Reliability

You added a circuit breaker to protect against cascading failures. But it never opens — requests keep failing, the downstream service stays overloaded, and your system doesn't recover. Here's why circuit breakers fail silently and how to configure them correctly.

You scale your app to 3 instances. Your daily billing cron runs on all 3 simultaneously. 3x the emails, 3x the charges, 3x the chaos. Distributed cron requires distributed locking. Here's how to ensure your scheduled jobs run exactly once across any number of instances.

Your DLQ has 2 million messages. They've been there for 3 months. Nobody noticed. Those are failed orders, unpaid invoices, and unprocessed refunds — silently rotting. Here's how to build a DLQ strategy that's actually monitored, alerting, and self-healing.

Your message queue delivers an event twice. Your consumer processes it twice. The order ships twice, the email sends twice, the payment charges twice. At-least-once delivery is a guarantee — not a bug. Here's how to build idempotent consumers that handle duplicate events safely.

Network timeout on a payment request. Client retries. Customer gets charged twice. This is the most expensive bug in fintech — and it's completely preventable with idempotency keys. Here's the complete implementation.

In distributed systems, failure is never all-or-nothing. A service returns a response — but it's corrupt. An API call times out — but the action already executed. A message is delivered — but the reply never arrives. This is partial failure, and it is the hardest problem in distributed systems.

Your service is degraded, returning errors 30% of the time. Smart clients with retry logic start hammering it — 3 retries each means 3x the load on an already failing system. The retry storm amplifies the original failure until full collapse. Here's how to retry safely.

Network partition splits your 3-node cluster into two halves. Both halves think they're the primary. Both accept writes. Network heals. You have two diverged databases with conflicting data. This is split brain — one of the most dangerous failure modes in distributed systems.