I went straight to input validation at the API boundary and talked about rejecting early, but the interviewer kept pushing on the tolerate-vs-reject tradeoff.
Start by framing input validation as a layered defense: validate at the edge (API gateway), then in the service layer, and finally at the data layer. Discuss specific techniques like schema validation, sanitization, and rate limiting, and explain how you decide between failing fast (for security-critical or contract violations) and tolerating bad data (for non-critical fields or backward compatibility). Use examples from a high-scale, real-time system like DoorDash to illustrate trade-offs.
Pro tip: Emphasize that you treat validation as a product and security concern, not just a technical one—e.g., logging malformed inputs for observability and using feature flags to gradually enforce stricter validation without breaking existing clients.
Ask about the service's role, expected input sources, SLAs, and security requirements to tailor your approach. For example, a payment service may require strict fail-fast, while a logging service might tolerate more.
Describe a multi-layered strategy: edge validation (API gateway, WAF), service-level schema validation (e.g., JSON Schema, Protobuf), and domain-specific business rule checks. Mention sanitization for injection attacks (SQL, XSS) and normalization.
Explain criteria: fail fast for security-critical fields, contract violations, or when data integrity is paramount; tolerate with logging and defaulting for non-critical fields or during migration periods. Consider idempotency and retries.
Detail how you'd monitor validation failures, log malformed inputs (with PII redaction), and alert on anomalies. Use metrics to inform whether to tighten or loosen validation.
Discuss how you'd handle schema evolution, backward compatibility, and gradual rollout of stricter validation using feature flags or canary deployments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through external sort, streaming, and sharding.
Start by clarifying the constraints (data size, processing frequency, latency requirements, and available infrastructure) before proposing solutions. Then present a structured set of options—streaming, chunking, external memory algorithms, distributed processing, and out-of-core databases—and explain how you'd choose based on trade-offs like complexity, cost, and performance. Finally, tie your answer to a concrete example relevant to DoorDash, such as processing delivery logs or order histories.
Pro tip: Emphasize that the best solution often depends on whether the data is static or streaming, and whether you need real-time or batch results—showing you can tailor the approach to the business need rather than just listing technologies.
Ask about data size, growth rate, processing frequency, latency needs, and available resources (memory, disk, cluster). This ensures your solution fits the actual problem.
List approaches such as streaming/chunked processing, external sorting, memory-mapped files, distributed frameworks (Spark, Flink), and out-of-core databases. Briefly describe each.
Compare options on dimensions like implementation complexity, cost, scalability, fault tolerance, and latency. Highlight when each is most appropriate.
Choose the best option for the given scenario and explain why, referencing the trade-offs. If possible, mention a fallback or hybrid approach.
Connect your answer to a plausible DoorDash use case, such as processing delivery event streams or large order datasets, to show practical relevance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went through fallbacks and degraded modes pretty quickly but then blanked a bit on idempotency with retries.
Start by clarifying the dependency's criticality and failure modes, then outline a layered resilience strategy covering timeouts, retries, circuit breakers, fallbacks, and graceful degradation. Emphasize trade-offs between consistency, availability, and complexity, and tie your choices to DoorDash's high-availability, real-time delivery context.
Pro tip: Show maturity by discussing how you'd validate resilience through chaos engineering and load testing, and how you'd monitor and alert on dependency health to catch degradation before full outages.
Ask about the dependency's role, expected latency, criticality, and failure types (partial vs. full). Identify what happens if the dependency is slow, unavailable, or returns errors.
Apply timeouts, retries with exponential backoff and jitter, circuit breakers, bulkheads, and rate limiting to prevent cascading failures and resource exhaustion.
Define fallback strategies: cached responses, default values, degraded features, or asynchronous processing. Ensure the system remains partially functional and user experience is acceptable.
Add metrics, logging, and tracing for dependency calls. Use chaos engineering and fault injection to validate resilience. Set up alerts for error rates and latency.
Acknowledge trade-offs: consistency vs. availability, complexity vs. resilience, cost vs. redundancy. Explain how you'd prioritize based on business impact and iterate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Circuit breakers, timeouts, exponential backoff with jitter, rate limiting.
Frame your answer around reliability, resilience, and observability, since production integrations must handle failures gracefully. Walk through the key layers: timeouts, retries with backoff, circuit breakers, idempotency, and monitoring. Tailor examples to high-scale, low-latency environments like DoorDash, where third-party outages can impact orders and deliveries.
Pro tip: Emphasize idempotency and graceful degradation—interviewers at DoorDash care about preventing duplicate orders or payments and keeping the core experience working even when a third-party API is down. Mention concrete metrics (e.g., p99 latency, error rates) to show you think in production terms.
