Monitoring & Observability — system design interview guide

Monitoring & Observability is a depth lesson in the Reliability & Operations family of the Thita system design course.

Monitoring, observability, deployment strategies, and graceful degradation.

It sits on the Deep Dive path — worth reading once the Interview Soon lessons are solid, or when a role names this area explicitly.

The rest of Reliability & Operations

• Monitoring & Observability — Depth lesson.
• Deployment & Failure Handling — Depth lesson.

Other system design pattern families

• Foundations — The 45-minute interview framework, estimation with modern numbers, and CAP theorem. 6 lessons · about 2 hours · Interview Soon · 1 worked case study.
• Networking & APIs — Protocols (REST, gRPC, WebSocket, SSE), API design, pagination, and idempotency. 5 lessons · about 2 hours · Interview Soon · 1 worked case study.
• Databases — SQL vs NoSQL decision tree, replication, sharding, and indexing strategies. 7 lessons · about 3 hours · Interview Soon · 2 worked case studies.
• Caching — Cache-aside, write-through, failure modes, and when NOT to cache. 5 lessons · about 1.5 hours · Interview Soon · 2 worked case studies.
• Queues & Async — Kafka vs SQS, delivery guarantees, idempotent consumers, and event-driven patterns. 10 lessons · about 1.5 hours · Interview Soon · 5 worked case studies.
• Scalability — Consistent hashing, read vs write scaling, fan-out strategies, and circuit breakers. 7 lessons · about 1.5 hours · Interview Soon · 4 worked case studies.
• Coordination & Transactions — Raft consensus, distributed locking, saga pattern, and conflict resolution. 9 lessons · about 1.5 hours · Interview Soon · 5 worked case studies.
• Search, Geo & Aggregation — Full-text search, geospatial indexing, stream processing, and probabilistic structures. 9 lessons · about 1.5 hours · Interview Soon · 5 worked case studies.
• Security & Auth — OAuth 2.0, JWT vs sessions, API security, and encryption. 2 lessons · about 3 hours · Deep Dive.
• AI Infrastructure — LLM serving, inference batching, RAG pipelines, and vector search at scale. 6 lessons · about 6 hours · Deep Dive · 1 worked case study.
• Real-World Architecture Studies — Deep dives into Discord, Uber, Stripe, Netflix, and Kafka production architectures. 5 lessons · about 4 hours · Deep Dive.

Related interview preparation

• Looking for the practice sheet? — Track your progress against every family as a checklist, with company tags and AI interview practice.
• Low Level Design course — The object-level companion: OOP, SOLID, the design patterns and the machine-coding round.
• DSA patterns course — The coding round: the algorithmic patterns behind the problems asked alongside system design.
• Latency and capacity numbers — The reference table of modern latency, throughput and storage figures used in estimation.
• AI mock interviews — Practise the spoken answer against an interviewer that pushes back on the trade-offs.