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’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. ...
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 has a famous concurrency proverb: “Do not communicate by sharing memory; instead, share memory by communicating.” This is good advice. It’s also misapplied regularly. Channels are not universally better than mutexes — they’re a different tool for a different set of problems. After years of Go in production, here’s when I reach for each. ...
The first thing every Java developer learns about goroutines is that they’re cheap. Start a million of them, no problem. The mental model that follows from this — “goroutines are threads but lighter” — is close enough to be useful and wrong enough to cause confusion. Here’s the refined model. ...
Three months after deploying Kafka with Avro schemas and the Confluent Schema Registry, we had a production incident where a schema change caused a downstream consumer to silently produce incorrect output — wrong field values, no error thrown, no monitoring alert triggered. That incident rewired how the team thought about schema evolution. The tools don’t protect you from all the failure modes. Understanding the rules and building organisational processes around them is what does. ...
Financial systems are natural candidates for event sourcing. Regulators want to know the state of positions at any point in time. Audit trails are not optional. The need to replay a day’s events to debug a pricing anomaly comes up regularly. These requirements — which other domains treat as optional — map directly onto event sourcing’s core properties. That said, event sourcing in production has costs that the enthusiast literature systematically underplays. Here’s an honest accounting. ...