How do you design a massively scalable web crawler capable of processing 10 billion web pages in just five days—while staying polite, fault-tolerant, and efficient? In this video, we break down a real-world system design problem focused on building a web crawler specifically for training Large Language Models (LLMs).
We walk through a production-grade architecture using a multi-stage pipeline powered by distributed crawlers, SQS queues, and S3 blob storage to handle extreme scale and throughput. You’ll learn how to manage crawl scheduling, deduplication, and failure recovery while respecting web standards.
Key deep dives include:
Enforcing robots.txt compliance and crawl politeness
Rate limiting with jitter to avoid overloading hosts
Handling DNS bottlenecks at massive scale
Designing fault-tolerant crawl pipelines
Storage and data flow optimization for LLM training datasets
We also compare system design interview expectations across Mid-level, Senior, and Staff engineers, helping you understand how much architectural depth and trade-off analysis interviewers expect at each level.
This is a must-watch for engineers preparing for LLM infrastructure, backend, or large-scale system design interviews.
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