References

This document serves as a curated directory of real-world system architectures, foundational distributed systems papers, and official company engineering blogs.

How to Study These Architectures

Do not get bogged down in the nitty-gritty details of each article. Instead, focus on the following:

Identify Patterns: Look for shared principles, common technologies, and recurring patterns (e.g., how often Redis is used for caching, or Kafka for decoupling).
Understand the "Why": Study the specific problems solved by each component, where the architecture excels, and where it falls short.
Extract Lessons Learned: Pay attention to the engineering trade-offs and retrospective lessons shared by the authors.

Foundational Systems

These papers and presentations detail the core distributed systems and databases that power modern infrastructure.

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

Company Architectures

Deep dives into how major tech companies handle massive scale, traffic spikes, and data persistence.

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

Engineering Blogs

Stay up to date with how industry leaders tackle evolving architectural challenges. These blogs are highly recommended reading before interviewing with the respective companies, as interview questions are frequently drawn from their real-world engineering hurdles.