* [Additional system design interview questions](#additional-system-design-interview-questions)
#### For interviews, do I need to know everything here?
**No, you don't need to know everything here to prepare for the interview**.
What you are asked in an interview depends on variables such as:
* How much experience you have
* What your technical background is
* What positions you are interviewing for
* Which companies you are interviewing with
* Luck
More experienced candidates are generally expected to know more about system design. Architects or team leads might be expected to know more than individual contributors. Top tech companies are likely to have one or more design interview rounds.
#### Any resources to prep for coding interviews?
Check out the sister repo [**interactive-coding-challenges**](https://github.com/donnemartin/interactive-coding-challenges) for coding interview resources.
> Suggested topics to review based on your interview timeline (short, medium, long).
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 experience, what positions you are interviewing for, and which companies you are interviewing with.
* **Short** - Aim for **breadth** with system design topics. Practice by solving **some** interview questions.
* **Medium** - Aim for **breadth** and **some depth** with system design topics. Practice by solving a **many** interview questions.
* **Long** - Aim for **breadth** and **more depth** with system design topics. Practice by solving a **most** interview questions.
| | Short | Medium | Long |
|---|---|---|---|
| Read through the [System design topics](#index-of-system-design-topics) to get a broad understanding of how systems work | :+1: | :+1: | :+1: |
| Read through a few articles in the [Company engineering blogs](#company-engineering-blogs) for the companies you are interviewing with | :+1: | :+1: | :+1: |
| Read through a few [Real world architectures](#real-world-architectures) | :+1: | :+1: | :+1: |
| Review [How to approach a system design interview question](#how-to-approach-a-system-design-interview-question) | :+1: | :+1: | :+1: |
| Work through [System design interview questions with solutions](#system-design-interview-questions-with-solutions) | Some | Many | Most |
| Work through [Object-oriented design interview questions with solutions](#object-oriented-design-interview-questions-with-solutions) | Some | Many | Most |
| Review [Additional system design interview questions](#additional-system-design-interview-questions) | Some | Many | Most |
## How to approach a system design interview question
> How to tackle a system design interview question.
The system design interview is an **open-ended conversation**. You are expected to lead it.
You can use the following steps to guide the discussion. To help solidify this process, work through the [System design interview questions with solutions](#system-design-interview-questions-with-solutions) section using the following steps.
### Step 1: Outline use cases, constraints, and assumptions
Gather requirements and scope the problem. Ask questions to clarify use cases and constraints. Discuss assumptions.
* Who is going to use it?
* How are they going to use it?
* How many users are there?
* What does the system do?
* What are the inputs and outputs of the system?
* How much data do we expect to handle?
* How many requests per second do we expect?
* What is the expected read to write ratio?
### Step 2: Create a high level design
Outline a high level design with all important components.
Dive into details for each core component. For example, if you were asked to [design a url shortening service](https://github.com/donnemartin/system-design-primer/blob/master/solutions/system_design/pastebin/README.md), discuss:
* [MD5](https://github.com/donnemartin/system-design-primer/blob/master/solutions/system_design/pastebin/README.md) and [Base62](https://github.com/donnemartin/system-design-primer/blob/master/solutions/system_design/pastebin/README.md)
Identify and address bottlenecks, given the constraints. For example, do you need the following to address scalability issues?
* Load balancer
* Horizontal scaling
* Caching
* Database sharding
Discuss potential solutions and trade-offs. Everything is a trade-off. Address bottlenecks using [principles of scalable system design](#index-of-system-design-topics).
### Back-of-the-envelope calculations
You might be asked to do some estimates by hand. Refer to the [Appendix](#appendix) for the following resources:
* [Use back of the envelope calculations](http://highscalability.com/blog/2011/1/26/google-pro-tip-use-back-of-the-envelope-calculations-to-choo.html)
* [Powers of two table](#powers-of-two-table)
* [Latency numbers every programmer should know](#latency-numbers-every-programmer-should-know)
### Source(s) and further reading
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/)
* [The system design interview](http://www.hiredintech.com/system-design)
* [Intro to Architecture and Systems Design Interviews](https://www.youtube.com/watch?v=ZgdS0EUmn70)
| Design the data structures for a social network | [Solution](https://github.com/donnemartin/system-design-primer/blob/master/solutions/system_design/social_graph/README.md) |
| Design a key-value store for a search engine | [Solution](https://github.com/donnemartin/system-design-primer/blob/master/solutions/system_design/query_cache/README.md) |
| Design a system that scales to millions of users on AWS | [Solution](https://github.com/donnemartin/system-design-primer/blob/master/solutions/system_design/scaling_aws/README.md) |
## Object-oriented design interview questions with solutions
> Common object-oriented design interview questions with sample discussions, code, and diagrams.
