I started with the listing entity and worked outward.
Start by clarifying the scope and requirements (e.g., scale, consistency needs, search functionality) before diving into the design. Then, propose a high-level architecture, define the core entities and their relationships, and design RESTful APIs for listing and purchasing items. Finally, discuss trade-offs and potential optimizations.
Pro tip: Emphasize idempotency and concurrency control in the purchase flow to prevent double-selling, and mention how you'd handle search and recommendations, as these are critical for eBay's marketplace.
Ask questions to understand scale, consistency, latency, and features like search, bidding, and payments. This shows you can tailor the design to real-world constraints.
Identify core entities (User, Item, Order, etc.) and their relationships. Choose appropriate database technologies (e.g., SQL for transactions, NoSQL for scale) and discuss indexing for search.
Define RESTful endpoints for listing items, searching, purchasing, and managing orders. Include request/response schemas, status codes, and authentication/authorization.
Discuss concurrency (e.g., preventing double-selling), idempotency, scalability (sharding, caching), and consistency (e.g., eventual consistency for search).
Recap the design, highlight trade-offs made, and suggest potential improvements or extensions (e.g., microservices, event-driven architecture).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about Elasticsearch as the search layer with a separate write path syncing from the primary DB.
Start by clarifying requirements and scale (e.g., QPS, data volume, latency SLOs), then propose a high-level architecture that separates write and read paths, using an inverted index for search and a columnar store for filtering. Discuss trade-offs between consistency, latency, and cost, and how to evolve the design as scale grows.
Pro tip: Emphasize the importance of a two-phase retrieval: first use an inverted index to get a candidate set, then apply filters and ranking. This avoids expensive full scans and is key to scaling.
Ask about expected QPS, data size, latency requirements, consistency needs, and query types (keyword, filters, facets). This ensures the design meets actual needs.
Propose a system with separate indexing pipeline (for writes) and query service (for reads). Use a distributed search engine (e.g., Elasticsearch) for text search and a columnar database (e.g., Cassandra, BigQuery) for filters.
Design the index schema: inverted index for text, doc values for filters, and denormalized fields for sorting. Discuss sharding, replication, and refresh strategies.
Explain how queries are processed: parse, rewrite, retrieve candidates from index, apply filters, rank, and paginate. Mention caching, query planning, and avoiding deep pagination.
Discuss trade-offs: consistency vs. latency, index freshness vs. throughput, cost vs. performance. Cover scaling strategies: horizontal scaling, tiered storage, and read replicas.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
CDN plus object storage, resize on upload, generate multiple resolutions.
Structure your answer around the end-to-end lifecycle of an image: ingestion, processing, storage, delivery, and lifecycle management. Emphasize trade-offs at each stage, such as synchronous vs. asynchronous processing, storage tiers, and CDN strategies, while tying decisions back to eBay's scale and user experience.
Pro tip: Proactively discuss failure modes and cost implications—e.g., what happens if image processing fails, or how to optimize storage costs without sacrificing performance. This shows you think beyond the happy path and understand production realities.
Describe how to validate images on the client (file type, size, dimensions) to reduce server load, and choose an upload method (direct-to-S3 with pre-signed URLs vs. through API) based on security and scalability needs.
Explain how to decouple upload from processing using a queue (e.g., Kafka, SQS) to handle resizing, format conversion, and thumbnail generation asynchronously, ensuring the user gets a fast response.
Discuss storing original and processed images in object storage (e.g., S3) with appropriate tiers (hot vs. cold), and maintaining a metadata database (e.g., DynamoDB) for quick lookups and associations with listings.
Cover serving images via a CDN with cache-control headers, using responsive image techniques (srcset, WebP) to optimize for different devices, and implementing on-the-fly resizing or pre-generated variants.
Mention policies for deleting or archiving images when listings are removed, and monitoring pipeline health (e.g., queue depth, processing latency) to ensure reliability and cost efficiency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one tripped me up more than I expected.
Start by clarifying requirements and scale, then design a high-level architecture that separates payment processing from escrow management, using idempotent operations and a ledger for consistency. Focus on trade-offs between consistency, availability, and latency, and explain how you would handle failures and reconciliation.
Pro tip: Emphasize idempotency and exactly-once processing for payment operations, as duplicate charges or missed payments are critical issues in marketplaces. Also, mention the importance of a double-entry ledger for auditability and reconciliation.
