Two interviewers, no prompt beyond the general domain.
Start by clarifying the scope and requirements through targeted questions, then propose a high-level design that addresses the core challenges of the chosen Datadog domain. Focus on frontend concerns such as data visualization, real-time updates, and performance, while showing awareness of backend constraints. Drive the session by prioritizing requirements and iterating on the design based on feedback.
Pro tip: Demonstrate user-centric thinking by tying technical decisions to the needs of Datadog's users (e.g., engineers and SREs), and proactively discuss trade-offs between performance, scalability, and development complexity.
Ask questions to understand the specific domain, scale, user personas, and key features. For example, for a dashboarding system, clarify the number of metrics, update frequency, and customization needs.
Sketch the main components (e.g., data ingestion, storage, API, frontend) and how they interact. Emphasize the frontend's role in fetching and rendering data efficiently.
Discuss specific frontend concerns such as real-time data streaming (WebSockets, SSE), efficient rendering of large datasets (virtualization, canvas), and state management.
Analyze trade-offs between different approaches (e.g., polling vs. streaming, client-side vs. server-side rendering) and how the design scales with increasing data volume and users.
Recap the design, highlight how it meets the requirements, and suggest potential improvements or areas for further discussion based on feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scale and access patterns (e.g., query frequency, retention, cardinality), then propose a tiered storage strategy with time-based rollups and compression algorithms. Emphasize trade-offs between precision, storage cost, and query latency, and how the frontend can leverage pre-aggregated data for fast visualizations.
Pro tip: Mention that downsampling should be query-aware: different resolutions for different time ranges (e.g., 1s for last hour, 1m for last day, 1h for last month) and that the frontend should request the appropriate resolution based on the zoom level to avoid over-fetching.
Ask about data volume, write/read throughput, retention period, query patterns, and acceptable latency. This shows you understand the problem before jumping to solutions.
Propose storing raw data for short-term, then progressively downsampling to coarser resolutions (e.g., 1m, 1h, 1d) for longer retention. Explain how this reduces storage and speeds up queries.
Discuss algorithms like Gorilla (for floating-point time-series), delta-of-delta encoding, and dictionary encoding for labels. Highlight how they exploit temporal locality and reduce size.
Explain how the frontend can request downsampled data based on zoom level and use aggregation functions (avg, max, min) to render charts efficiently. Mention caching and pre-computation.
Discuss trade-offs: loss of precision vs. storage savings, query latency vs. cost, and how to handle high cardinality. Mention horizontal scaling and distributed storage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The optimization discussion is apparently the centerpiece of this round and I got maybe 8 minutes on it.
Start by briefly recapping the system's architecture and its key components, then systematically identify bottlenecks using performance metrics and profiling. Prioritize optimizations based on impact and effort, and explain how you would implement and measure each improvement.
Pro tip: Tie optimizations to Datadog's core value of observability—mention how you'd use real user monitoring (RUM) and synthetic testing to validate improvements. Also, emphasize trade-offs: e.g., caching improves speed but adds complexity, so discuss when it's worth it.
Briefly summarize the system you designed, its purpose, and the key performance goals (e.g., load time, interactivity, scalability). This sets context for optimization.
Describe how you would profile the system using tools like Lighthouse, Chrome DevTools, or Datadog RUM to find bottlenecks in rendering, network, or JavaScript execution.
Rank bottlenecks based on their impact on user experience and the effort required to fix them. Focus on high-impact, low-effort wins first.
For each bottleneck, propose specific solutions: code splitting, lazy loading, caching, image optimization, CDN usage, etc. Explain how each addresses the root cause.
Explain how you would validate improvements using metrics and A/B testing, and how you would monitor for regressions. Emphasize continuous optimization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.