Start by clarifying functional and non-functional requirements, then design a scalable architecture that handles crawling, price comparison, and notifications. Focus on trade-offs between polling vs. event-driven updates, storage choices, and notification delivery guarantees.
Pro tip: Proactively discuss how to handle anti-scraping measures and rate limits from e-commerce sites, and propose a hybrid approach using APIs where available and respectful crawling otherwise. This shows awareness of real-world constraints and legal/ethical considerations.
Ask about scale (number of users, products, sites), update frequency, notification channels, and consistency needs. Define core features: user registration, product tracking, price threshold setting, and notifications.
Sketch main components: API gateway, user service, product catalog, price fetcher, price storage, notification service, and scheduler. Explain data flow from user adding a product to receiving a notification.
Detail the price fetching mechanism (crawlers vs. APIs, scheduling, deduplication), storage schema (time-series DB for prices, relational for users/thresholds), and notification system (push, email, SMS with retries and idempotency).
Discuss partitioning, caching, rate limiting, fault tolerance, and monitoring. Explain how to handle failures in fetching or notifications, and ensure eventual consistency.
Compare polling vs. event-driven updates, SQL vs. NoSQL, push vs. pull notifications, and cost implications. Justify choices based on requirements and constraints.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Time-series storage was the obvious answer and I said it, but I fumbled explaining why a general-purpose relational DB would struggle here at scale.
Start by clarifying requirements: scale (millions of products, update frequency), query patterns (threshold evaluation, historical analysis), and consistency needs. Then propose a hybrid storage model: a time-series database or wide-column store for raw price history, and a derived, indexed structure (e.g., inverted index or materialized view) for efficient threshold queries. Discuss trade-offs between write throughput, read latency, and storage cost.
Pro tip: Emphasize that threshold evaluation often requires pre-computation or indexing (e.g., bucketing prices or using a search index) to avoid full scans; mention that you'd validate the design with back-of-the-envelope calculations for storage and QPS.
Ask about data volume (millions of products, how many price points per product?), update frequency (real-time vs batch), query patterns (threshold evaluation: how many thresholds, latency requirements?), and retention policy.
Select a scalable time-series or wide-column store (e.g., Cassandra, Bigtable, or specialized TSDB) that handles high write throughput and efficient range scans by product and time.
Model the data with a primary key like (product_id, timestamp) for raw history, and consider column families or partitions to optimize for time-range queries and compression.
Create a derived index or materialized view that maps price ranges or thresholds to products, enabling fast lookups without scanning all history. For example, use an inverted index or a bucketed approach.
Discuss consistency (eventual vs strong), cost of maintaining derived views, and how to handle updates (e.g., stream processing to update indexes). Mention partitioning and sharding strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements: scale (millions of users), latency (near real-time), and delivery guarantees (at-least-once vs exactly-once). Then propose a scalable, decoupled architecture using a message queue and fan-out service, and discuss trade-offs like push vs pull, batching, and prioritization.
Pro tip: Emphasize idempotency and deduplication to handle retries and avoid spamming users, and mention how you'd monitor and throttle the system to prevent cascading failures.
Ask about scale (number of users, events per second), latency expectations, delivery guarantees, and whether notifications are personalized. This shows you avoid assumptions and design for the right problem.
Propose a decoupled pipeline: event triggers -> message queue (e.g., Kafka) -> fan-out service -> delivery channels (push, SMS, email). Explain how this scales horizontally and handles spikes.
Detail how to fan out: use a partitioned queue, worker pools, and batch processing. Discuss push vs pull models, and how to handle different channels (e.g., APNs, FCM) with retries and backoff.
Explain partitioning, sharding, and rate limiting to avoid overwhelming downstream services. Mention idempotency, deduplication, and dead-letter queues for failures.
Discuss monitoring (latency, success rate), alerting, and trade-offs like cost vs latency, push vs pull, and exactly-once vs at-least-once delivery.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.