DoorDash·Software Engineer·Onsite - System Design / Architecture·Senior

Senior

Jul 2026

Summary

DoorDash system design round focused entirely on a gnarly incident scenario involving a payment-integration service. One long, multi-part question that kept branching into follow-ups. Pretty intense for what felt like a single prompt.

Questions Asked (1)

Your internal payment-integration service is showing high CPU and memory usage, autoscaling nodes keep crashing on startup, upstream traffic is 10x normal, and downstream calls to a financial institution are timing out with no error details. No recent deployments. Walk through how you'd mitigate the incident immediately, investigate root cause systematically, and prevent recurrence long-term.

System DesignRoot Cause AnalysisTechnical Trade-offs

Author's notes

This question is basically three questions stitched together and they absolutely expect you to cover all three.

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AI HintsAI Generated

Suggested Approach

Start by acknowledging the incident and prioritizing immediate mitigation to restore service, then systematically investigate root cause using observability data and isolation techniques, and finally propose long-term preventive measures. Emphasize communication, prioritization, and learning from the incident.

Pro tip: During mitigation, focus on reducing blast radius first (e.g., shedding load, circuit breaking) rather than finding the root cause—this shows you understand incident response priorities. Also, mention that you'd document everything for a post-mortem to drive systemic improvements.

1. Immediate Mitigation

Stabilize the system by reducing load (e.g., rate limiting, queueing), scaling down non-critical services, and enabling circuit breakers or fallbacks for downstream calls. Communicate status to stakeholders and assemble an incident response team.

2. Triage and Data Collection

Gather metrics, logs, and traces from all affected components (CPU/memory, autoscaling events, upstream traffic patterns, downstream timeouts). Check for recent changes in configuration, infrastructure, or dependencies, even if no code deployments occurred.

3. Root Cause Analysis

Form hypotheses and test them systematically: e.g., is the upstream traffic legitimate or a DDoS? Are downstream timeouts due to network issues, rate limits, or a degraded third-party? Use tools like distributed tracing, profiling, and dependency graphs to pinpoint the failure.

4. Resolution and Recovery

Once root cause is identified, apply a targeted fix (e.g., rollback a config change, add capacity, fix a bug). Verify recovery with metrics and gradually restore normal traffic, monitoring for recurrence.

5. Prevention and Follow-up

Conduct a blameless post-mortem, implement long-term fixes (e.g., better autoscaling policies, circuit breakers, load shedding, improved observability, chaos testing), and update runbooks and alerts to prevent similar incidents.

Key Points to Mention

Prioritize mitigation over root cause during active incident to minimize customer impact.
Use observability tools (metrics, logs, traces) to correlate events across services.
Consider upstream traffic spike as potential cause (e.g., retry storm, DDoS, misbehaving client) and downstream timeouts as symptom.
Check for non-deployment changes: config updates, feature flags, infrastructure changes, or third-party issues.
Implement circuit breakers, rate limiting, and graceful degradation to protect services.
Conduct blameless post-mortem and implement preventive measures like load testing, chaos engineering, and improved autoscaling.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.