Start by clarifying functional and non-functional requirements, such as event volume, latency, and zoom levels. Then propose a high-level architecture that separates ingestion, storage, and serving layers, using pre-aggregation for performance. Finally, dive into data modeling and trade-offs for real-time updates and efficient spatial queries.
Pro tip: Emphasize the use of a multi-resolution grid (e.g., geohash or S2) to pre-aggregate data at different zoom levels, which drastically reduces query latency and storage costs. Also, discuss how to handle late-arriving data and ensure eventual consistency in live updates.
Ask about expected event volume, latency requirements, zoom levels, time-range filters, and live update frequency. This scopes the problem and guides design decisions.
Outline components: ingestion (e.g., Kafka), processing (e.g., Flink/Spark Streaming), storage (e.g., time-series DB, spatial index), and serving (e.g., API with caching). Explain data flow from event to visualization.
Propose a spatial indexing scheme (e.g., geohash, S2, or H3) and pre-aggregate counts per cell at multiple resolutions. Store aggregates in a database optimized for time-range and spatial queries.
Design an API that returns heatmap data for a given viewport, zoom, and time range. For live updates, use WebSockets or SSE to push incremental updates, leveraging the pre-aggregated data.
Discuss trade-offs: pre-aggregation vs. on-the-fly, consistency vs. latency, cost vs. performance. Address scaling ingestion, storage, and serving horizontally.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I actually liked this part of the conversation.
Start by clarifying the system's requirements and the business need for real-time versus historical data, then propose a hybrid architecture that separates the two paths. Discuss trade-offs like latency, accuracy, and cost, and justify where you draw the line based on use cases and SLAs.
Pro tip: Emphasize that the line between streaming and batch is not fixed but driven by business SLAs and data freshness requirements; show you can make pragmatic trade-offs rather than dogmatic choices.
Ask about the specific use cases, expected data volumes, latency requirements, and consistency needs to understand what 'real-time' means for this system.
Propose separate architectures: a streaming path for low-latency updates (e.g., Kafka, Flink) and a batch path for historical analysis and rollups (e.g., Spark, Hive).
Explain criteria for choosing streaming vs. batch, such as data freshness SLAs, cost, complexity, and whether the use case requires immediate action or can tolerate delay.
Discuss how to handle discrepancies between real-time and historical views, including eventual consistency, lambda architecture, or kappa architecture trade-offs.
Mention monitoring, backpressure, and the ability to adjust the boundary as requirements change, showing a focus on long-term maintainability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through regional data ingestion, partitioning by geohash prefix, and CDN caching for tiles.
Start by clarifying the scale and requirements (e.g., event size, latency, consistency needs) and then propose a high-level architecture that partitions the workload across regions, using a combination of ingestion, processing, and storage layers. Emphasize trade-offs between consistency, availability, and latency, and explain how you would handle global distribution through data replication and edge processing.
Pro tip: Demonstrate awareness of Uber's specific scale and existing infrastructure (e.g., Kafka, Flink, Cassandra, or regional data centers) and discuss how you would leverage or extend them rather than proposing a generic solution. Also, mention the importance of monitoring and backpressure to handle spikes gracefully.
Ask questions to understand event size, throughput, latency requirements, consistency needs, and budget. This ensures your design addresses the actual problem.
Propose partitioning the event stream by a key (e.g., user ID, region) to distribute load across multiple nodes. Use a scalable message queue like Kafka and a stream processing framework like Flink.
Deploy the system in multiple regions with data replication and possibly edge processing to reduce latency. Discuss how to route events to the nearest region and synchronize data across regions.
Explain how to handle failures (e.g., replication, checkpointing) and trade-offs between strong and eventual consistency. Mention idempotency and exactly-once processing if needed.
Discuss performance optimizations (e.g., batching, compression) and the need for monitoring, alerting, and auto-scaling to maintain SLAs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.