>
> Solutions linked to content in the `solutions/` folder.
>**Note: This section is under development**
| Question | |
|---|---|
| Design a deck of cards to be used for blackjack | [Solution](https://github.com/donnemartin/system-design-primer/blob/master/solutions/object_oriented_design/deck_of_cards/deck_of_cards.ipynb) |
| Design a call center | [Solution](https://github.com/donnemartin/system-design-primer/blob/master/solutions/object_oriented_design/call_center/call_center.ipynb) |
| Design a hash map | [Solution](https://github.com/donnemartin/system-design-primer/blob/master/solutions/object_oriented_design/hash_table/hash_map.ipynb) |
| Design a least recently used cache | [Solution](https://github.com/donnemartin/system-design-primer/blob/master/solutions/object_oriented_design/lru_cache/lru_cache.ipynb) |
| Design a parking lot | [Solution](https://github.com/donnemartin/system-design-primer/blob/master/solutions/object_oriented_design/parking_lot/parking_lot.ipynb) |
| Design a chat server | [Solution](https://github.com/donnemartin/system-design-primer/blob/master/solutions/object_oriented_design/chat_server/chat_server.ipynb) |
| Design a circular array | [Contribute](#contributing) |
| Add an object-oriented design question | [Contribute](#contributing) |
> Common system design interview questions, with links to resources on how to solve each.
| Question | Reference(s) |
|---|---|
| Design a file sync service like Dropbox | [youtube.com](https://www.youtube.com/watch?v=PE4gwstWhmc) |
| Design a search engine like Google | [queue.acm.org](http://queue.acm.org/detail.cfm?id=988407)<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 Google docs | [code.google.com](https://code.google.com/p/google-mobwrite/)<br/>[neil.fraser.name](https://neil.fraser.name/writing/sync/) |
| Design a key-value store like Redis | [slideshare.net](http://www.slideshare.net/dvirsky/introduction-to-redis) |
| Design a cache system like Memcached | [slideshare.net](http://www.slideshare.net/oemebamo/introduction-to-memcached) |
| Design a recommendation system like Amazon's | [hulu.com](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 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 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 chat function | [erlang-factory.com](http://www.erlang-factory.com/upload/presentations/31/EugeneLetuchy-ErlangatFacebook.pdf)<br/>[facebook.com](https://www.facebook.com/note.php?note_id=14218138919&id=9445547199&index=0) |
| Design a graph search function like Facebook's | [facebook.com](https://www.facebook.com/notes/facebook-engineering/under-the-hood-building-out-the-infrastructure-for-graph-search/10151347573598920)<br/>[facebook.com](https://www.facebook.com/notes/facebook-engineering/under-the-hood-indexing-and-ranking-in-graph-search/10151361720763920)<br/>[facebook.com](https://www.facebook.com/notes/facebook-engineering/under-the-hood-the-natural-language-interface-of-graph-search/10151432733048920) |
| Design a content delivery network like CloudFlare | [cmu.edu](http://repository.cmu.edu/cgi/viewcontent.cgi?article=2112&context=compsci) |
| Design a trending topic system like Twitter's | [michael-noll.com](http://www.michael-noll.com/blog/2013/01/18/implementing-real-time-trending-topics-in-storm/)<br/>[snikolov .wordpress.com](http://snikolov.wordpress.com/2012/11/14/early-detection-of-twitter-trends/) |
| Design a random ID generation system | [blog.twitter.com](https://blog.twitter.com/2010/announcing-snowflake)<br/>[github.com](https://github.com/twitter/snowflake/) |
| Return the top k requests during a time interval | [ucsb.edu](https://icmi.cs.ucsb.edu/research/tech_reports/reports/2005-23.pdf)<br/>[wpi.edu](http://davis.wpi.edu/xmdv/docs/EDBT11-diyang.pdf) |
| Design a system that serves data from multiple data centers | [highscalability.com](http://highscalability.com/blog/2009/8/24/how-google-serves-data-from-multiple-datacenters.html) |
| Design an online multiplayer card game | [indieflashblog.com](http://www.indieflashblog.com/how-to-create-an-asynchronous-multiplayer-game.html)<br/>[buildnewgames.com](http://buildnewgames.com/real-time-multiplayer/) |
| Design a garbage collection system | [stuffwithstuff.com](http://journal.stuffwithstuff.com/2013/12/08/babys-first-garbage-collector/)<br/>[washington.edu](http://courses.cs.washington.edu/courses/csep521/07wi/prj/rick.pdf) |
| Add a system design question | [Contribute](#contributing) |
> Articles on how real world systems are designed.
