Start by clarifying functional and non-functional requirements, then design a scalable search architecture that separates query understanding, retrieval, and ranking. Focus on how to handle Uber Eats' unique aspects like geo-spatial constraints, real-time availability, and personalization while ensuring low latency and high availability.
Pro tip: Emphasize the trade-offs between freshness (e.g., real-time menu updates) and search relevance, and propose a hybrid approach using both inverted indexes and vector search for semantic understanding. Also, discuss how to handle the 'cold start' problem for new restaurants and items.
Ask about scale (e.g., number of restaurants, items, queries per second), latency requirements, consistency needs, and key features like filters, sorting, and personalization. Define what 'search' includes: restaurant discovery, item search, or both.
Outline the main components: query parser, search index, ranking service, and data pipelines. Discuss using a distributed search engine like Elasticsearch for text search and a geo-spatial index for location-based filtering.
Explain how to model restaurants and items, including attributes like cuisine, price, ratings, and availability. Describe the indexing strategy: inverted index for text, geohash for location, and possibly embeddings for semantic search.
Detail the ranking pipeline: candidate generation, scoring (e.g., relevance, distance, popularity, personalization), and re-ranking. Mention machine learning models for personalization and how to incorporate real-time signals like current order volume.
Discuss scaling the index (sharding, replication), caching strategies, and handling failures. Address trade-offs between consistency and availability, and between freshness and cost. Mention monitoring and A/B testing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the goals of the monitoring system: ensure search reliability, performance, and relevance. Then propose a layered metrics framework covering system health, search quality, and business impact, and explain how you would collect, visualize, and alert on these metrics.
Pro tip: Tie every metric to a user-facing outcome or business KPI, and mention how you'd avoid alert fatigue by setting dynamic thresholds and using anomaly detection.
Clarify what the monitoring system should achieve: detect outages, performance regressions, relevance drops, and business impact. Align with SLOs and SLIs.
List metrics across layers: infrastructure (CPU, memory), service (latency, error rate, QPS), search quality (CTR, zero-result rate, ranking metrics), and business (orders, revenue).
Choose tools for metrics collection (e.g., Prometheus, StatsD), logging (e.g., ELK), and tracing (e.g., Jaeger). Ensure scalability and low overhead.
Create dashboards for real-time monitoring and define alert rules with appropriate thresholds and escalation policies. Use anomaly detection to reduce noise.
Regularly review metrics and alerts, conduct post-mortems, and refine the system based on incidents and changing requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.