Systems on Brave New Geek

Take It to the Limit: Considerations for Building Reliable Systems

Tue, 20 Dec 2016 19:55:52 -0600

Complex systems usually operate in failure mode. This is because a complex system typically consists of many discrete pieces, each of which can fail in isolation (or in concert). In a microservice architecture where a given function potentially comprises several independent service calls, high availability hinges on the ability to be partially available. This is a core tenet behind resilience engineering. If a function depends on three services, each with a reliability of 90%, 95%, and 99%, respectively, partial availability could be the difference between 99.995% reliability and 84% reliability (assuming failures are independent). Resilience engineering means designing with failure as the normal.

Benchmarking Commit Logs

Sun, 27 Nov 2016 13:28:55 -0600

In this article, we look at Apache Kafka and NATS Streaming, two messaging systems based on the idea of a commit log. We’ll compare some of the features of both but spend less time talking about Kafka since by now it’s quite well known. Similar to previous studies, we’ll attempt to quantify their general performance characteristics through careful benchmarking.

The purpose of this benchmark is to test drive the newly released NATS Streaming system, which was made generally available just in the last few months. NATS Streaming doesn’t yet support clustering, so we try to put its performance into context by looking at a similar configuration of Kafka.

You Are Not Paid to Write Code

Wed, 16 Nov 2016 22:20:11 -0600

“Taco Bell Programming” is the idea that we can solve many of the problems we face as software engineers with clever reconfigurations of the same basic Unix tools. The name comes from the fact that every item on the menu at Taco Bell, a company which generates almost $2 billion in revenue annually, is simply a different configuration of roughly eight ingredients.

Many people grumble or reject the notion of using proven tools or techniques. It’s boring. It requires investing time to learn at the expense of shipping code. It doesn’t do this one thing that we need it to do. It won’t work for us. For some reason—and I continue to be completely baffled by this—everyone sees their situation as a unique snowflake despite the fact that a million other people have probably done the same thing. It’s a weird form of tunnel vision, and I see it at every level in the organization. I catch myself doing it on occasion too. I think it’s just human nature.

Benchmarking Message Queue Latency

Sat, 13 Feb 2016 16:23:39 -0600

About a year and a half ago, I published Dissecting Message Queues, which broke down a few different messaging systems and did some performance benchmarking. It was a naive attempt and had a lot of problems, but it was also my first time doing any kind of system benchmarking. It turns out benchmarking systems correctly is actually pretty difficult and many folks get it wrong. I don’t claim to have gotten it right, but over the past year and a half I’ve learned a lot, tried to build some better tools, and improve my methodology.

From the Ground Up: Reasoning About Distributed Systems in the Real World

Fri, 01 Jan 2016 14:26:50 -0600

The rabbit hole is deep. Down and down it goes. Where it ends, nobody knows. But as we traverse it, patterns appear. They give us hope, they quell the fear.

Distributed systems literature is abundant, but as a practitioner, I often find it difficult to know where to start or how to synthesize this knowledge without a more formal background. This is a non-academic’s attempt to provide a line of thought for rationalizing design decisions. This piece doesn’t necessarily contribute any new ideas but rather tries to provide a holistic framework by studying some influential existing ones. It includes references which provide a good starting point for thinking about distributed systems. Specifically, we look at a few formal results and slightly less formal design principles to provide a basis from which we can argue about system design.

Designed to Fail

Tue, 21 Jul 2015 20:17:17 -0500

When it comes to reliability engineering, people often talk about things like fault injection, monitoring, and operations runbooks. These are all critical pieces for building systems which can withstand failure, but what’s less talked about is the need to design systems which deliberately fail.

Reliability design has a natural progression which closely follows that of architectural design. With monolithic systems, we care more about preventing failure from occurring. With service-oriented architectures, controlling failure becomes less manageable, so instead we learn to anticipate it. With highly distributed microservice architectures where failure is all but guaranteed, we embrace it.

Dissecting Message Queues

Mon, 07 Jul 2014 00:33:53 -0500

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.