<palign="center">
<imgsrc="http://i.imgur.com/TcUo2fw.png">
<br/>
<i><ahref=https://www.infoq.com/presentations/Twitter-Timeline-Scalability>Source: Twitter timelines at scale</a></i>
</p>
**Don't focus on nitty gritty details for the following articles, instead:**
* Identify shared principles, common technologies, and patterns within these articles
* Study what problems are solved by each component, where it works, where it doesn't
* Review the lessons learned
|Type | System | Reference(s) |
|---|---|---|
| Data processing | **MapReduce** - Distributed data processing from Google | [research.google.com](http://static.googleusercontent.com/media/research.google.com/zh-CN/us/archive/mapreduce-osdi04.pdf) |
| Data processing | **Spark** - Distributed data processing from Databricks | [slideshare.net](http://www.slideshare.net/AGrishchenko/apache-spark-architecture) |
| Data processing | **Storm** - Distributed data processing from Twitter | [slideshare.net](http://www.slideshare.net/previa/storm-16094009) |
| | | |
| Data store | **Bigtable** - Distributed column-oriented database from Google | [harvard.edu](http://www.read.seas.harvard.edu/~kohler/class/cs239-w08/chang06bigtable.pdf) |
| Data store | **HBase** - Open source implementation of Bigtable | [slideshare.net](http://www.slideshare.net/alexbaranau/intro-to-hbase) |
| Data store | **Cassandra** - Distributed column-oriented database from Facebook | [slideshare.net](http://www.slideshare.net/planetcassandra/cassandra-introduction-features-30103666)
| Data store | **DynamoDB** - Document-oriented database from Amazon | [harvard.edu](http://www.read.seas.harvard.edu/~kohler/class/cs239-w08/decandia07dynamo.pdf) |
| Data store | **MongoDB** - Document-oriented database | [slideshare.net](http://www.slideshare.net/mdirolf/introduction-to-mongodb) |
| Data store | **Spanner** - Globally-distributed database from Google | [research.google.com](http://research.google.com/archive/spanner-osdi2012.pdf) |
| Data store | **Memcached** - Distributed memory caching system | [slideshare.net](http://www.slideshare.net/oemebamo/introduction-to-memcached) |
| Data store | **Redis** - Distributed memory caching system with persistence and value types | [slideshare.net](http://www.slideshare.net/dvirsky/introduction-to-redis) |
| | | |
| File system | **Google File System (GFS)** - Distributed file system | [research.google.com](http://static.googleusercontent.com/media/research.google.com/zh-CN/us/archive/gfs-sosp2003.pdf) |
| File system | **Hadoop File System (HDFS)** - Open source implementation of GFS | [apache.org](https://hadoop.apache.org/docs/r1.2.1/hdfs_design.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) |
| ESPN | [Operating At 100,000 duh nuh nuhs per second](http://highscalability.com/blog/2013/11/4/espns-architecture-at-scale-operating-at-100000-duh-nuh-nuhs.html) |
| Google | [Google architecture](http://highscalability.com/google-architecture) |
| Instagram | [14 million users, terabytes of photos](http://highscalability.com/blog/2011/12/6/instagram-architecture-14-million-users-terabytes-of-photos.html)<br/>[What powers Instagram](http://instagram-engineering.tumblr.com/post/13649370142/what-powers-instagram-hundreds-of-instances) |
| Justin.tv | [Justin.Tv's live video broadcasting architecture](http://highscalability.com/blog/2010/3/16/justintvs-live-video-broadcasting-architecture.html) |
| Facebook | [Scaling memcached at Facebook](https://cs.uwaterloo.ca/~brecht/courses/854-Emerging-2014/readings/key-value/fb-memcached-nsdi-2013.pdf)<br/>[TAO: Facebook’s distributed data store for the social graph](https://cs.uwaterloo.ca/~brecht/courses/854-Emerging-2014/readings/data-store/tao-facebook-distributed-datastore-atc-2013.pdf)<br/>[Facebook’s photo storage](https://www.usenix.org/legacy/event/osdi10/tech/full_papers/Beaver.pdf) |
| Mailbox | [From 0 to one million users in 6 weeks](http://highscalability.com/blog/2013/6/18/scaling-mailbox-from-0-to-one-million-users-in-6-weeks-and-1.html) |
| Pinterest | [From 0 To 10s of billions of page views a month](http://highscalability.com/blog/2013/4/15/scaling-pinterest-from-0-to-10s-of-billions-of-page-views-a.html)<br/>[18 million visitors, 10x growth, 12 employees](http://highscalability.com/blog/2012/5/21/pinterest-architecture-update-18-million-visitors-10x-growth.html) |
| Playfish | [50 million monthly users and growing](http://highscalability.com/blog/2010/9/21/playfishs-social-gaming-architecture-50-million-monthly-user.html) |
| Tumblr | [15 billion page views a month](http://highscalability.com/blog/2012/2/13/tumblr-architecture-15-billion-page-views-a-month-and-harder.html) |
