← Databricks Interview Insights
Start by clarifying functional and non-functional requirements, then sketch a high-level architecture with an API gateway, fan-out service, and partner adapters. Dive into the critical path: parallel calls to partner stores with timeouts, result aggregation, and decision logic for auto-purchase vs. price return vs. out-of-stock. Finally, address scalability, fault tolerance, and consistency concerns like idempotency and payment safety.
Pro tip: Emphasize idempotency and exactly-once semantics for order placement, since duplicate orders from retries or timeouts can be costly. Also, discuss how to handle partial failures gracefully—return the best available price even if some partners fail, and clearly communicate any uncertainty to the user.
Ask questions to understand expected scale (QPS, number of partners), latency SLAs, consistency needs, and payment handling. Define what 'out of stock' means across partners and whether the system should retry or fallback.
Outline components: API gateway for request validation, a fan-out service to query partners in parallel, partner-specific adapters, a result aggregator, and a decision engine. Include a database for order state and a notification service.
Explain how to call hundreds of partners concurrently with timeouts and circuit breakers. Aggregate responses to find the lowest price, then apply business rules: if price ≤ max bid, place order; else return price; if no stock, notify. Ensure idempotent order placement.
Discuss horizontal scaling of fan-out workers, caching partner responses, rate limiting, and handling partner failures. Use async processing with queues for order placement and notifications to decouple and improve resilience.
Explain how to avoid double-charging and duplicate orders using idempotency keys, transactional outbox, and two-phase commit or saga patterns. Discuss how to reconcile with partners and handle payment authorization/capture.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: latency budget, consistency needs, and failure tolerance. Then propose a strategy that aggregates partial results with timeouts and fallbacks, and explain when and how to use a circuit breaker to prevent cascading failures. Emphasize trade-offs between completeness and availability.
Pro tip: Mention that circuit breakers should be combined with bulkheads and fallbacks, and that you'd monitor breaker state and adjust thresholds dynamically based on error rates and latency percentiles.
Ask about the expected latency, consistency requirements (e.g., can we return stale or partial data?), and the number of downstream bookstores. This sets the context for trade-offs.
Propose querying all bookstores in parallel with a per-request timeout. Collect successful responses and ignore failures, returning partial results with metadata indicating completeness.
Explain that a circuit breaker per bookstore can prevent repeated timeouts by short-circuiting requests after a failure threshold, allowing the system to degrade gracefully.
Compare circuit breakers with retries, hedging, or bulkheads. Highlight that circuit breakers add complexity but improve resilience; consider if simpler approaches like timeouts suffice.
Conclude with how you'd monitor breaker states, error rates, and latency, and possibly use adaptive thresholds to balance availability and completeness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the system context and requirements, then propose a multi-layer caching strategy (client, CDN, application, database) with appropriate TTLs based on data volatility. Emphasize trade-offs between freshness, consistency, and cost, and explain how you would set TTL for book prices using business rules and adaptive techniques.
Pro tip: Demonstrate awareness of cache invalidation challenges and propose a hybrid approach (e.g., TTL with event-based invalidation) to balance freshness and load reduction. Mention monitoring cache hit rates and adjusting TTLs dynamically based on observed update patterns.
Ask about call volume, latency requirements, data consistency needs, and update frequency of book prices. Understand the downstream bookstore API's rate limits and SLAs.
Propose caching at multiple layers: client-side (browser), CDN for static content, application-level cache (Redis/Memcached) for API responses, and database query cache. Discuss cache key design and eviction policies.
Set TTL based on price volatility: shorter TTL (e.g., 5-15 min) for frequently updated prices, longer TTL (e.g., hours) for stable prices. Consider using a hybrid approach with event-based invalidation for critical updates.
Discuss strategies like write-through, write-behind, or cache-aside. Implement invalidation via pub/sub or webhooks when prices change. Use versioning or timestamps to avoid stale data.
Propose monitoring cache hit/miss ratios, latency, and downstream call volume. Use A/B testing or adaptive TTLs to optimize based on real-world patterns.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scenario: high concurrent requests for the same ISBN cause a cache miss and all requests hit downstream bookstores simultaneously. Then propose a multi-layered solution: request coalescing (single-flight) at the cache layer, distributed locking or lease-based caching, and possibly a message queue to serialize and batch requests. Emphasize trade-offs like added latency, complexity, and consistency.
Pro tip: Mention that the solution should be idempotent and handle failures gracefully—e.g., if the lock holder crashes, others should eventually proceed. Also, consider using a short-lived cache entry with a 'soft' expiration to trigger background refresh while serving stale data, avoiding a hard miss.
Ask about traffic volume, latency SLAs, consistency needs, and whether the downstream bookstores have rate limits. This shows you understand the problem context before jumping to solutions.
Explain that the thundering herd occurs when many concurrent requests miss the cache for the same key and all try to fetch from the source simultaneously, overwhelming downstream services.
Describe using a single-flight mechanism (e.g., Go's singleflight, or a distributed lock) so only one request fetches the data while others wait and share the result. Mention that this can be done in-process or distributed.
Suggest using a lease-based cache (e.g., Redis with SET NX and expiration) to prevent multiple fetches, and serve stale data while refreshing asynchronously to avoid hard misses.
Cover added latency for waiting requests, potential for lock contention, and how to handle failures (e.g., timeouts, lock expiration). Also mention monitoring and fallback strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where the interview got uncomfortable.
Start by clarifying that the order should be placed first to create a durable record, then charge the customer, and finally update the order status. Emphasize that the system must be designed for idempotency and reconciliation to handle crashes and prevent double-charging. Discuss trade-offs between consistency and availability, and how to use distributed transactions or sagas.
Pro tip: Mention that you would use a unique idempotency key for each charge attempt and store it with the order, so retries don't double-charge. Also, highlight the importance of a reconciliation process to detect and resolve inconsistencies.
Explain that placing the order first creates a persistent record with a unique ID, which can be used as an idempotency key for the charge. This ensures that even if the charge fails, the order exists and can be retried or canceled.
Use idempotent operations: the charge API should accept an idempotency key (e.g., order ID) to prevent duplicate charges. Implement a state machine for the order (e.g., PENDING, CHARGED, FAILED) and use a write-ahead log or transactional outbox to ensure atomicity between order creation and charge initiation.
If a crash occurs after order placement but before charge, a background job can retry the charge using the order ID. If a crash occurs after charge but before updating the order, reconciliation can compare charge records with order records to update statuses. Use a saga pattern with compensating actions if needed.
Ensure the charge service is idempotent: it should check if a charge with the given idempotency key already exists and return the same result. Also, use database transactions or locks to prevent concurrent charge attempts for the same order.
Acknowledge that two-phase commit (2PC) across external services is often impractical; instead, prefer eventual consistency with idempotency and reconciliation. Mention that charging first could be an alternative but risks charging without an order, which is harder to reverse.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.