Start by clarifying requirements and defining durability guarantees, then design a write-ahead logging (WAL) or journaling mechanism to ensure crash consistency. Walk through each file operation, explaining how the log and metadata updates are ordered and made durable, and discuss trade-offs between performance and durability.
Pro tip: Emphasize the importance of ordering writes and using barriers/flushes to enforce durability, and mention how Netflix's scale might influence design choices like batching or async replication.
Ask about expected durability (e.g., after crash, after power loss), performance needs, and scale. Define what 'correct recovery' means for metadata and data.
Propose a write-ahead log (WAL) or journal for metadata and data, plus a copy-on-write or log-structured approach. Explain how inodes, data blocks, and the log interact.
For create, read, write, delete, rename: describe the sequence of log writes, metadata updates, and data writes, ensuring atomicity and durability via fsync/barriers.
Explain how on mount after crash, the system replays the log to a consistent state, handling partial writes and ensuring idempotency.
Compare journaling vs. log-structured vs. copy-on-write; discuss performance impact of fsync, batching, and how Netflix's workload might influence choices.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Came at the end and I was already a bit fried.
Acknowledge the bottlenecks in your design by systematically analyzing each component, then propose concrete changes to address them for production scale. Focus on trade-offs and how you would validate improvements, showing a balance between theoretical knowledge and practical engineering.
Pro tip: Frame bottlenecks as opportunities for optimization and tie your solutions to Netflix's scale and reliability requirements, such as handling millions of concurrent streams and ensuring low latency globally.
Walk through the system components (e.g., database, cache, network, compute) and pinpoint where performance degrades under load. Use metrics like latency, throughput, and error rates to justify.
Rank bottlenecks by their potential impact on user experience and system reliability. Consider frequency, severity, and ease of mitigation.
For each bottleneck, suggest specific changes (e.g., sharding, caching, async processing, autoscaling) and explain how they address the issue. Mention trade-offs.
Describe how you would test the changes (e.g., load testing, canary deployments) and monitor improvements. Emphasize iterative refinement based on data.
Tie solutions to Netflix's global, high-availability requirements, such as multi-region deployment, chaos engineering, and cost efficiency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.