Ask questions to understand expected transaction volume, supported payment methods, regulatory constraints, and escrow duration. This ensures the design meets business needs and scales appropriately.
Outline core components: payment service, escrow service, ledger, and notification service. Explain how they interact and the data flow for a transaction.
Design a double-entry ledger for financial accuracy and discuss consistency models (e.g., strong consistency for balances, eventual consistency for notifications). Address idempotency keys to prevent duplicate operations.
Describe strategies for handling network failures, timeouts, and partial failures, including retries with exponential backoff, dead-letter queues, and reconciliation jobs.
Discuss trade-offs between consistency and availability (e.g., CAP theorem), and how to scale horizontally. Consider using a distributed transaction pattern like Saga for escrow release.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about atomic decrement in the DB with a check-and-set pattern, and mentioned distributed locks as an alternative if inventory lived across services.
Start by clarifying the scale and consistency requirements, then propose a layered architecture that combines strong consistency for inventory decrements with caching and asynchronous processing for high read throughput. Emphasize how you prevent overselling through atomic operations, reservations, and idempotency, while maintaining availability and performance.
Pro tip: Mention that overselling is often a business trade-off—sometimes allowing limited overselling with compensation (e.g., backorders) is acceptable, but for high-value items you need strict consistency. Show you understand the difference between preventing overselling and handling it gracefully.
Ask about scale (e.g., millions of items, high concurrency), consistency needs (strong vs eventual), and latency requirements. This shows you tailor the solution to the problem.
Propose a schema that tracks inventory counts, reservations, and versioning. Consider using a relational database with ACID transactions for critical updates, or a distributed store with conditional writes.
Describe how to atomically decrement stock using optimistic locking (version numbers) or pessimistic locking, and how to create time-bound reservations to hold items during checkout.
Explain techniques like idempotency keys for order operations, distributed locks, and queue-based processing to serialize updates and avoid race conditions.
Discuss caching strategies (e.g., read replicas, Redis for hot items), sharding by item ID, and fallback mechanisms like eventual consistency with reconciliation to handle failures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I covered the basics: velocity checks, device fingerprinting, flagging accounts with unusual listing patterns.
Start by framing fraud prevention as a risk management problem that requires balancing security with user experience and business goals. Then walk through a layered, data-driven system design that detects and mitigates fraud in real-time, and discuss key trade-offs such as false positives vs. false negatives and latency vs. accuracy.
Pro tip: Emphasize that fraud prevention is an adversarial problem: attackers constantly adapt, so your system must continuously learn and evolve. Mention the importance of feedback loops and human-in-the-loop review to improve models over time.
Identify common fraud types in marketplaces (e.g., fake listings, payment fraud, account takeover) and the actors involved. Clarify business impact and risk tolerance.
Propose multiple layers: prevention (e.g., verification, rate limiting), detection (e.g., ML models, rules), and mitigation (e.g., holds, manual review). Explain how they work together.
Describe key data sources (user behavior, transaction history, device fingerprints) and how to engineer features for real-time scoring. Mention the need for both batch and stream processing.
Discuss trade-offs like false positives vs. false negatives, latency vs. accuracy, and automation vs. manual review. Define success metrics (e.g., fraud rate, precision/recall, user friction).
Explain how to build feedback loops, monitor model performance, and update rules/models to counter evolving threats. Highlight the importance of A/B testing and human review.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Token bucket at the API gateway, keyed by user ID and IP.
Start by clarifying requirements such as scale, API types, and rate limit policies. Then propose a distributed rate limiting solution using a centralized store like Redis, and discuss algorithms like token bucket or sliding window. Finally, cover implementation details including middleware, monitoring, and failure handling.
Pro tip: Emphasize the importance of graceful degradation and clear communication with API consumers through headers like X-RateLimit-Remaining and Retry-After. Also, mention the need for dynamic configuration to adjust limits without redeploying.
Ask about the scale (requests per second), types of APIs (public, internal, partner), and desired rate limit policies (per user, per IP, per API key). Understand the consequences of exceeding limits and the need for different tiers.
Select an algorithm such as token bucket, leaky bucket, fixed window, or sliding window. Consider trade-offs between accuracy, memory usage, and burst handling. For eBay's scale, a distributed approach with Redis and sliding window or token bucket is suitable.
Use a centralized data store like Redis to maintain counters across multiple API servers. Implement atomic operations (e.g., Lua scripts) to avoid race conditions. Consider sharding or clustering for high availability and scalability.
