tag
Microservices
- #101 7 min
SRE Doesn’t Scale
We encounter a lot of organizations talking about or attempting to implement SRE as part of our consulting at Real Kinetic. We’ve even discussed and debated ourselves, ad nauseam, how we can apply it at our own product company, Witful. There’s a brief, unassuming section in the SRE book tucked away towards the tail end of chapter 32, “The Evolving SRE Engagement Model.” Between the SLIs and SLOs, the error budgets, alerting, and strategies for handling change management, it’s probably one of the most overlooked parts of the book. It’s also, in my opinion, one of the most important.
- #91 17 min
Microservice Observability, Part 2: Evolutionary Patterns for Solving Observability Problems
In part one of this series, I described the difference between monitoring and observability and why the latter starts to become more important when dealing with microservices. Next, we’ll discuss some strategies and patterns for implementing better observability. Specifically, we’ll look at the idea of an observability pipeline and how we can start to iteratively improve observability in our systems. To recap, observability can be described simply as the ability to ask questions of your systems without knowing those questions in advance. This requires capturing a variety of signals such as logs, metrics, and traces as well as tools for interpreting those signals like log analysis, SIEM, data warehouses, and time-series databases. A number of challenges surface as a result of this. Clint Sharp does a great job discussing the key problems, which I’ll summarize below along with some of my own observations.
- #90 9 min
Microservice Observability, Part 1: Disambiguating Observability and Monitoring
“Pets versus cattle” has become something of a standard vernacular for describing the shift in how we build systems. It alludes to the elastic and dynamic nature of these (typically, but not necessarily) container-based systems with on-demand scaling and more transparent fault-tolerance. I’ve talked before about this transition before and specifically how it relates to monitoring. In particular, with these more dynamic, microservice-based systems, the conversation starts to shift away from traditional monitoring toward observability. In this series, I’ll describe that distinction, explain why it matters, and share some concrete tactical items for implementing observability in a microservice environment.
- #80 12 min
The Observability Pipeline
The rise of cloud and containers has led to systems that are much more distributed and dynamic in nature. Highly elastic microservice and serverless architectures mean containers spin up on demand and scale to zero when that demand goes away. In this world, servers are very much cattle, not pets. This shift has exposed deficiencies in some of the tools and practices we used in the world of servers-as-pets. It has also led to new tools and services created to help us support our systems.
- #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.