#distributed systems

Two things decide whether a load balancer helps. The algorithm, where round robin quietly assumes every request costs the same. And the health check, which is either too shallow to notice a broken server or so deep that one shared dependency marks your entire fleet unhealthy at once.

Not hard, not expensive, not a premium tier. Impossible, in the proven sense, and it follows from the Two Generals problem. Every product advertising exactly-once is selling at-least-once with deduplication bolted on, which means the work was always going to land on your consumer.

Logs, metrics and traces is a taxonomy of data formats, not of capability. Having all three does not mean you can answer a question you did not anticipate. What lets you do that is high-cardinality context on every request, which is also precisely what your observability vendor prices on.

The famous framing is wrong twice over. Partition tolerance was never something you choose, and the trade-off only exists while a partition is happening. Which means CAP is silent about the decision you actually make every day, and PACELC is the version that names it.

Almost every explanation frames this as being kind to a struggling downstream service. That is a side effect. Its actual job is stopping your own threads from piling up on timeouts, which is how a dependency you barely use takes down a service that does not need it.

The design detail that surprises people: Stripe caches the result of the first request under a key even when that result was a 500, and replays the failure to every retry. That is correct, and once you see why, the rest of the design follows, including where most homegrown implementations go wrong.

Everyone compares token bucket to sliding window and stops there. The decisions that actually determine whether a rate limiter works are what you key it on, whether your Retry-After header is quietly scheduling a thundering herd, and whether a client that gets a 429 can safely retry at all.

Microservices solve an org-scale problem, not a code problem. Here is how to actually decide between a monolith and microservices, and why most startups should start with the monolith.

A deep technical guide to building low-latency, globally distributed systems on edge runtimes, from CDN functions to stateful Durable Objects.

Most teams pick Postgres by default and that is usually right. Here is how to know when it stops being right, and what the alternatives actually cost you.