I started with the ingestion layer and spent probably too long on it.
Start by clarifying requirements and scale, then design a high-level architecture that separates flight data ingestion, search, and aggregation. Focus on how to efficiently query multiple providers, normalize results, and handle flexible date ranges and filters while ensuring low latency and cost efficiency.
Pro tip: Emphasize the trade-offs between caching strategies (e.g., TTL vs. event-driven invalidation) and the importance of idempotent, resilient API integrations with providers, as these are common failure points in real-world systems.
Ask about expected scale (QPS, number of providers), latency SLAs, data freshness requirements, and budget constraints. Clarify flexible date ranges (e.g., ±3 days) and how stops are defined (e.g., max stops).
Propose a layered architecture: ingestion layer to fetch data from providers (via APIs or feeds), storage layer (e.g., search index, cache), and query layer that aggregates and ranks results. Consider using a search engine like Elasticsearch for flexible queries.
Design a pipeline to periodically pull or receive flight data from multiple providers, normalize it into a common schema (origin, destination, dates, cabin, stops, price), and index it for fast retrieval. Handle provider-specific quirks and rate limits.
Explain how to process a search request: parse flexible date ranges, query the index for matching flights, aggregate results across providers, deduplicate, and sort by price. Use caching for popular routes and consider pre-computation for common searches.
Discuss scaling the ingestion and query layers (e.g., sharding, replication), handling provider failures with retries and fallbacks, and trade-offs between freshness and cost (e.g., caching TTL vs. real-time queries).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This came as a follow-up and I was not fully prepared for the specificity.
Start by clarifying the scale and query patterns (read/write ratio, latency SLAs, price update frequency). Then propose a schema that separates current prices from historical data, and an indexing strategy that balances fast reads with efficient writes, such as covering indexes for reads and batch updates for writes.
Pro tip: Mention that you would use a write-optimized store (e.g., LSM-tree based) for updates and a read-optimized cache or materialized view for queries, and discuss how you'd handle consistency between them.
Ask about query patterns (e.g., by product ID, category, time range), read/write ratio, latency SLAs, and data volume. This determines the schema and indexing choices.
Propose a normalized schema for products and prices, with a separate table for current prices and another for historical prices. Consider partitioning by time or product ID for scalability.
For fast reads, create covering indexes on frequently queried columns (e.g., product_id, timestamp) and consider composite indexes for multi-column filters. For efficient updates, minimize the number of indexes on the write-heavy table.
Use batch updates, avoid hot spots by partitioning, and consider append-only writes for historical data. For current prices, use an upsert pattern with a primary key on product_id.
Discuss sharding, replication, and caching (e.g., Redis) for read scalability. Explain how to maintain consistency between cache and database, and how to handle eventual consistency for historical data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.