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

SeniorPrefer not to say

May 2026Remote

Summary

DoorDash system design round focused entirely on a cache debugging scenario. Pretty intense for a single question but it covered a lot of ground, from initial triage all the way through post-incident process.

Questions Asked (1)

You get paged for elevated latency and a spike in cache misses. Walk through your full debugging plan: what you ask the on-call partner, which metrics you check, how you form and test hypotheses, what mitigations you propose, and what post-incident steps you take.

Root Cause AnalysisSystem DesignTechnical Trade-offs

Author's notes

This one sprawled in a way I wasn't fully ready for.

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AI HintsAI Generated

Suggested Approach

Structure your answer as a chronological incident response: start with information gathering from the on-call partner, then move to metric analysis, hypothesis-driven debugging, mitigation, and post-incident review. Emphasize clear communication, prioritization of user impact, and systematic elimination of causes. Show that you balance speed with thoroughness and learn from the incident.

Pro tip: Always state that you first check if the issue is widespread or isolated to a specific region/service, and immediately assess user impact to decide on mitigation urgency. This shows you prioritize customer experience and can make quick, informed decisions under pressure.

1. Gather Information and Assess Impact

Ask the on-call partner for specifics: when the latency started, which services/regions are affected, recent deployments, and any error logs. Simultaneously check dashboards for user-facing impact (e.g., order success rate) to determine severity.

2. Analyze Metrics and Form Hypotheses

Examine key metrics: cache hit/miss ratio, latency percentiles (p50, p95, p99), request rates, error rates, and resource utilization (CPU, memory, network). Look for correlations and anomalies to form hypotheses about root cause (e.g., cache stampede, memory leak, traffic spike).

3. Test Hypotheses and Isolate Cause

Use tools like distributed tracing, logs, and profiling to validate or eliminate hypotheses. For example, if cache misses spiked, check if a recent deployment changed cache keys or TTLs, or if a hot key expired. Isolate the cause by comparing affected vs. unaffected components.

4. Mitigate and Communicate

Propose immediate mitigations: rollback recent changes, scale up resources, warm caches, or enable circuit breakers. Communicate status and actions to stakeholders, and document the timeline for post-mortem.

5. Post-Incident Review and Prevention

Conduct a blameless post-mortem: identify root cause, contributing factors, and action items (e.g., add monitoring, improve cache invalidation, load testing). Share learnings with the team and track follow-ups.

Key Points to Mention

Prioritize user impact and communicate clearly with stakeholders.
Use a systematic approach: gather data, form hypotheses, test, and iterate.
Leverage observability tools: metrics, logs, traces, and dashboards.
Consider common causes: cache stampede, hot keys, deployment issues, traffic spikes, resource exhaustion.
Propose both short-term mitigations and long-term fixes.
Emphasize blameless post-mortem and continuous improvement.

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