-site_scripting) and [SQL injection](https://en.wikipedia.org/wiki/SQL_injection).
* Use parameterized queries to prevent SQL injection. - Use parameterized queries to prevent SQL injection.
* Use the principle of [least privilege](https://en.wikipedia.org/wiki/Principle_of_least_privilege). - Use the principle of [least privilege](https://en.wikipedia.org/wiki/Principle_of_least_privilege).
### Source(s) and further reading ### Source(s) and further reading
* [API security checklist](https://github.com/shieldfy/API-Security-Checklist) - [API security checklist](https://github.com/shieldfy/API-Security-Checklist)
* [Security guide for developers](https://github.com/FallibleInc/security-guide-for-developers) - [Security guide for developers](https://github.com/FallibleInc/security-guide-for-developers)
* [OWASP top ten](https://www.owasp.org/index.php/OWASP_Top_Ten_Cheat_Sheet) - [OWASP top ten](https://www.owasp.org/index.php/OWASP_Top_Ten_Cheat_Sheet)
## Appendix ## Appendix
@ -1595,7 +1595,7 @@ Power Exact Value Approx Value Bytes
#### Source(s) and further reading #### Source(s) and further reading
* [Powers of two](https://en.wikipedia.org/wiki/Power_of_two) - [Powers of two](https://en.wikipedia.org/wiki/Power_of_two)
### Latency numbers every programmer should know ### Latency numbers every programmer should know
@ -1627,12 +1627,12 @@ Notes
Handy metrics based on numbers above: Handy metrics based on numbers above:
* Read sequentially from HDD at 30 MB/s - Read sequentially from HDD at 30 MB/s
* Read sequentially from 1 Gbps Ethernet at 100 MB/s - Read sequentially from 1 Gbps Ethernet at 100 MB/s
* Read sequentially from SSD at 1 GB/s - Read sequentially from SSD at 1 GB/s
* Read sequentially from main memory at 4 GB/s - Read sequentially from main memory at 4 GB/s
* 6-7 world-wide round trips per second - 6-7 world-wide round trips per second
* 2,000 round trips per second within a data center - 2,000 round trips per second within a data center
#### Latency numbers visualized #### Latency numbers visualized
@ -1640,17 +1640,17 @@ Handy metrics based on numbers above:
#### Source(s) and further reading #### Source(s) and further reading
* [Latency numbers every programmer should know - 1](https://gist.github.com/jboner/2841832) - [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) - [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) - [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) - [Software Engineering Advice from Building Large-Scale Distributed Systems](https://static.googleusercontent.com/media/research.google.com/en//people/jeff/stanford-295-talk.pdf)
### Additional system design interview questions ### Additional system design interview questions
> Common system design interview questions, with links to resources on how to solve each. > Common system design interview questions, with links to resources on how to solve each.
| Question | Reference(s) | | Question | Reference(s) |
|---|---| | ----------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Design a file sync service like Dropbox | [youtube.com](https://www.youtube.com/watch?v=PE4gwstWhmc) | | 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)<br/>[stackexchange.com](http://programmers.stackexchange.com/questions/38324/interview-question-how-would-you-implement-google-search)<br/>[ardendertat.com](http://www.ardendertat.com/2012/01/11/implementing-search-engines/)<br/>[stanford.edu](http://infolab.stanford.edu/~backrub/google.html) | | Design a search engine like Google | [queue.acm.org](http://queue.acm.org/detail.cfm?id=988407)<br/>[stackexchange.com](http://programmers.stackexchange.com/questions/38324/interview-question-how-would-you-implement-google-search)<br/>[ardendertat.com](http://www.ardendertat.com/2012/01/11/implementing-search-engines/)<br/>[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 a scalable web crawler like Google | [quora.com](https://www.quora.com/How-can-I-build-a-web-crawler-from-scratch) |
@ -1659,7 +1659,7 @@ Handy metrics based on numbers above:
| Design a cache system like Memcached | [slideshare.net](http://www.slideshare.net/oemebamo/introduction-to-memcached) | | 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)<br/>[ijcai13.org](http://ijcai13.org/files/tutorial_slides/td3.pdf) | | 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)<br/>[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 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 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)<br/>[highscalability.com](http://highscalability.com/blog/2011/12/6/instagram-architecture-14-million-users-terabytes-of-photos.html) | | Design a picture sharing system like Instagram | [highscalability.com](http://highscalability.com/flickr-architecture)<br/>[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)<br/>[quora.com](http://www.quora.com/Activity-Streams/What-are-the-scaling-issues-to-keep-in-mind-while-developing-a-social-network-feed)<br/>[slideshare.net](http://www.slideshare.net/danmckinley/etsy-activity-feeds-architecture) | | Design the Facebook news feed function | [quora.com](http://www.quora.com/What-are-best-practices-for-building-something-like-a-News-Feed)<br/>[quora.com](http://www.quora.com/Activity-Streams/What-are-the-scaling-issues-to-keep-in-mind-while-developing-a-social-network-feed)<br/>[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)<br/>[highscalability.com](http://highscalability.com/blog/2012/1/23/facebook-timeline-brought-to-you-by-the-power-of-denormaliza.html) | | Design the Facebook timeline function | [facebook.com](https://www.facebook.com/note.php?note_id=10150468255628920)<br/>[highscalability.com](http://highscalability.com/blog/2012/1/23/facebook-timeline-brought-to-you-by-the-power-of-denormaliza.html) |
