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

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Jun 2026

Summary

Lyft system design round for a software engineering role. The main question was a distributed web crawler, and the follow-ups kept coming in waves, coordination, fault tolerance, load balancing. Left feeling like I covered the basics but probably didn't go deep enough on the trickier parts.

Questions Asked (1)

Design a distributed web crawler that starts from a single seed URL and spreads the work across 1,000 machines. Walk through coordination between nodes, load balancing, fault tolerance, and how the system scales.

System DesignTechnical Trade-offsAdaptability & Ambiguity

Author's notes

I started with a URL frontier and a central queue, which felt right, but then they pushed on what happens when a node dies mid-crawl and I kind of stumbled.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then outline a high-level architecture with a central coordinator and distributed workers. Dive into each component (coordination, load balancing, fault tolerance, scaling) while discussing trade-offs and alternatives. Conclude by summarizing how the design meets the goals and handles edge cases.

Pro tip: Emphasize the importance of politeness and respecting robots.txt to avoid legal issues and ensure ethical crawling. Also, discuss how you would monitor and adapt the system in real-time to handle dynamic web content and failures.

1. Clarify Requirements and Constraints

Ask questions to understand scale, crawl rate, politeness, data freshness, and storage needs. Define functional and non-functional requirements.

2. High-Level Architecture

Propose a master-worker architecture with a central coordinator (or distributed coordination service) and 1,000 worker nodes. Outline components: URL frontier, fetcher, parser, storage.

3. Coordination and Load Balancing

Explain how the coordinator assigns URLs to workers using a work queue, and how workers fetch and parse pages. Discuss load balancing strategies like consistent hashing or dynamic work stealing.

4. Fault Tolerance and Scalability

Describe mechanisms for handling worker failures (heartbeats, retries, checkpointing) and scaling the system by adding more workers or sharding the coordinator.

5. Trade-offs and Optimizations

Discuss trade-offs between centralization vs. decentralization, push vs. pull models, and optimizations like caching, rate limiting, and deduplication.

Key Points to Mention

Use of a distributed queue (e.g., Kafka, RabbitMQ) for URL distribution and back-pressure handling.
Deduplication of URLs using Bloom filters or distributed sets to avoid crawling the same page multiple times.
Politeness policies: respecting robots.txt, rate limiting per domain, and avoiding overloading servers.
Fault tolerance: worker heartbeats, task reassignment, and persistent storage of crawl state.
Scalability: horizontal scaling of workers, sharding the URL frontier, and using consistent hashing for domain-to-worker assignment.
Monitoring and metrics: tracking crawl rate, queue sizes, error rates, and adapting dynamically.

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