category
Software Engineering
- #77 20 min
Scaling DevOps and the Revival of Operations
Operations is going through a renaissance right now. With the move to cloud, the increasing amount of automation, and the increasing importance of automation, Ops as we know it is reinventing itself out of necessity. Infrastructure is becoming more and more sophisticated—and commoditized—and practices are just now starting to grow up around that. So while some worry about robots taking our jobs, the reality is more about how automation will help augment us to build better software and focus on higher-value things. It’s not so much about the distant future—whatever that may hold—so much as it is about the next five to ten years, what Operations looks like in that timeframe, and why I think it has to retool.
- #76 5 min
More Environments Will Not Make Things Easier
Microservices are hard. They require extreme discipline. They require a lot more upfront thinking. They introduce integration challenges and complexity that you otherwise wouldn’t have with a monolith, but service-oriented design is an important part of scaling organization structure. Hundreds of engineers all working on the same codebase will only lead to angst and the inability to be nimble. This requires a pretty significant change in the way we think about things. We’re creatures of habit, so if we’re not careful, we’ll just keep on applying the same practices we used before we did services. And that will end in frustration.
- #75 5 min
There and Back Again: Why PaaS Is Passé (And Why It’s Not)
In 10 years nobody will be talking about Kubernetes. Not because people stopped using it or because it fell out of favor, but because it became utility. Containers, Kubernetes, service meshes—they’ll all be there, the same way VMs, hypervisors, and switches will be. Compute is a commodity, and I don’t care how my workload runs so long as it meets my business’s SLOs and other requirements. Within AWS alone, there are now innumerable ways to run a compute workload.
- #73 15 min
Building a Distributed Log from Scratch, Part 5: Sketching a New System
In part four of this series we looked at some key trade-offs involved with a distributed log implementation and discussed a few lessons learned while building NATS Streaming. In this fifth and final installment, we’ll conclude by outlining the design for a new log-based system that draws from the previous entries in the series. The Context For context, NATS and NATS Streaming are two different things. NATS Streaming is a log-based streaming system built on top of NATS, and NATS is a lightweight pub/sub messaging system. NATS was originally built (and then open sourced) as the control plane for Cloud Foundry. NATS Streaming was built in response to the community’s ask for higher-level guarantees—durability, at-least-once delivery, and so forth—beyond what NATS provided. It was built as a separate layer on top of NATS. I tend to describe NATS as a dial tone—ubiquitous and always on—perfect for “online” communications. NATS Streaming is the voicemail—leave a message after the beep and someone will get to it later. There are, of course, more nuances than this, but that’s the gist.
- #72 8 min
Building a Distributed Log from Scratch, Part 4: Trade-Offs and Lessons Learned
In part three of this series we talked about scaling message delivery in a distributed log. In part four, we’ll look at some key trade-offs involved with such systems and discuss a few lessons learned while building NATS Streaming. Competing Goals There are a number of competing goals when building a distributed log (these goals also extend to many other types of systems). Recall from part one that our key priorities for this type of system are performance, high availability, and scalability. The preceding parts of this series described at various levels how we can accomplish these three goals, but astute readers likely noticed that some of these things conflict with one another.