@ -1688,19 +1688,19 @@ Handy metrics based on numbers above:
**Don't focus on nitty gritty details for the following articles, instead:** **Don't focus on nitty gritty details for the following articles, instead:**
* Identify shared principles, common technologies, and patterns within these articles - 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 - Study what problems are solved by each component, where it works, where it doesn't
* Review the lessons learned - Review the lessons learned
|Type | System | Reference(s) | | 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 | **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 | **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 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 | **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 | **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 | **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 | **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 | **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 | **Spanner** - Globally-distributed database from Google | [research.google.com](http://research.google.com/archive/spanner-osdi2012.pdf) |
@ -1711,7 +1711,7 @@ Handy metrics based on numbers above:
| 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) | | 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 | **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 | **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 | **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) | | Misc | **Zookeeper** - Centralized infrastructure and services enabling synchronization | [slideshare.net](http://www.slideshare.net/sauravhaloi/introduction-to-apache-zookeeper) |
| | Add an architecture | [Contribute](#contributing) | | | Add an architecture | [Contribute](#contributing) |
@ -1719,7 +1719,7 @@ Handy metrics based on numbers above:
### Company architectures ### Company architectures
| Company | Reference(s) | | Company | Reference(s) |
|---|---| | -------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Amazon | [Amazon architecture](http://highscalability.com/amazon-architecture) | | 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) | | 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) | | 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) |
@ -1750,60 +1750,60 @@ Handy metrics based on numbers above:
> >
> Questions you encounter might be from the same domain. > Questions you encounter might be from the same domain.
* [Airbnb Engineering](http://nerds.airbnb.com/) - [Airbnb Engineering](http://nerds.airbnb.com/)
* [Atlassian Developers](https://developer.atlassian.com/blog/) - [Atlassian Developers](https://developer.atlassian.com/blog/)
* [AWS Blog](https://aws.amazon.com/blogs/aws/) - [AWS Blog](https://aws.amazon.com/blogs/aws/)
* [Bitly Engineering Blog](http://word.bitly.com/) - [Bitly Engineering Blog](http://word.bitly.com/)
* [Box Blogs](https://blog.box.com/blog/category/engineering) - [Box Blogs](https://blog.box.com/blog/category/engineering)
* [Cloudera Developer Blog](http://blog.cloudera.com/) - [Cloudera Developer Blog](http://blog.cloudera.com/)
* [Dropbox Tech Blog](https://tech.dropbox.com/) - [Dropbox Tech Blog](https://tech.dropbox.com/)
* [Engineering at Quora](https://www.quora.com/q/quoraengineering) - [Engineering at Quora](https://www.quora.com/q/quoraengineering)
* [Ebay Tech Blog](http://www.ebaytechblog.com/) - [Ebay Tech Blog](http://www.ebaytechblog.com/)
* [Evernote Tech Blog](https://blog.evernote.com/tech/) - [Evernote Tech Blog](https://blog.evernote.com/tech/)
* [Etsy Code as Craft](http://codeascraft.com/) - [Etsy Code as Craft](http://codeascraft.com/)
* [Facebook Engineering](https://www.facebook.com/Engineering) - [Facebook Engineering](https://www.facebook.com/Engineering)
* [Flickr Code](http://code.flickr.net/) - [Flickr Code](http://code.flickr.net/)
* [Foursquare Engineering Blog](http://engineering.foursquare.com/) - [Foursquare Engineering Blog](http://engineering.foursquare.com/)
* [GitHub Engineering Blog](https://github.blog/category/engineering) - [GitHub Engineering Blog](https://github.blog/category/engineering)
* [Google Research Blog](http://googleresearch.blogspot.com/) - [Google Research Blog](http://googleresearch.blogspot.com/)
* [Groupon Engineering Blog](https://engineering.groupon.com/) - [Groupon Engineering Blog](https://engineering.groupon.com/)
* [Heroku Engineering Blog](https://engineering.heroku.com/) - [Heroku Engineering Blog](https://engineering.heroku.com/)
* [Hubspot Engineering Blog](http://product.hubspot.com/blog/topic/engineering) - [Hubspot Engineering Blog](http://product.hubspot.com/blog/topic/engineering)
* [High Scalability](http://highscalability.com/) - [High Scalability](http://highscalability.com/)
* [Instagram Engineering](http://instagram-engineering.tumblr.com/) - [Instagram Engineering](http://instagram-engineering.tumblr.com/)
