tag
Scalability
- #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.
- #34 19 min
Stream Processing and Probabilistic Methods: Data at Scale
Stream processing and related abstractions have become all the rage following the rise of systems like Apache Kafka, Samza, and the Lambda architecture. Applying the idea of immutable, append-only event sourcing means we’re storing more data than ever before. However, as the cost of storage continues to decline, it’s becoming more feasible to store more data for longer periods of time. With immutability, how the data lives isn’t interesting anymore. It’s all about how it moves.
- #31 5 min
Fast, Scalable Networking in Go with Mangos
In the past, I’ve looked at nanomsg and why it’s a formidable alternative to the well-regarded ZeroMQ. Like ZeroMQ, nanomsg is a native library which markets itself as a way to build fast and scalable networking layers. I won’t go into detail on how nanomsg accomplishes this since my analysis of it already covers that fairly extensively, but instead I want to talk about a Go implementation of the protocol called Mangos. ((Full disclosure: I am a contributor on the Mangos project, but only because I was a user first!)) If you’re not familiar with nanomsg or Scalability Protocols, I recommend reading my overview of those first.
- #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.
- #22 12 min
Dissecting Message Queues
Disclaimer (10/29/20) – The benchmarks and performance analysis presented in this post should not be relied on. This post was written roughly six years ago, and at the time, was just the result of my exploration of various messaging systems. The benchmarks are not implemented in a meaningful way, which I discussed in a follow-up post. This post will remain for posterity and learning purposes, but I do not claim that this information is accurate or useful.