Design an async job system and cache layer

Design two systems. You can assume a large-scale production environment; focus on clear APIs, data models, scaling, reliability, and trade-offs. # Part A) Design an asynchronous Job/Task system (service-oriented) Design a service that lets clients submit background jobs and later query results. ## Requirements - Clients can create jobs and poll/subscribe for status. - Jobs move through well-defined states (e.g., pending/running/succeeded/failed/canceled). - Support retries with exponential backoff. - Ensure idempotency (no duplicate execution for the same logical request). - Choose storage (RDBMS vs NoSQL) and justify. ## Discuss explicitly - API design and state transitions - Worker model / scheduling - Retry + backoff, dead-letter handling - Idempotency strategy - Observability (metrics/logging/tracing) --- # Part B) Design a high-throughput cache system (Redis-like / in-memory layer) Design a caching layer in front of a database to reduce latency and increase throughput. ## Requirements - Very high QPS, low latency reads, concurrent reads/writes. - Cache key design, eviction (LRU/LFU/TTL). - Multi-node scaling and sharding. - Cache consistency and invalidation strategy; explain trade-offs vs DB consistency. - Handle hot keys. - High availability and performance under failures. ## Discuss explicitly - Consistency model and write/read paths - Invalidation strategies and failure modes - Sharding/replication and rebalancing - Hot key mitigation and rate limiting - Capacity planning and SLOs