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0+ curated questions with the same answer shape every time: quick recap, deep dive, pitfalls, and follow-ups. Coverage includes .
System design fundamentals questions are anchored to Go and cloud-native patterns. This module covers capacity estimation with Go service assumptions (a well-tuned Go HTTP server handles 10K–100K req/s on a single core; goroutine overhead at 2KB vs Java thread at 1MB), caching strategies anchored to Go services (go-redis cache-aside, in-memory LRU with golang-lru, the thundering herd problem and singleflight as the Go-idiomatic fix), horizontal scaling patterns for Go (stateless handlers + external state in Redis/Postgres, graceful shutdown with context propagation), the CAP theorem mapped to databases Go services commonly use (PostgreSQL = CP, Redis Cluster = AP-leaning, DynamoDB = tunable), consistent hashing for sharding (the jump hash algorithm used in Go distributed systems), and rate limiting design (token bucket in Go with Redis, the leaky bucket alternative). Examples throughout use Go-specific numbers and libraries.
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