tag
Cap Theorem
- #38 4 min
CAP and the Illusion of Choice
The CAP theorem is widely discussed and often misunderstood within the world of distributed systems. It states that any networked, shared-data system can, at most, guarantee two of three properties: consistency, availability, and partition tolerance. I won’t go into detail on CAP since the literature is abundant, but the notion of “two of three”—while conceptually accessible—is utterly misleading. Brewer has indicated this, echoed by many more, but there still seems to be a lot of confusion when the topic is brought up. The bottom line is you can’t sacrifice partition tolerance, but it seems CAP is a bit more nuanced than that.
- #36 7 min
You Cannot Have Exactly-Once Delivery
I’m often surprised that people continually have fundamental misconceptions about how distributed systems behave. I myself shared many of these misconceptions, so I try not to demean or dismiss but rather educate and enlighten, hopefully while sounding less preachy than that just did. I continue to learn only by following in the footsteps of others. In retrospect, it shouldn’t be surprising that folks buy into these fallacies as I once did, but it can be frustrating when trying to communicate certain design decisions and constraints.
- #35 8 min
If State Is Hell, SOA Is Satan
More and more companies are describing their success stories regarding the switch to a service-oriented architecture. As with any technological upswing, there’s a clear and palpable hype factor involved (Big Data™ or The Cloud™ anyone?), but obviously it’s not just puff. While microservices and SOA have seen a staggering rate of adoption in recent years, the mindset of developers often seems to be stuck in the past. I think this is, at least in part, because we seek a mental model we can reason about. It’s why we build abstractions in the first place. In a sense, I would argue there’s a comparison to be made between the explosion of OOP in the early 90’s and today’s SOA trend. After all, SOA is as much about people scale as it is about workload scale, so it makes sense from an organizational perspective.
- #27 1 min
From Mainframe to Microservice: An Introduction to Distributed Systems
I gave a talk at Iowa Code Camp this weekend on distributed systems. It was primarily an introduction to them, so it explored some core concepts at a high level. We looked at why distributed systems are difficult to build (right), the CAP theorem, consensus, scaling shared data and CRDTs. There was some interest in making the slides available online. I’m not sure how useful they are without narration, but here they are anyway for posterity.
- #26 5 min
Scaling Shared Data in Distributed Systems
Sharing mutable data at large scale is an exceedingly difficult problem. In their seminal paper CRDTs: Consistency without concurrency control, Shapiro et al. describe why the CAP theorem demands a give and take between scalability and consistency. In general, CAP requires us to choose between CP and AP. The former requires serializing every write, which doesn’t scale beyond a small cluster. The latter ensures scalability by giving up consistency. Sharing Data in Centralized Systems We tend to prefer weaker consistency models because they mean lower latency and higher availability. To highlight this point, consider the fact that the memory models for most programming languages are not serializable by default. More concisely, programs with shared memory are not inherently thread-safe. This is a conscious design decision because enforcing memory serializability incurs a significant latency penalty. Instead, programming languages require explicit memory barriers which can be used around the critical sections which need this property.