Start by identifying what can go wrong (timeouts, errors, rate limits, partial failures) and what the business impact is. Clarify SLAs, expected traffic, and whether the call is on a critical path.
Use timeouts, retries with exponential backoff and jitter, and circuit breakers to avoid cascading failures. Ensure idempotency keys for non-idempotent operations to safely retry.
Instrument metrics (latency, error rates, retry counts), structured logging, and distributed tracing. Set up alerts for anomalies and dashboards for real-time monitoring.
Design fallback behavior: cached responses, default values, or queuing for later processing. Ensure the system can degrade gracefully without breaking the user experience.
Use chaos engineering, fault injection, and load testing to validate resilience. Continuously review and update based on incidents and changing third-party behavior.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements (throughput, latency, ordering, delivery guarantees) and then sketch a high-level producer/consumer architecture with a message broker. Focus on partitioning strategy, explaining how you'd choose a partition key and the tradeoffs between throughput, ordering, and operational complexity. Conclude by discussing how partitioning impacts scalability, fault tolerance, and consumer group rebalancing.
Pro tip: Tie partitioning choices directly to business impact—e.g., for DoorDash, partitioning by order ID ensures per-order ordering while allowing horizontal scaling, but hot partitions from popular restaurants require mitigation like composite keys or dynamic partitioning.
Ask about expected throughput, latency SLAs, ordering guarantees, and delivery semantics (at-least-once vs exactly-once). This shapes the entire design.
Propose producers publishing to a distributed log (e.g., Kafka) with consumers in groups. Mention decoupling, buffering, and replayability.
Explain how to choose partition keys (e.g., order ID, user ID) to balance load and preserve ordering. Discuss number of partitions and scaling implications.
Compare tradeoffs: more partitions increase parallelism but add overhead and rebalancing cost; key-based partitioning ensures ordering but risks hot spots; random partitioning maximizes throughput but loses ordering.
Cover consumer rebalancing, offset management, backpressure, and monitoring. Mention how partitioning affects recovery and exactly-once semantics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is one of those questions that sounds easy until you're live.
Start by clearly defining at-least-once and exactly-once delivery, emphasizing that exactly-once is a system-wide property requiring coordination between producers, brokers, and consumers. Then explain practical techniques like idempotent producers, transactional messaging, and deduplication, and discuss trade-offs such as latency and complexity. Finally, relate it to real-world systems like Kafka and DoorDash's order processing to show applied understanding.
Pro tip: Acknowledge that true exactly-once delivery is impossible in distributed systems without assumptions, so the practical goal is exactly-once processing via idempotency and deduplication. This shows you understand the theoretical limits and focus on pragmatic solutions.
Explain at-least-once (messages may be duplicated but not lost) and exactly-once (each message processed exactly once, no duplicates or losses). Mention at-most-once for completeness.
Discuss the two-generals problem, network failures, and the impossibility of guaranteeing exactly-once delivery without idempotency or transactions. Highlight that it's a system-wide concern.
Cover idempotent producers (e.g., Kafka's idempotent producer), transactional messaging (e.g., Kafka transactions), consumer-side deduplication (e.g., storing message IDs), and exactly-once processing frameworks (e.g., Flink).
Mention increased latency, complexity, and resource usage. Give examples like Kafka's exactly-once semantics (EOS) and how DoorDash might use it for order processing or payment systems.
Summarize that achieving exactly-once requires end-to-end design: idempotent operations, transactional boundaries, and monitoring. Emphasize choosing the right semantics based on business needs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Latency requirements, data freshness, operational complexity.
Start by defining the core trade-off between latency and throughput, then walk through the key factors that drive the decision, such as data volume, latency requirements, complexity, and cost. Use concrete examples from DoorDash's domain (e.g., real-time order tracking vs. daily sales reports) to illustrate when each approach is appropriate.
Pro tip: Emphasize that the decision is not binary—many systems use a hybrid approach (e.g., Lambda architecture) where batch and streaming complement each other. Also, mention that operational complexity and team expertise often tip the scales in real-world scenarios.
Identify the latency, throughput, and accuracy requirements of the use case. Ask questions to understand if the data needs to be processed immediately or if delayed insights are acceptable.
Consider the volume, velocity, and variety of data. Streaming is better for high-velocity, continuous data; batch is suitable for large volumes of bounded data.
Compare factors like latency, cost, complexity, fault tolerance, and exactly-once semantics. Streaming often introduces higher operational overhead but provides lower latency.
Discuss scenarios where combining both (e.g., streaming for real-time alerts, batch for historical analysis) can be optimal. Mention architectures like Lambda or Kappa.
Tie the decision back to business impact: real-time personalization vs. daily reporting, cost constraints, and team readiness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.