← Bytedance Interview Insights
This is basically a full platform design in one question.
Start by clarifying the scope and requirements with the interviewer, then outline a high-level architecture that separates data collection, storage, and querying. Dive into key components like the push vs pull model, time-series database design, and scalability considerations, while discussing trade-offs and potential bottlenecks.
Pro tip: Emphasize the importance of cardinality control and downsampling to handle high-dimensional metrics efficiently, as this is a common pain point in large-scale monitoring systems. Also, relate your design to real-world systems like Prometheus and Datadog to show practical awareness.
Ask questions to understand the scale, data sources, query patterns, and retention policies. Define functional and non-functional requirements clearly.
Sketch the main components: data collection agents, ingestion pipeline, storage layer, query engine, and alerting. Explain data flow from collection to visualization.
Describe the time-series data model, including metric naming, labels, and timestamps. Discuss storage options like TSDB, and techniques for compression and retention.
Address how to scale ingestion and storage horizontally, ensure fault tolerance, and handle high availability. Mention sharding, replication, and partitioning strategies.
Discuss trade-offs between consistency and availability, push vs pull, and query performance. Highlight optimizations like downsampling, caching, and indexing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.