Integrate rate limiting as middleware in the API gateway or application layer. Extract identifiers (API key, user ID, IP) and check against limits. Return 429 Too Many Requests with appropriate headers when limits are exceeded.
Set up monitoring for rate limit hits, latency, and Redis performance. Use metrics to adjust limits and identify abuse. Implement logging and alerting for anomalies. Ensure the system can degrade gracefully if Redis is unavailable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went through the standard triad: structured logs, distributed traces, and metrics with percentile latency alerts.
Start by framing observability around the system's key user journeys and SLOs, then describe how metrics, logs, and alerts work together to detect and diagnose issues. Emphasize a layered approach: metrics for detection, logs for diagnosis, and alerts for actionable response, tailored to eBay's scale and e-commerce criticality.
Pro tip: Tie every metric and alert to a user-facing SLO and explicitly discuss reducing alert noise through aggregation and intelligent thresholds—this shows you understand production maturity beyond just tooling.
Identify the most important user flows (e.g., search, checkout) and set measurable SLOs for latency, error rate, and availability. This anchors all observability decisions.
Collect latency, traffic, errors, and saturation metrics at every layer (service, host, dependency). Use histograms for latency and counters for errors to enable percentile analysis.
Emit structured logs (JSON) with consistent fields and propagate a request ID across services. This enables fast root-cause analysis and trace reconstruction.
Alert on symptoms (e.g., SLO violation) rather than causes, using multi-window burn-rate alerts to balance sensitivity and noise. Route alerts to the right on-call teams with runbooks.
Create dashboards for real-time monitoring and trend analysis, and use incident postmortems to refine metrics, logs, and alert thresholds continuously.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Partitioned the orders table by user ID, talked through hot partition problems with power sellers, and suggested a hash-range hybrid.
Start by clarifying the system's requirements and current bottlenecks, then systematically address each scaling technique (partitioning, indexing, caching, CDN) with trade-offs and eBay-specific considerations. Conclude by discussing how these techniques work together and potential challenges in implementation.
Pro tip: At eBay, emphasize how partitioning and caching can handle the massive scale of listings and bids, and mention real-world constraints like data consistency and cost. Show awareness of eBay's specific architecture, such as their use of sharding and CDN for static assets.
Ask about the system's scale, read/write ratio, latency requirements, and current pain points to tailor your answer. This shows you don't jump to solutions without understanding the problem.
Explain how you would partition data (e.g., by user ID, item category, or geographic region) to distribute load. Discuss trade-offs like hotspotting and rebalancing.
Describe indexing strategies for efficient queries, such as composite indexes, covering indexes, and avoiding over-indexing. Mention how indexing interacts with partitioning.
Outline caching at multiple levels (client, CDN, application, database) with appropriate eviction policies and consistency considerations. Highlight cache invalidation challenges.
Explain how a CDN can offload static and dynamic content, reduce latency, and handle traffic spikes. Discuss cache headers, edge logic, and invalidation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Active-active vs active-passive tradeoff, I argued for active-passive with read replicas in secondary regions and a promotion playbook for failover.
Start by clarifying the scope and requirements, such as expected traffic, data consistency needs, and recovery objectives (RTO/RPO). Then, propose a multi-region architecture with active-active or active-passive setups, detailing components like data replication, failover mechanisms, and disaster recovery drills. Finally, discuss trade-offs between cost, complexity, and resilience, and how you would validate the plan.
Pro tip: Emphasize the importance of defining clear RTO and RPO metrics early, as they drive architectural decisions and demonstrate business alignment. Also, mention the need for regular disaster recovery testing to ensure the plan works under real-world conditions.
Ask questions to understand the expected scale, user distribution, data consistency requirements, and recovery objectives (RTO/RPO). This ensures your design meets business needs.
Outline a high-level architecture with regions, availability zones, and key components like load balancers, databases, and caching. Choose between active-active or active-passive based on requirements.
Explain how data will be replicated across regions (e.g., synchronous vs asynchronous) and how failover will be triggered and managed, including DNS routing and health checks.
Describe backup strategies, recovery procedures, and regular DR drills. Discuss how to handle region-wide failures and ensure minimal data loss and downtime.
Analyze trade-offs between cost, latency, consistency, and complexity. Explain how you would test and monitor the deployment to ensure it meets SLAs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.