This was basically every system design topic crammed into one question.
Start by clarifying requirements and scale (e.g., expected donations per second, global regions, consistency needs), then design the high-level architecture covering core components: API gateway, donation service, payment integration, data stores, and real-time leaderboard. Dive into critical details like idempotency, fraud detection, rate limiting, and failure handling, explaining trade-offs (e.g., consistency vs. availability, SQL vs. NoSQL) and how they address the three-day campaign's unique challenges.
Pro tip: Emphasize idempotency and exactly-once processing for payments, as duplicate charges are a major risk; use idempotency keys and a ledger-based data model to ensure correctness. Also, discuss how you'd handle delayed webhooks and backpressure to maintain system stability under peak load.
Ask questions to understand expected traffic (e.g., donations per second, peak times), global regions, consistency requirements, and budget constraints. Define functional and non-functional requirements.
Sketch the main components: API gateway, donation service, payment service, database, cache, message queue, and analytics pipeline. Explain how they interact and the data flow.
Detail payment idempotency, fraud checks, rate limiting, backpressure, delayed webhooks, and real-time leaderboards. Discuss trade-offs and specific technologies (e.g., Redis for leaderboards, Kafka for streaming).
Cover regional availability, data partitioning, caching strategies, observability, and failure handling (e.g., retries, circuit breakers, dead letter queues). Explain how the system meets the three-day campaign's demands.
Recap the design, highlighting key decisions and their trade-offs (e.g., consistency vs. latency, cost vs. performance). Mention potential bottlenecks and how to mitigate them.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Follow-up that came after I mentioned idempotency keys.
Start by clarifying the system's payment flow and failure modes, then propose a layered strategy: idempotency for double-submits, retries with exponential backoff and circuit breakers for outages, and fallback providers or queuing for resilience. Emphasize trade-offs between consistency, availability, and complexity, and tie your answer to DoorDash's high-volume, low-latency environment.
Pro tip: Mention that you'd use idempotency keys tied to the order ID and store them with a TTL, and that you'd monitor provider health with synthetic transactions to fail over proactively—this shows you've thought about real-world production issues beyond textbook solutions.
Ask about expected throughput, consistency requirements, and the impact of payment failures on user experience. Identify specific failure scenarios: provider downtime, timeouts, duplicate requests, and partial failures.
Propose using idempotency keys (e.g., order ID + user ID) stored in a fast datastore with TTL, and ensure the payment provider supports idempotent APIs. Discuss client-side and server-side deduplication.
Outline retry logic with exponential backoff and jitter, circuit breakers to avoid cascading failures, and a fallback provider or queue for asynchronous processing. Consider graceful degradation and user communication.
Describe monitoring of provider health, alerting on error rates, and reconciliation jobs to detect and resolve inconsistencies. Include manual intervention paths for stuck transactions.
Compare synchronous vs. asynchronous payment processing, strong vs. eventual consistency, and the cost of adding redundancy. Acknowledge that no solution is perfect and explain how you'd prioritize based on business needs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with partitioning donations by campaign ID and then by time bucket since the query patterns are mostly campaign-scoped and time-range-scoped.
Start by clarifying the platform's scale and access patterns, then propose a partitioning strategy that aligns with query patterns and data distribution. Discuss storage choices (e.g., SQL vs NoSQL, hot vs cold storage) and trade-offs around consistency, latency, and cost. Conclude with how you'd handle rebalancing and growth.
Pro tip: Tie your partitioning key to the most frequent query pattern and explicitly call out how you avoid hotspots—this shows you understand real-world production concerns beyond theory.
Ask about data volume, read/write ratio, latency SLAs, and geographic distribution to ground your design in concrete numbers.
Select a partition key (e.g., user_id, order_id, geo) based on access patterns, and decide between hash, range, or composite partitioning to balance load.
Map data types to appropriate stores (e.g., relational for transactions, NoSQL for scale, blob storage for media) and consider hot/cold tiering.
Discuss CAP theorem implications, consistency models (strong vs eventual), and how they affect user experience and system complexity.
Explain how you'd handle rebalancing, resharding, monitoring, and failure recovery as the platform grows.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Redis sorted sets felt like the obvious answer and I led with that.
Start by clarifying requirements: define 'real-time' (e.g., sub-second vs. minute-level), the expected write throughput (e.g., 100K writes/sec), and the read patterns for the leaderboard. Then propose a scalable architecture that decouples writes from reads, using an append-only log or stream processing for aggregation and a fast in-memory store for serving the leaderboard.
Pro tip: Emphasize that perfect accuracy may not be required; approximate counts with bounded error (e.g., using HyperLogLog or Count-Min Sketch) can drastically reduce cost and complexity while still meeting product needs. Also, discuss how to handle late-arriving data and ensure idempotency to avoid double-counting.
Ask about write volume, read latency, consistency needs, and whether the leaderboard must be exact or can be approximate. Also consider the campaign duration and peak patterns.
Use a distributed message queue (e.g., Kafka) to absorb spikes and decouple producers from consumers. Ensure writes are partitioned by user or campaign to enable parallel processing.
Process the stream with a stream processor (e.g., Flink, Spark Streaming) to compute running totals. Use windowing and watermarks to handle late data, and maintain state in a scalable store (e.g., RocksDB).
Store aggregated totals in a low-latency database (e.g., Redis, DynamoDB) and maintain a sorted set for the leaderboard. Use caching and read replicas to scale reads.
Discuss consistency vs. availability, exactly-once vs. at-least-once processing, and how to recover from failures. Mention monitoring and alerting for lag and errors.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.