Start by clarifying requirements (scale, latency, freshness, query patterns) and then walk through the end-to-end architecture: data ingestion, batch processing to compute popular items, storage of results, and API serving. Emphasize trade-offs in data modeling, batch vs. streaming, and how to keep the API performant and scalable.
Pro tip: Mention that you would precompute and store the top N popular items per restaurant (e.g., in a key-value store) to ensure low-latency API reads, and discuss how to handle updates incrementally to avoid full recomputation.
Ask about data volume, update frequency, latency requirements, and query patterns (e.g., top K items per restaurant, time windows). This shapes the design.
Outline how order history is collected (e.g., Kafka, S3) and stored in a data lake or warehouse (e.g., HDFS, BigQuery) for batch processing.
Describe the batch job (e.g., Spark, MapReduce) that aggregates orders to compute popular items per restaurant, handling deduplication, time windows, and ranking.
Explain how results are stored (e.g., Redis, Cassandra) and exposed via a RESTful API, including caching, pagination, and read scalability.
Discuss trade-offs (batch vs. streaming, cost vs. freshness) and potential improvements like incremental updates or real-time processing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and scale, then propose a RESTful API design with cursor-based pagination, flexible filtering, and tenant isolation enforced at the data access layer. Walk through the key components: endpoint definition, query parameters, authentication/authorization, and performance considerations.
Pro tip: Emphasize cursor-based pagination over offset-based for large datasets to avoid performance degradation and inconsistent results, and mention the importance of indexing on tenant_id and filter columns.
Ask about expected traffic, data volume, latency requirements, and whether multi-tenancy is logical or physical. This shows you think about non-functional requirements before jumping to solutions.
Specify the endpoint (e.g., GET /items/top), query parameters for pagination (cursor, limit), filtering (category, min_price, max_price), and sorting. Include response format with metadata like next_cursor.
Choose cursor-based pagination using a stable sort key (e.g., score or created_at). Explain how filters are applied efficiently, possibly using a search index like Elasticsearch for complex queries.
Enforce tenant isolation by extracting tenant ID from the auth token (e.g., JWT) and including it in every database query. Discuss row-level security or separate schemas/databases based on isolation needs.
Mention indexing strategies (composite indexes on tenant_id and filter columns), caching, and read replicas. Discuss how to handle large result sets and potential rate limiting per tenant.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the specific use case and requirements (e.g., data volume, latency tolerance, cost constraints) to ground your answer. Then compare offline batch and real-time streaming across key dimensions like latency, throughput, cost, complexity, and fault tolerance. Conclude by explaining why batch is the right choice for this scenario and acknowledge when streaming would be preferred.
Pro tip: Quantify the trade-offs with concrete numbers (e.g., 'batch can process 10x the volume at 1/5 the cost') and mention that many systems use a hybrid approach (Lambda architecture) to get the best of both worlds.
Ask about the data volume, velocity, latency requirements, and business needs to ensure you understand the context before diving into trade-offs.
Briefly define offline batch processing (high throughput, scheduled, high latency) and real-time streaming (low latency, continuous, complex) to set the stage.
Discuss trade-offs across dimensions: latency, throughput, cost, complexity, fault tolerance, and data completeness. Highlight that batch excels in throughput and cost, while streaming excels in latency.
Explain why batch is suitable here: e.g., non-time-critical analytics, large historical data processing, cost efficiency, and simpler operations.
Mention scenarios where streaming would be better (e.g., real-time alerts, fraud detection) and note that hybrid architectures (Lambda/Kappa) can combine both.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.