The most expensive lie a system can tell is that it accepted work it cannot process in time. It usually
Continue readingBackpressure: What Your System Should Do When Consumers Fall Behind
Every language-level sync.Mutex rests on a guarantee the operating system makes for you: if a thread dies holding the lock,
Rate limiting looks like a solved problem until the day your API runs on more than one machine. The in-memory
Continue readingDistributed Rate Limiting in Go: Token Buckets, Redis, and the Fail-Open Decision
Here is one of the oldest bugs in distributed systems, still shipping to production every week: a service commits a
Replica lag is one of those distributed-systems facts everyone accepts in the abstract and nobody plans for in the code.
Every production rate limiter has two layers. The first layer is the algorithm: token bucket, leaky bucket, fixed window, sliding
# POST 1: Transactional Outbox (Slot 10 backend) You update a row in your database, then publish an event to
Continue readingThe Transactional Outbox Pattern in Go: Reliable Events Without Dual Writes
Here is a failure mode that shows up in every system with more than two backends: round robin distributes requests
Continue readingLoad Balancing Beyond Round Robin: Least Outstanding Requests and Consistent Hashing
Solve the dual-write problem with one extra table and SKIP LOCKED: a working Go implementation of the transactional outbox, plus the delivery-semantics gotchas that bite in production. … Continue readingThe Transactional Outbox Pattern: Reliable Events Without Distributed Transactions
When a request crosses five services and the latency budget blows up, a single log line rarely tells you where
Continue readingDistributed Tracing in Go with OpenTelemetry