I started with the obvious stuff: API layer, a product metadata store, a scheduler that kicks off fetch workers, and a notification service.
Start by clarifying requirements and scale, then design a high-level architecture that separates ingestion, storage, and alerting. Focus on data modeling for time-series price data, scalability for millions of users, and trade-offs between consistency and latency in alert delivery.
Pro tip: Emphasize the importance of idempotent and exactly-once processing in the alert pipeline to avoid duplicate notifications, and discuss how to handle late-arriving price updates gracefully.
Ask questions to understand the number of products, update frequency, user base, and alert latency expectations. Define functional and non-functional requirements.
Outline components: data ingestion (crawlers/APIs), message queue, storage (time-series DB), alert service, and notification system. Sketch data flow.
Design schema for products, price history, user watchlists, and alerts. Choose appropriate databases (e.g., Cassandra for time-series, Redis for caching).
Discuss partitioning, sharding, replication, and caching strategies to handle millions of users and high write throughput.
Design alert evaluation logic (thresholds, percentage drops), ensure scalability and fault tolerance. Discuss trade-offs like push vs pull, consistency vs latency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and scale, then propose a tiered scheduling system where crawl frequency is dynamically adjusted based on product popularity signals. Discuss how to compute popularity, assign tiers, and handle trade-offs like freshness vs. resource usage, and finally outline the architecture and data flow.
Pro tip: Emphasize the importance of a feedback loop: use crawl results to update popularity metrics, and consider implementing adaptive rate limiting to avoid overloading target sites. Also, mention that you would start with a simple heuristic and iterate based on metrics.
Ask questions to understand the scale (number of products, crawl rate), freshness requirements, and constraints (e.g., politeness, cost). This shows you think before designing.
Identify signals for popularity such as page views, sales rank, search frequency, or historical price change frequency. Discuss how to compute and update these metrics in real-time or batch.
Propose a multi-tier system (e.g., high, medium, low) with different crawl intervals. Explain how products move between tiers based on popularity changes.
Outline components: a scheduler service, a priority queue, a worker pool, and a datastore for popularity metrics. Describe how the scheduler assigns tasks and how workers fetch and update data.
Discuss trade-offs: resource allocation, freshness vs. cost, and potential starvation of long-tail products. Mention strategies like exponential backoff, jitter, and dynamic adjustment based on load.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the notification system's architecture and failure modes, then propose idempotency as the core principle. Describe concrete mechanisms like idempotency keys, deduplication stores, and transactional outbox patterns, and explain how they handle retries and partial failures.
Pro tip: Emphasize that idempotency must be enforced at the consumer side, not just the producer, and discuss how to handle the 'at-least-once' delivery guarantee by making operations idempotent. Also, mention the trade-offs between different deduplication strategies (e.g., time-based vs. permanent) and how to monitor for duplicates.
Ask about the notification system's scale, delivery guarantees (at-least-once, at-most-once), and common failure points (e.g., network timeouts, partial writes). This shows you understand the problem context before jumping to solutions.
Explain that to prevent duplicates, each notification request must be idempotent, meaning repeated attempts have the same effect as a single attempt. This is achieved by assigning a unique idempotency key to each logical notification.
Describe how to use a deduplication store (e.g., Redis, database) to track processed idempotency keys. On retry, the system checks if the key exists; if so, it skips sending. Discuss TTL and storage considerations.
Propose using a transactional outbox or two-phase commit to ensure that notification sending and state updates are atomic. This prevents scenarios where a notification is sent but the system crashes before recording it.
Discuss how to handle duplicate keys from different sources, clock skew, and cleanup of old keys. Also, mention monitoring and alerting for duplicate detection to catch issues in production.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about tiered storage: keep recent high-resolution data in a time-series DB, then downsample older data into hourly or daily aggregates and ship to cold object storage.