* [Intel Software Blog](https://software.intel.com/en-us/blogs/) - [Intel Software Blog](https://software.intel.com/en-us/blogs/)
* [Jane Street Tech Blog](https://blogs.janestreet.com/category/ocaml/) - [Jane Street Tech Blog](https://blogs.janestreet.com/category/ocaml/)
* [LinkedIn Engineering](http://engineering.linkedin.com/blog) - [LinkedIn Engineering](http://engineering.linkedin.com/blog)
* [Microsoft Engineering](https://engineering.microsoft.com/) - [Microsoft Engineering](https://engineering.microsoft.com/)
* [Microsoft Python Engineering](https://blogs.msdn.microsoft.com/pythonengineering/) - [Microsoft Python Engineering](https://blogs.msdn.microsoft.com/pythonengineering/)
* [Netflix Tech Blog](http://techblog.netflix.com/) - [Netflix Tech Blog](http://techblog.netflix.com/)
* [Paypal Developer Blog](https://medium.com/paypal-engineering) - [Paypal Developer Blog](https://medium.com/paypal-engineering)
* [Pinterest Engineering Blog](https://medium.com/@Pinterest_Engineering) - [Pinterest Engineering Blog](https://medium.com/@Pinterest_Engineering)
* [Reddit Blog](http://www.redditblog.com/) - [Reddit Blog](http://www.redditblog.com/)
* [Salesforce Engineering Blog](https://developer.salesforce.com/blogs/engineering/) - [Salesforce Engineering Blog](https://developer.salesforce.com/blogs/engineering/)
* [Slack Engineering Blog](https://slack.engineering/) - [Slack Engineering Blog](https://slack.engineering/)
* [Spotify Labs](https://labs.spotify.com/) - [Spotify Labs](https://labs.spotify.com/)
* [Twilio Engineering Blog](http://www.twilio.com/engineering) - [Twilio Engineering Blog](http://www.twilio.com/engineering)
* [Twitter Engineering](https://blog.twitter.com/engineering/) - [Twitter Engineering](https://blog.twitter.com/engineering/)
* [Uber Engineering Blog](http://eng.uber.com/) - [Uber Engineering Blog](http://eng.uber.com/)
* [Yahoo Engineering Blog](http://yahooeng.tumblr.com/) - [Yahoo Engineering Blog](http://yahooeng.tumblr.com/)
* [Yelp Engineering Blog](http://engineeringblog.yelp.com/) - [Yelp Engineering Blog](http://engineeringblog.yelp.com/)
* [Zynga Engineering Blog](https://www.zynga.com/blogs/engineering) - [Zynga Engineering Blog](https://www.zynga.com/blogs/engineering)
#### Source(s) and further reading #### Source(s) and further reading
Looking to add a blog? To avoid duplicating work, consider adding your company blog to the following repo: 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) - [kilimchoi/engineering-blogs](https://github.com/kilimchoi/engineering-blogs)
## Under development ## Under development
Interested in adding a section or helping complete one in-progress? [Contribute](#contributing)! Interested in adding a section or helping complete one in-progress? [Contribute](#contributing)!
* Distributed computing with MapReduce - Distributed computing with MapReduce
* Consistent hashing - Consistent hashing
* Scatter gather - Scatter gather
* [Contribute](#contributing) - [Contribute](#contributing)
## Credits ## Credits
@ -1811,15 +1811,15 @@ Credits and sources are provided throughout this repo.
Special thanks to: Special thanks to:
* [Hired in tech](http://www.hiredintech.com/system-design/the-system-design-process/) - [Hired in tech](http://www.hiredintech.com/system-design/the-system-design-process/)
* [Cracking the coding interview](https://www.amazon.com/dp/0984782850/) - [Cracking the coding interview](https://www.amazon.com/dp/0984782850/)
* [High scalability](http://highscalability.com/) - [High scalability](http://highscalability.com/)
* [checkcheckzz/system-design-interview](https://github.com/checkcheckzz/system-design-interview) - [checkcheckzz/system-design-interview](https://github.com/checkcheckzz/system-design-interview)
* [shashank88/system_design](https://github.com/shashank88/system_design) - [shashank88/system_design](https://github.com/shashank88/system_design)
* [mmcgrana/services-engineering](https://github.com/mmcgrana/services-engineering) - [mmcgrana/services-engineering](https://github.com/mmcgrana/services-engineering)
* [System design cheat sheet](https://gist.github.com/vasanthk/485d1c25737e8e72759f) - [System design cheat sheet](https://gist.github.com/vasanthk/485d1c25737e8e72759f)
* [A distributed systems reading list](http://dancres.github.io/Pages/) - [A distributed systems reading list](http://dancres.github.io/Pages/)
* [Cracking the system design interview](http://www.puncsky.com/blog/2016-02-13-crack-the-system-design-interview) - [Cracking the system design interview](http://www.puncsky.com/blog/2016-02-13-crack-the-system-design-interview)
## Contact info ## Contact info
@ -1829,7 +1829,7 @@ My contact info can be found on my [GitHub page](https://github.com/donnemartin)
## License ## License
*I am providing code and resources in this repository to you under an open source license. Because this is my personal repository, the license you receive to my code and resources is from me and not my employer (Facebook).* _I am providing code and resources in this repository to you under an open source license. Because this is my personal repository, the license you receive to my code and resources is from me and not my employer (Facebook)._
Copyright 2017 Donne Martin Copyright 2017 Donne Martin