Finished this one? 0 / 208 Handbooks done
Explore the topic
See this alongside everything else on the same subject — handbooks, system designs, challenges and tools, in one place.
More Handbooks
- The Kafka HandbookApache Kafka as a distributed append-only log, not a queue — topics, partitions and offsets, producers, consumers and consumer groups, per-key ordering, replication and ISR, delivery semantics (at-least-once and exactly-once), retention vs log compaction, and when Kafka beats a message queue.Read →
- Kafka vs RabbitMQThey both "move messages", which is exactly why teams pick the wrong one. Kafka is a durable, replayable log where consumers track their own offset; RabbitMQ is a smart broker that routes each message and deletes it on ack. The remember-vs-forget core difference, throughput and ordering trade-offs, and how to choose.Read →
- The WebSockets & Real-Time HandbookWhy "live" is hard on a protocol that can't push. Polling makes you wait on average half the interval to learn of an event (a 10s poll = ~5s staleness) and wastes a request every time nothing changed — and shrinking the interval only multiplies the waste. A WebSocket keeps one persistent full-duplex line open so the server pushes the instant something happens: latency ≈ one network hop, zero empty requests. The poll-vs-push latency math, when to use SSE instead, and the stateful-scaling pitfalls. With worked math and runnable code.Read →
- Kafka vs KinesisSame partitioned-log model underneath, different operational surface: Kafka is self-run and portable with a huge ecosystem; Kinesis is fully AWS-managed with a hard per-shard throughput ceiling. Retention, cost model, and the lock-in trade-off.Read →
- Streaming vs BatchBatch processes on a schedule; streaming reacts per-event. The real engineering cost of streaming isn’t speed, it’s correctness under disorder — event time vs processing time, watermarks, exactly-once semantics. The Lambda and Kappa architectures that combine both.Read →
- The Caching Patterns HandbookWhy the fastest query is the one you never run. A cache serves requests from a fast store and falls back to a slow source on a miss, so average latency is a hit-rate-weighted blend of the two paths — h·t_cache + (1−h)·t_source, dominated by the miss term. That makes hit rate the one number that decides a cache's value. The patterns (cache-aside, write-through, write-back), the eviction policy, and the two famously hard problems — invalidation/staleness and the cache stampede — that turn a speedup into an outage if you get them wrong. With worked math and runnable code.Read →