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This looked manageable until I started thinking about URL normalization and what 'same domain' actually means when you have subdomains, redirects, trailing slashes.
Start by clarifying requirements and constraints, then outline a modular design with separate components for URL management, fetching, parsing, and output. Discuss concurrency using asyncio and aiohttp, and explain how you handle deduplication, domain restriction, and sorting. Finally, mention trade-offs and potential extensions.
Pro tip: Emphasize politeness and robustness: implement rate limiting and respect robots.txt to avoid overwhelming servers, and handle exceptions gracefully to ensure the crawler doesn't crash on malformed pages.
Ask about expected scale, politeness policies, and whether JavaScript rendering is needed. Confirm that only HTML pages are processed and that concurrency should be bounded.
Outline a modular design: a URL frontier (queue) with deduplication, a fetcher using asyncio and aiohttp with a semaphore for concurrency control, a parser using BeautifulSoup or lxml, and a CSV writer.
Describe how to normalize URLs (e.g., remove fragments, sort query params), enforce same-domain restriction, skip non-HTML via Content-Type header, and extract title and status code.
Use asyncio.Queue for BFS traversal, track depth per URL, and use asyncio.gather with a semaphore to limit concurrent requests. Ensure proper error handling and timeouts.
Collect results in a list, sort by title then URL, and write to CSV using the csv module. Discuss memory considerations for large crawls.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through politeness delays, robots.txt, retry logic with backoff, and handling circular redirects.
Start by clarifying the crawler's purpose and scale, then systematically cover edge cases across crawling, data handling, and system reliability. For production readiness, discuss architectural improvements like distributed crawling, robust storage, and monitoring, tying them to ML data pipeline needs.
Pro tip: Emphasize that production crawlers must handle politeness and legal compliance (robots.txt, rate limiting) and that data quality directly impacts ML model performance, so validation and deduplication are critical.
Ask about the crawler's target sites, scale, and data usage to tailor edge cases and production requirements. Confirm whether it's for ML training data, which implies needs for freshness, diversity, and labeling.
Enumerate edge cases in crawling (e.g., dynamic content, infinite loops, traps), data handling (e.g., duplicates, encoding issues), and system failures (e.g., network errors, crashes). Prioritize based on impact.
Suggest architectural changes: distributed crawling with queues, scalable storage (e.g., data lakes), and monitoring/alerting. Include ML-specific needs like data versioning and feature extraction pipelines.
Highlight trade-offs between speed, coverage, and politeness; and between data freshness and cost. Define success metrics like crawl rate, data quality, and model performance impact.
Recap key points and explicitly link how a production-ready crawler supports ML workflows, such as providing clean, timely data for training and inference.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.