A complete system design for a YouTube-style platform. Upload, transcoding to HLS/DASH variants, storage at petabyte scale, CDN delivery, adaptive bitrate, recommendations, and the operational realities.
Durable execution explained. Why Temporal became standard infrastructure in 2026, when to reach for it, and concrete patterns for AI agents, payment workflows, sagas, and any long-running process that must survive crashes.
End-to-end system design for a WhatsApp / Telegram-style chat. WebSocket fleet, message storage, delivery receipts, online presence, group chat fanout, end-to-end encryption, and the operational realities of running it at scale.
End-to-end system design for a Twitter / News Feed service. Functional + non-functional requirements, capacity, schema, fanout-on-write vs fanout-on-read, hybrid for celebrities, ranking, caching, and what interviewers love.
Production patterns for idempotency keys, retry strategies, the outbox pattern, and the truth about exactly-once delivery. The patterns every backend engineer needs to handle network failure correctly.
How to design a distributed rate limiter end-to-end. The four classic algorithms, when to pick each, a Redis Lua implementation, distributed coordination, and the gotchas (clock skew, hot keys, fairness).
End-to-end system design for a URL shortener — requirements, capacity estimation, ID generation (counter, hash, base62), schema, caching, analytics, abuse prevention, and the parts most write-ups skip.
A backend engineer’s working mental model for distributed systems. Failure modes, CAP, replication, partitioning, consistency, time and ordering, idempotency — explained without the textbook.
A practical comparison of rate limiting algorithms (fixed window, sliding window, leaky bucket, token bucket), production-ready Redis implementations, and the headers your API should return.
How to use Redis as a cache properly — patterns (cache-aside, read-through, write-behind), key design, TTLs, invalidation, and the production gotchas to avoid.