| Twitter | [Making Twitter 10000 percent faster](http://highscalability.com/scaling-twitter-making-twitter-10000-percent-faster)<br/>[Storing 250 million tweets a day using MySQL](http://highscalability.com/blog/2011/12/19/how-twitter-stores-250-million-tweets-a-day-using-mysql.html)<br/>[150M active users, 300K QPS, a 22 MB/S firehose](http://highscalability.com/blog/2013/7/8/the-architecture-twitter-uses-to-deal-with-150m-active-users.html)<br/>[Timelines at scale](https://www.infoq.com/presentations/Twitter-Timeline-Scalability)<br/>[Big and small data at Twitter](https://www.youtube.com/watch?v=5cKTP36HVgI)<br/>[Operations at Twitter: scaling beyond 100 million users](https://www.youtube.com/watch?v=z8LU0Cj6BOU) |
A service is **scalable** if it results in increased **performance** in a manner proportional to resources added. Generally, increasing performance means serving more units of work, but it can also be to handle larger units of work, such as when datasets grow.<sup><ahref=http://www.allthingsdistributed.com/2006/03/a_word_on_scalability.html>1</a></sup>
Another way to look at performance vs scalability:
* If you have a **performance** problem, your system is slow for a single user.
* If you have a **scalability** problem, your system is fast for a single user but slow under heavy load.
### Source(s) and further reading
* [A word on scalability](http://www.allthingsdistributed.com/2006/03/a_word_on_scalability.html)
<i><ahref=http://robertgreiner.com/2014/08/cap-theorem-revisited>Source: CAP theorem revisited</a></i>
</p>
In a distributed computer system, you can only support two of the following guarantees:
* **Consistency** - Every read receives the most recent write or an error
* **Availability** - Every request receives a response, without guarantee that it contains the most recent version of the information
* **Partition Tolerance** - The system continues to operate despite arbitrary partitioning due to network failures
*Networks aren't reliable, so you'll need to support partition tolerance. You'll need to make a software tradeoff between consistency and availability.*
#### CP - consistency and partition tolerance
Waiting for a response from the partitioned node might result in a timeout error. CP is a good choice if your business needs require atomic reads and writes.
#### AP - availability and partition tolerance
Responses return the most recent version of the data, which might not be the latest. Writes might take some time to propagate when the partition is resolved.
AP is a good choice if the business needs allow for [eventual consistency](#eventual-consistency) or when the system needs to continue working despite external errors.
With multiple copies of the same data, we are faced with options on how to synchronize them so clients have a consistent view of the data. Recall the definition of consistency from the [CAP theorem](#cap-theorem) - Every read receives the most recent write or an error.
### Weak consistency
After a write, reads may or may not see it. A best effort approach is taken.
This approach is seen in systems such as memcached. Weak consistency works well in real time use cases such as VoIP, video chat, and realtime multiplayer games. For example, if you are on a phone call and lose reception for a few seconds, when you regain connection you do not hear what was spoken during connection loss.
### Eventual consistency
After a write, reads will eventually see it (typically within milliseconds). Data is replicated asynchronously.
This approach is seen in systems such as DNS and email. Eventual consistency works well in highly available systems.
### Strong consistency
After a write, reads will see it. Data is replicated synchronously.
This approach is seen in file systems and RDBMSes. Strong consistency works well in systems that need transactions.
### Source(s) and further reading
* [Transactions across data centers](http://snarfed.org/transactions_across_datacenters_io.html)
There are two main patterns to support high availability: **fail-over** and **replication**.
### Fail-over
#### Active-passive
With active-passive fail-over, heartbeats are sent between the active and the passive server on standby. If the heartbeat is interrupted, the passive server takes over the active's IP address and resumes service.
The length of downtime is determined by whether the passive server is already running in 'hot' standy or whether it needs to start up from 'cold' standby. Only the active server handles traffic.
Active-passive failover can also be referred to as master-slave failover.
#### Active-active
In active-active, both servers are managing traffic, spreading the load between them.
If the servers are public-facing, the DNS would need to know about the public IPs of both servers. If the servers are internal-facing, application logic would need to know about both servers.
Active-active failover can also be referred to as master-master failover.
### Disadvantage(s): failover
* 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.
### Replication
#### Master-slave and master-master
This topic is further discussed in the [Database](#database) section:
