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

SeniorPrefer not to say

Apr 2026

Summary

System design round at Anthropic for a software engineering role. The whole thing was a web crawler question that started simple and kept escalating into distributed systems territory. Came away feeling like I held my own but definitely left some things on the table.

Questions Asked (1)

Design a web crawler, starting from a single-machine implementation and then scaling it to multiple servers. Cover the shared work queue, URL deduplication across nodes, per-domain rate limiting, failure handling and retry logic, and horizontal scalability. You don't need to fully implement the distributed version, just explain the architecture and the trade-offs.

System DesignTechnical Trade-offs

Author's notes

I started with the single-machine version which felt fine, a queue of URLs, a visited set, fetch and parse loop.

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AI HintsAI Generated

Suggested Approach

Start by outlining a single-machine crawler with a simple queue and in-memory deduplication, then systematically identify its limitations (single point of failure, limited throughput) and evolve the design to a distributed architecture. For each distributed component, explain the chosen approach and trade-offs, focusing on the shared work queue, deduplication, rate limiting, failure handling, and scalability.

Pro tip: Emphasize the trade-offs between consistency and availability in deduplication and rate limiting; for example, using a distributed cache with eventual consistency can improve performance but may allow occasional duplicate crawls, which is often acceptable.

1. Single-machine crawler design

Describe a basic crawler with a URL frontier (queue), a fetcher, a parser, and a deduplication set. Mention using a Bloom filter or hash set for deduplication and a simple rate limiter per domain.

2. Identify limitations and scaling needs

Discuss the bottlenecks: single point of failure, limited CPU/memory/bandwidth, and inability to scale horizontally. Explain why a distributed system is needed.

3. Distributed architecture overview

Propose a multi-node architecture with a shared work queue (e.g., Kafka, RabbitMQ, or Redis), distributed deduplication (e.g., Redis or Cassandra), and a distributed rate limiter (e.g., using Redis or a token bucket per domain).

4. Failure handling and retry logic

Explain how to handle node failures: use acknowledgments in the queue, implement exponential backoff with jitter for retries, and have a dead-letter queue for persistent failures.

5. Scalability and trade-offs

Discuss horizontal scaling by adding more crawler nodes, partitioning the URL space, and trade-offs like consistency vs. availability in deduplication, and latency vs. throughput in rate limiting.

Key Points to Mention

Shared work queue: use a distributed message queue (e.g., Kafka) for decoupling and fault tolerance.
URL deduplication: use a distributed set (e.g., Redis) or Bloom filter with partitioning; consider eventual consistency.
Per-domain rate limiting: implement a distributed token bucket or sliding window using Redis or a dedicated service.
Failure handling: at-least-once delivery, idempotent processing, exponential backoff, and dead-letter queues.
Horizontal scalability: stateless crawler nodes, partitioned queues, and dynamic scaling based on load.
Trade-offs: consistency vs. availability in deduplication, latency vs. throughput in rate limiting, and cost vs. performance.

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