Start by clarifying requirements: data volume, query patterns, retention, and cost constraints. Then propose a tiered storage strategy using columnar formats and compression, with hot/warm/cold tiers and appropriate indexing. Discuss trade-offs between storage cost, query performance, and complexity.
Pro tip: Emphasize that price history is append-only and time-series, so you can leverage time-based partitioning and downsampling to reduce cost. Also mention that you would evaluate build vs. buy (e.g., using a managed time-series database) based on team expertise and scale.
Ask about data volume (e.g., number of instruments, years of history, granularity), query patterns (e.g., point lookups, range scans, aggregations), and cost constraints. Understand SLAs for latency and durability.
Recommend a columnar format like Parquet or ORC with compression (e.g., Snappy, Zstd) to reduce storage footprint and improve query performance. For time-series, consider specialized formats like Gorilla or Delta-of-Delta encoding.
Propose a hot/warm/cold tiering strategy: recent data in fast storage (e.g., SSD, in-memory), older data in cheaper object storage (e.g., S3, GCS) with lifecycle policies. Use partitioning by time (e.g., daily/monthly) to enable efficient pruning.
Add appropriate indexes (e.g., time-series indexes, bloom filters) and consider pre-aggregation or materialized views for common queries. For cold data, use serverless query engines (e.g., Athena, BigQuery) to avoid always-on costs.
Compare options like using a managed time-series database (e.g., TimescaleDB, InfluxDB) vs. building on object storage. Highlight trade-offs between cost, performance, operational complexity, and scalability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the question I was least prepared for.
Start by clarifying the requirements: which regions, what data isolation and compliance needs, and expected scale. Then propose a multi-tenant architecture with regional deployments, data partitioning, and configurable retailer-specific logic, while discussing trade-offs between consistency, latency, and cost.
Pro tip: Emphasize that you would design for regional data residency and compliance from the start, as this is a common pitfall in global systems. Also, mention that you would use feature flags and configuration over code to handle retailer-specific variations, enabling rapid adaptation without redeployments.
Ask about the number of retailers, regions, data residency laws, expected traffic patterns, and any retailer-specific customizations. This ensures the design addresses real constraints.
Decide on tenant isolation level (shared DB with tenant ID, separate schemas, or separate databases) based on compliance and scale. Consider a hybrid approach for cost efficiency.
Propose deploying the service in multiple regions with data replication and routing based on user location. Discuss active-active vs. active-passive setups and latency implications.
Use configuration-driven approaches (e.g., feature flags, rules engines) to manage variations in business rules, pricing, and workflows without code changes.
Cover monitoring, security, data consistency, and failure recovery across regions. Discuss trade-offs between consistency, availability, and partition tolerance (CAP theorem).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I said I'd assume a mix: official API where available, scraping as a fallback.
Clarify that the choice depends on the specific requirements, constraints, and scale of the project, and that a hybrid approach is often optimal. Then systematically compare the trade-offs of each method across dimensions like reliability, cost, legal risk, and maintenance, and conclude with a recommendation based on the context.
Pro tip: Emphasize that APIs are generally preferred for their reliability and structured data, but web scraping can be a fallback for retailers without APIs; however, always consider the legal and ethical implications and the potential for breaking changes.
Ask clarifying questions about the project's goals, scale, data needs, and constraints (e.g., budget, timeline, legal). This shows you don't jump to solutions without understanding the problem.
Discuss key dimensions: reliability (API is stable, scraping breaks with UI changes), data quality (API provides structured data, scraping requires parsing), cost (API may have fees, scraping requires infrastructure), legal/ethical (API is sanctioned, scraping may violate ToS), and maintenance (API is easier, scraping needs constant updates).
Explain that using APIs where available and scraping as a fallback can balance coverage and reliability, but adds complexity in maintaining two systems.
Tie back to the original requirements: for a large-scale, long-term project, prefer APIs; for a quick prototype or when APIs are unavailable, scraping might be acceptable with caution.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.