The fintech startup had three different time-series storage problems at the same time. After evaluating the options available in 2020, we ended up running two different systems. Here’s the decision framework and why the landscape fragmented the way it did. ...
Go had resisted generics for years. The arguments against were practical: generics complicate the language, they interact badly with Go’s interface system, and most cases where you want generics can be handled with interface composition or code generation. The arguments weren’t wrong. But the proposal that eventually shipped in Go 1.18 (2022) addressed a real gap — a gap that was producing either duplicated code or interface{} with runtime type assertions everywhere. Here’s what the proposal was solving. ...
The fintech startup hit the platform question about eighteen months in. We had product-market fit, we were growing, and the engineering team was doubling every six months. The systems that had worked at ten engineers were showing strain at twenty-five. The question became: dedicate engineering time to platform work, or keep all capacity on product features? This is a hard question. The people who get it right aren’t smarter — they’re clearer about what they’re actually trading off. ...
Go’s goroutine scheduler sits between your code and the OS. Understanding it is useful not because you’ll tune it (you rarely should) but because its behaviour explains a class of performance surprises and concurrency patterns that look odd until you see why they exist. ...
The fintech startup processed market events at sustained rates of 50,000–200,000 per second through a normalisation and enrichment pipeline. Go channels and goroutines are the natural tool. The naive implementation falls apart at scale. Here’s what works. ...
I was promoted to tech lead at the startup in early 2020. The team was five engineers, including me. My previous experience managing people: none, unless you count mentoring a couple of interns. The first month was humbling. The second month was better. By the third month I had a clearer mental model of what the role actually required and how it differed from what I’d been doing before. ...
Every Go service should have profiling endpoints enabled by default. The overhead of having net/http/pprof imported and listening is negligible — a few goroutines, no continuous sampling. The payoff when you need it is enormous. This post is about the workflow I use for diagnosing real performance problems in production Go services. ...
Go has first-class benchmarking built into the testing package. go test -bench=. is enough to get started. The hard part isn’t running benchmarks — it’s writing ones that measure what you intend to measure. These are the patterns I’ve found essential and the mistakes I’ve made repeatedly enough to write down. ...
context.Context appears in the signature of almost every non-trivial Go function. It’s the idiomatic way to carry deadlines, cancellation signals, and request-scoped values across API boundaries and goroutines. After years of reading Go codebases, the misuse patterns are as common as the correct uses. ...
Go modules are one of the more carefully designed dependency management systems I’ve worked with. The core ideas — versioned module paths, minimum version selection, reproducible builds via go.sum — are sound. After running services on modules since 1.11, the rough edges are predictable enough to document. ...