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

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Jul 2026

Summary

Lyft system design round, one big sprawling question about building a distributed web crawler from scratch. They really wanted to see how you'd think through scale and failure modes across a thousand machines, not just the happy path.

Questions Asked (1)

Q1

Design a distributed web crawler that starts from a single seed URL and scales to 1,000 worker nodes. Cover URL partitioning and deduplication, politeness policies, prioritization, retry logic, content deduplication, failure handling, coordination, backpressure, fetch semantics, storage, monitoring, and safety controls. Also define APIs and data models for enqueueing URLs, checking status, and retrieving results.

System DesignTechnical Trade-offsData Modeling
Author's notes

This question is basically a whole system design interview wrapped into one prompt.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale (1,000 workers, seed URL, politeness, dedup, etc.), then sketch a high-level architecture with a URL frontier, worker pool, and storage layers. Dive into each component (partitioning, dedup, retries, backpressure, monitoring) while explicitly discussing trade-offs and API/data models. Conclude with failure handling and safety controls.

Pro tip: Emphasize idempotency and at-least-once processing with deduplication at multiple levels (URL and content) to handle retries and failures gracefully. Also, mention that politeness policies must be enforced per-domain, not per-worker, to avoid overloading any single site.

1. Clarify Requirements and Scope

Ask about scale (1,000 workers), crawl rate, politeness constraints, content types, and storage needs. Define functional and non-functional requirements (e.g., dedup, prioritization, fault tolerance).

2. High-Level Architecture

Outline components: URL frontier (partitioned queue), worker nodes, deduplication service, storage (raw content, metadata), and coordination service (e.g., ZooKeeper/etcd). Explain data flow from seed URL to results.

3. Deep Dive into Key Components

Discuss URL partitioning (consistent hashing), deduplication (Bloom filters + persistent store), politeness (per-domain rate limiting), prioritization (priority queues), retry logic (exponential backoff), content dedup (hashing), backpressure (queue monitoring), and fetch semantics (HTTP caching, robots.txt).

4. APIs and Data Models

Define REST/gRPC APIs for enqueueing URLs, checking status, and retrieving results. Specify data models for URL metadata, crawl status, and content storage (e.g., URL, priority, domain, next_fetch_time, status, content_hash).

5. Failure Handling, Monitoring, and Safety

Explain failure detection (heartbeats), recovery (reassigning partitions), monitoring (metrics, logging), and safety controls (rate limiting, robots.txt compliance, blacklists).

Key Points to Mention

  • URL partitioning via consistent hashing to distribute load and enable deduplication across workers.
  • Politeness policies: per-domain rate limiting and robots.txt compliance to avoid overloading servers.
  • Deduplication: URL dedup using Bloom filters + persistent storage; content dedup using hashing (e.g., SHA-256).
  • Backpressure: monitor queue depths and worker utilization; dynamically adjust crawl rate or scale workers.
  • Retry logic: exponential backoff with jitter, and dead-letter queues for persistent failures.
  • APIs: enqueue(url, priority), status(url), results(query) with clear data models for URL metadata and content.

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