Lyft ETA & Routing System Architecture Questions
Design and architecture of large-scale ride-hailing ETA and routing systems, covering distributed system design, real-time data processing, routing algorithms, fault tolerance, geo-distributed services, data consistency considerations, and integration with external mapping and traffic data sources.
HardTechnical
88 practiced
For large-scale map-matching, design an approximate nearest road-segment lookup suitable for low-latency operations. Focus on in-memory spatial indexing and caching strategies (e.g., geohash prefix, quadtrees, lightweight R-tree), update patterns, and how to handle moving vehicles without deep database design details.
HardSystem Design
70 practiced
Architect an end-to-end ETA and routing platform for global scale: 100M daily users, 10B location updates/day, 99th percentile API latency <200ms. Outline key services, data flows, consistency assumptions, cross-region concerns, cost/latency trade-offs, and major failure modes you would mitigate.
MediumTechnical
114 practiced
You want to validate ETA model improvements in production without impacting users. Propose an experiment plan: canary/A-B testing approach, metrics to measure (ETA error, cancellation, arrival variance), required sample sizes, and rollback criteria. Describe guardrails to prevent revenue or safety regressions.
MediumTechnical
126 practiced
Explain how eventual consistency can impact ETA correctness during a network partition where driver updates are delayed. Provide concrete failure scenarios, user-visible impacts, and mitigations (e.g., bounded staleness, client hints, read-repair) to reduce wrong ETAs while preserving availability.
MediumTechnical
73 practiced
Implement a function in Python that computes mean, variance, and a 95% confidence interval for ETA using a sample of past trip segment durations. Input: list of floats (seconds). Output: mean, variance, lower_95, upper_95. Describe assumptions about distribution and sample size and handle edge cases (N < 2).
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