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.
MediumTechnical
71 practiced
Implement a Python function that merges two independent ETA estimates, each represented as (mean_seconds, variance_seconds_squared), into a single fused estimate using Gaussian weighting (assume errors are Gaussian and independent). Handle missing variance by assuming a default large variance, and return fused mean and variance.
MediumSystem Design
125 practiced
Design a quota and adaptive rate-limiting strategy for consuming upstream map/traffic APIs with strict daily quotas. How would you prioritize calls, implement adaptive throttling, fall back to cached values, and reserve emergency quota for critical operations?
HardSystem Design
79 practiced
Design an approach to compute ETAs for long-distance trips that cross multiple regions and potentially different traffic providers. Ensure low latency, consistent ETA presentation to users, and graceful handling of provider gaps, different coverage, and timezone differences.
MediumSystem Design
91 practiced
You must integrate two external traffic providers with different update cadences, geographic coverage, and measurement quality into ETA calculations. How would you reconcile conflicting travel-time signals, handle provider rate limits, failover, and gracefully degrade ETA when providers are unavailable?
HardTechnical
115 practiced
Discuss appropriate consistency models for driver location and ETA across microservices: eventual consistency, causal consistency, and strong consistency. For each model, explain implications on user experience, latency, system complexity, and examples of when each is acceptable in the ETA stack.
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