Problem Description
This problem asks you to design a production-grade web crawler service. The core challenge lies in efficiently traversing a vast number of pages or resources while strictly adhering to external rate limits imposed by upstream services. Each page expansion requires an outbound HTTP or API call, and these upstream services may have stringent throttling policies to prevent abuse or denial-of-service.
Your design must fulfill the following requirements:
- Traverse a large set of pages or resources efficiently.
- Avoid creating per-host bottlenecks within the crawler itself.
- Prevent overwhelming upstream services or accidentally causing a denial-of-service.
- Safely handle retries, timeouts, and partial failures.
- Scale effectively across multiple workers or machines.
Be prepared to discuss the overall architecture, the chosen concurrency model, request scheduling mechanisms, deduplication strategies, rate limiting implementation, backoff strategy, how fairness is ensured across different domains, and the observability aspects of your system.
AI interview prep
Core dilemma
Balancing aggressive, large-scale data acquisition with strict external rate limits, ensuring politeness, resilience, and efficient resource utilization across a distributed system.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Problem Description
This problem asks you to design a production-grade web crawler service. The core challenge lies in efficiently traversing a vast number of pages or resources while strictly adhering to external rate limits imposed by upstream services. Each page expansion requires an outbound HTTP or API call, and these upstream services may have stringent throttling policies to prevent abuse or denial-of-service.
Your design must fulfill the following requirements:
- Traverse a large set of pages or resources efficiently.
- Avoid creating per-host bottlenecks within the crawler itself.
- Prevent overwhelming upstream services or accidentally causing a denial-of-service.
- Safely handle retries, timeouts, and partial failures.
- Scale effectively across multiple workers or machines.
Be prepared to discuss the overall architecture, the chosen concurrency model, request scheduling mechanisms, deduplication strategies, rate limiting implementation, backoff strategy, how fairness is ensured across different domains, and the observability aspects of your system.