Got both solutions down, which felt fine in the moment.
Start by clarifying requirements (scale, politeness, deduplication) and then present a unified crawler architecture with a pluggable frontier that can operate as either a stack (DFS) or queue (BFS). Explain the trade-offs between DFS and BFS for crawling, including memory, completeness, and politeness, and tie the choice to ML data collection needs like coverage and freshness.
Pro tip: Emphasize politeness and distributed crawling early—Google cares about not hammering servers and about scaling to billions of pages; mentioning robots.txt, rate limiting, and sharding shows production maturity.
Ask about scale, politeness, deduplication, and whether the crawler is for training data or indexing. This shapes the choice of DFS vs BFS and the need for distributed processing.
Outline components: URL frontier, fetcher, parser, deduplication (Bloom filter), and storage. Highlight that DFS and BFS differ only in the frontier's data structure (stack vs queue).
Describe DFS using a stack (LIFO) and BFS using a queue (FIFO), with pseudocode. Mention iterative implementations to avoid recursion limits and handling of cycles via visited set.
Compare DFS and BFS on memory usage, completeness, politeness, and suitability for ML data collection. For example, BFS gives breadth and freshness, DFS may go deep but risk missing important pages.
Cover distributed crawling with multiple workers, sharding the frontier, rate limiting per domain, robots.txt compliance, and handling dynamic content. Relate to ML needs like data quality and coverage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by explaining Python's instance behavior: instance attributes are stored in a per-instance dictionary, and method lookup follows the MRO. Then connect this to recursive solutions, discussing how recursion depth, state management, and performance are affected by Python's object model.
Pro tip: Mention that Python's default recursion limit and lack of tail-call optimization can cause stack overflows, so for deep recursion, consider iterative approaches or increasing the recursion limit with caution.
Describe how instance attributes are stored in __dict__ and how methods are bound to instances. Mention the role of __init__ and self.
Explain how Python resolves method calls in inheritance hierarchies, which is crucial for recursive methods that may be overridden.
Connect instance behavior to recursion: each recursive call may create new instances or modify instance state, affecting memory and correctness.
Discuss recursion depth limits, stack memory usage, and potential optimizations like memoization or iterative conversion.
Tie the discussion to ML scenarios, such as recursive algorithms in tree-based models or graph neural networks, highlighting trade-offs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Knew there was a default recursion limit, said something like 'you can increase it with sys.setrecursionlimit' and left it there.
Start by explaining Python's default recursion limit (typically 1000) and how it's enforced by the interpreter's call stack. Then discuss how a DFS-based crawler using recursion can hit this limit on deep websites, and propose iterative alternatives or increasing the limit with caution. Finally, relate this to system design considerations for scalable crawling.
Pro tip: Mention that while you can increase the recursion limit with sys.setrecursionlimit(), it risks a C stack overflow and is not a scalable solution; instead, emphasize iterative DFS with an explicit stack for production systems.
Explain that Python's default recursion limit is 1000, set by sys.getrecursionlimit(), and that exceeding it raises a RecursionError. Mention that it can be adjusted but with risks.
Describe how each recursive call adds a frame to the call stack, consuming memory. Deep recursion can lead to stack overflow or excessive memory usage, especially in a crawler traversing many pages.
Discuss how a recursive DFS crawler can easily hit the recursion limit on sites with deep link hierarchies (e.g., >1000 levels). This would cause the crawler to crash or require error handling.
Suggest converting the recursive DFS to an iterative one using an explicit stack (e.g., list or deque). Alternatively, mention increasing the recursion limit as a temporary fix but highlight its dangers.
Tie this to broader system design: for a production crawler at Google scale, iterative approaches are preferred for scalability, and other factors like distributed crawling and memory management should be considered.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the role of hashing in a web crawler, such as URL deduplication, content fingerprinting, and caching. Then systematically discuss potential hash-related bugs, including collisions, inconsistent hashing, and hash function weaknesses, and how they could impact crawler behavior. Finally, relate these bugs to broader system reliability and ML data quality issues.
Pro tip: Emphasize that hash collisions in URL deduplication can lead to missed pages, which directly affects training data completeness and model performance. Mentioning real-world examples like the Google crawler's scale shows you understand production challenges.
List where hashing is used: URL deduplication, content checksums, bloom filters for visited links, and caching. This sets the context for potential bugs.
Discuss how hash collisions can cause false positives in deduplication (skipping unique URLs) or false negatives (crawling duplicates), leading to incomplete or redundant data.
Consider bugs from weak hash functions (e.g., MD5 collisions), inconsistent hashing across distributed nodes, or improper handling of hash outputs (e.g., truncation).
Explain consequences: reduced crawl coverage, biased training data, increased storage costs, and potential model degradation due to missing or duplicated content.
Suggest solutions like using cryptographic hashes (SHA-256), consistent hashing, collision-resistant data structures, and monitoring hash distribution.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.