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

Senior

Jun 2026

Summary

DoorDash system design round, one meaty question about keeping your own service healthy when a dependency starts falling apart. Pretty intense scope for a single question, they clearly wanted to see how far you could push the design.

Questions Asked (1)

If a service you depend on suddenly starts responding slowly, how do you make sure your own service still meets its latency targets?

System DesignTechnical Trade-offsRoot Cause Analysis

Author's notes

This question has a lot of surface area and I kind of scattered my answer all over the place.

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AI HintsAI Generated

Suggested Approach

Start by acknowledging the problem and proposing immediate mitigation strategies like timeouts, circuit breakers, and fallbacks to protect your service's latency. Then discuss longer-term solutions such as caching, asynchronous processing, and capacity planning, while emphasizing monitoring and root cause analysis. Conclude by highlighting the importance of graceful degradation and maintaining user experience.

Pro tip: Quantify the impact: mention specific latency targets (e.g., p99 < 200ms) and how you'd measure the dependency's contribution to your latency, showing you think in terms of SLIs/SLOs. Also, emphasize that you'd communicate proactively with stakeholders about any potential degradation.

1. Detect and Assess

Identify the slowdown through monitoring and determine its impact on your service's latency. Check metrics like response times, error rates, and dependency call durations.

2. Immediate Mitigation

Implement short-term fixes to protect your latency: set aggressive timeouts, enable circuit breakers, and use fallback responses (e.g., cached data or default values).

3. Long-Term Resilience

Design your service to be resilient: add caching, use asynchronous calls, implement bulkheads, and consider degrading non-critical features to preserve core functionality.

4. Monitor and Iterate

Continuously monitor latency and dependency health, set up alerts, and conduct post-mortems to prevent future occurrences. Adjust strategies based on learnings.

Key Points to Mention

Timeouts and retries with exponential backoff and jitter
Circuit breaker pattern to fail fast and prevent cascading failures
Fallback mechanisms such as cached responses or default values
Asynchronous processing and queueing to decouple from slow dependencies
Caching strategies (e.g., local cache, Redis) to reduce dependency calls
Monitoring and alerting on latency SLIs/SLOs, and root cause analysis

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