Send a reminder in 24 hours. Retry this job in 5 minutes. Expire this hold at midnight. Delayed execution is everywhere, and Thread.sleep isn’t the answer.

Three nodes, one job. Without leader election, all three run it simultaneously. With leader election, exactly one does the work while the others stand by.

FIFO queues treat every message equally. But urgent config updates shouldn’t wait behind a thousand bulk sync jobs. Priority queues fix this, if you handle starvation.

Counting unique items across billions of events. A HashSet needs gigabytes. HyperLogLog does it in 12KB. The trick is accepting a little error.

Green dot means online. Simple, right? Behind that dot is a distributed system making heartbeat-based guesses about user liveness.

Cache expires. 10,000 requests hit the database simultaneously. Your DB collapses. How request coalescing and probabilistic expiration prevent the stampede.

Why distributed locking is harder than it looks. Naive Redis locks, Redlock, fencing tokens, and when to avoid locks entirely.

Why one cache isn’t enough. How to layer local, distributed, and CDN caches for maximum performance without losing your mind on consistency.

The four fundamental caching patterns every engineer should know. When to use cache-aside vs write-through vs write-behind vs read-through.

In-memory doesn’t always mean fast. How shifting from object-based to vector-based storage (Apache Arrow) delivered a 13x performance boost.