CAP Theorem: You don’t need CP, you don’t want AP, and you can’t have CA
Submitted by Siddhartha Reddy (@sids) on Sunday, 14 June 2015
Section: Full Talk Technical level: Intermediate
Abstract
CAP Theorem is everywhere: “Consistency, Availability, Partition tolerance — choose any two!” But it is oversimplified and misunderstood more often than not. CAP’s consistency isn’t what most people think it is; CAP’s availability isn’t what most people think it is; what does partitiontolerance even mean?
In this talk we’ll explore the CAPtheorem and understand what it is really asserting. We’ll understand that just calling a system out as CP or AP (or even CA) is pretty pointless, and learn to judge them beyond the simple monikers. We’ll also analyse some popular databases of the world (Cassandra, MongoDB, HBase, MySQL etc.) with this framework.
Outline
If you use any distributed database, you would have surely heard of the CAP Theorem: “Consistency, Availability, Partition tolerance — choose any two!” But CAP theorem is a little more nuanced than that, and it’s impact on how you should choose and operate distributed databases even more so.
Consider these two examples:
 A distributed keyvalue store where nodes accept all writes but then respond to all read requests with a null.
 A distributed keyvalue store where every node rejects all write as well as read requests.
We can build the above systems using just some bash utilities. The first is an example of an AP system and the second is an example of a CP system; in fact, these examples are moreorless straight out of the paper that proved the CAP Theorem. Given that such trivial systems can be classified as CP/AP, is CAPTheorem even a viable framework for analysing distributed databases?
Tentatively, the talk will cover:
 intro to CAP theorem (the assertion; meaning of C, A, & P; proof)
 examples of trivial CP & AP systems
 why CA is not meaningful, CP is not viable, AP is not useful
 alternatives to CAP theorem
 analysis of a few realworld databases
Speaker bio
Siddhartha is an Architect at Flipkart, presently working on the company’s central Data Platform. He had earlier built and operated Flipkart.com’s search system and offers platform for a couple of years each. Through these and other systems he has been exposed to several distributed data systems with varying needs of scale, consistency, & availability. Siddhartha’s areas of interest include distributed systems, databases, and information retrieval.
Links
 A mindmap of the talk's content: https://dl.dropboxusercontent.com/u/163925/CAPTheoremFifthElephant2015.pdf
Comments


Vishal Uderani (@vishalud)

Kiran Jonnalagadda (@jace)
Sid, how different is your talk from this blog post?

Siddhartha Reddy (@sids) Proposer
Kiran, I will indeed be covering aspects of what Martin has written about i.e. that the CAPtheorem does not accurately represent the tradeoffs that exist in distributed databases. But there is a tradeoff nevertheless and I will be talking about how to make those tradeoffs, and what those tradeoffs would mean for applications. I’ll also present some alternative frameworks for analysing these tradeoffs. I’m also hoping to present the analyses of a few realworld databases as a means to demonstrate how to go about thinking about these tradeoffs in the context of evaluating architectural choices.
Please refer to the mindmap of the talk linked to above for an overview of the talk’s planned content.



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