Startup Resources: Scalability

Learn how to scale your tech stack to handle more users, more traffic, and more data. See the "Scalability" chapter in Part II, Technologies for more info.

Scalability Patterns
Distributed Systems Reading
Performance profiling and debugging tools and techniques
Further reading

These startup resources are based on the book Hello, Startup: A Programmer's Guide to Building Products, Technologies, and Teams by Yevgeniy Brikman. These resources are a work in a progress. They are also open source, so you can add your contributions by submitting a pull request to the Hello, Startup GitHub Repository. To see how these resources fit into the bigger picture, check out the The Startup Checklist, which is a comprehensive collection of everything you need to do to launch a startup.

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Scalability Patterns

Building Software Systems At Google and Lessons Learned

https://www.youtube.com/watch?v=modXC5IWTJI

Jeff Dean's talk at Stanford EE Computer Systems Colloquium. See also the slides.

Scalability at YouTube

https://www.youtube.com/watch?v=G-lGCC4KKok

A talk by one of the original engineers at YouTube, Mike Solomon.

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8 Commonly Used Scalable System Design Patterns

http://highscalability.com/blog/2010/12/1/8-commonly-used-scalable-system-design-patterns.html

Thinking at Scale

http://www.cloudera.com/content/cloudera/en/resources/library/training/thinking-at-scale.html

20 Obstacles to Scalability

http://queue.acm.org/detail.cfm?id=2512489

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The Canonical Cloud Architecture

http://highscalability.com/blog/2009/8/7/the-canonical-cloud-architecture.html

Real World Web: Performance & Scalability

http://develooper.com/talks/rww-mysql-2008.pdf

Optimizing the Critical Rendering Path

http://www.sitepoint.com/optimizing-critical-rendering-path/

Breaking the 1000ms Time to Glass Mobile Barrier

https://www.youtube.com/watch?v=Il4swGfTOSM

Scalable Web Architecture and Distributed Systems

http://aosabook.org/en/distsys.html

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Introduction to Architecting Systems for Scale

http://lethain.com/introduction-to-architecting-systems-for-scale/

How to start learning high scalability

http://leandromoreira.com.br/2014/11/20/how-to-start-to-learn-high-scalability/

Google Pro Tip: Use Back-Of-The-Envelope-Calculations To Choose The Best Design

http://highscalability.com/blog/2011/1/26/google-pro-tip-use-back-of-the-envelope-calculations-to-choo.html

Latency Numbers Every Programmer Should Know

http://www.eecs.berkeley.edu/~rcs/research/interactive_latency.html

Distributed Systems Reading

Distributed systems theory for the distributed systems engineer

http://the-paper-trail.org/blog/distributed-systems-theory-for-the-distributed-systems-engineer/

Readings in Distributed Systems

http://bytepawn.com/readings-in-distributed-systems/

Readings in distributed systems

http://christophermeiklejohn.com/distributed/systems/2013/07/12/readings-in-distributed-systems.html

Papers We Love

https://github.com/papers-we-love/papers-we-love

Papers We Love is a community built around reading, discussing and learning more about academic computer science papers. This repository serves as a directory of some of the best papers the community can find, bringing together documents scattered across the web.

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What are the seminal papers in distributed systems?

http://www.quora.com/What-are-the-seminal-papers-in-distributed-systems-Why

Advanced Distributed Systems

https://courses.engr.illinois.edu/cs525/sp2011/sched.htm

Syllabus from the CS 525 course at University of Illinois at Urbana-Champaign.

Performance profiling and debugging tools and techniques

Performance Analysis Methodology

http://www.brendangregg.com/methodology.html

A performance analysis methodology is a procedure that you can follow to analyze system or application performance. These generally provide a starting point and then guidance to root cause, or causes.

USE Method: Linux Performance Checklist

http://www.brendangregg.com/USEmethod/use-linux.html

The USE Method provides a strategy for performing a complete check of system health, identifying common bottlenecks and errors. For each system resource, metrics for utilization, saturation and errors are identified and checked.

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