← Anthropic Interview Insights
I started with the URL frontier and worked outward, which felt right.
Start by clarifying the scale and scope requirements (e.g., billions of pages, crawl frequency, politeness constraints) before diving into components, as these constraints drive every architectural decision. Then walk through the system end-to-end — from seed URLs through the frontier, fetchers, parsers, and into storage — explicitly calling out trade-offs at each layer. Conclude by addressing cross-cutting concerns like deduplication, fault tolerance, and politeness to show systems-level maturity.
Pro tip: Interviewers at AI-focused companies like Anthropic care deeply about data quality and ethical considerations — proactively mention robots.txt compliance, crawl rate limiting per domain, and how you'd handle duplicate or near-duplicate content, as these signal you think beyond raw throughput to responsible system design.
Ask about target scale (e.g., 1B pages/month), crawl freshness requirements, politeness constraints, and whether this is a general-purpose or domain-specific crawler. These answers will justify your architectural choices throughout the discussion.
Describe a prioritized, distributed queue (e.g., backed by Kafka or Redis) that manages which URLs to crawl next, enforcing per-domain politeness delays and priority scoring based on PageRank or freshness signals. Explain how the frontier is partitioned by domain to prevent hammering a single host.
Outline a horizontally scalable pool of fetcher workers that respect robots.txt, set appropriate User-Agent headers, handle HTTP redirects, and implement exponential backoff on failures. Discuss DNS caching and connection pooling to reduce latency at scale.
Explain how fetched HTML is parsed to extract outbound links and page content, with deduplication handled via URL canonicalization and content fingerprinting (e.g., SimHash or MD5 stored in a distributed bloom filter or key-value store). Describe how newly discovered URLs are fed back into the frontier.
Describe a tiered storage strategy — raw HTML in object storage (e.g., S3), structured metadata in a distributed database (e.g., Cassandra or BigTable), and an inverted index for search if needed. Close by addressing monitoring, crawl scheduling, and how you'd handle failures or re-crawls.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and scale, then propose a distributed architecture with a central coordinator and stateless workers. Walk through each concern (concurrency, rate limiting, back-pressure, fault tolerance, delivery semantics) and explain trade-offs, emphasizing practical choices like at-least-once with idempotent processing.
Pro tip: Demonstrate awareness of real-world constraints: per-host rate limiting must be global across workers, and back-pressure should be handled at multiple levels (worker, queue, coordinator) to avoid cascading failures.
Ask about expected crawl volume, number of hosts, politeness requirements, and latency tolerance. This informs architecture choices like queue technology and worker count.
Propose a central coordinator (e.g., using a distributed queue like Kafka or Redis) that manages URL frontier and assigns tasks to stateless worker nodes. Workers fetch and parse pages, then push new URLs back to the coordinator.
Use multithreading within each worker for I/O-bound tasks, with a thread pool. Implement per-host rate limiting via a distributed token bucket or leaky bucket, ensuring global limits across all workers.
Apply back-pressure by bounding queues and using blocking calls or async backoff. For fault tolerance, use heartbeats, task leases, and retries with exponential backoff; ensure idempotent processing to handle duplicates.
Explain that exactly-once is impractical in distributed systems; recommend at-least-once with idempotent operations (e.g., deduplication via URL hashing) to achieve effectively-once processing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.