- Conclusions And QA
Conclusions, best-practice summary, implementation checklist, and community Q&A on a distributed in-memory cache for heavy-read APIs, covering Redis Pub/Sub sync, race conditions, warm-up, and Redis versus Memcached at scale.
- Designing For Distributed Failure
How to design a multi-service system around failure: timeouts that mean unknown, partial failure, clock skew, replication lag, sagas and compensation, duplicate or out-of-order messages, retry budgets, tracing, and the trade-offs behind each choice.
- Distributed Systems Problems
Explains the core problems of distributed systems: partial failures, unreliable networks and clocks, synchronous versus asynchronous networks, and long-tail latency.
- Leader Election Pattern
Explains coordinating distributed instances by electing one leader via a distributed mutex or consensus algorithm, including heartbeat, lease, and failure considerations.
- Hedged Request
Explains how hedged requests reduce long-tail latency in distributed systems by sending a duplicate request to another replica after a p95 delay, with the trade-offs around idempotency and choosing the delay.