I went straight to BFS with a queue and a visited set, which was the right call.
Model the problem as a graph traversal where each URL is a node and hyperlinks are directed edges, then apply BFS starting from the seed URL using a queue. Use a visited set to prevent revisiting URLs and filter discovered links to only include those matching the starting hostname. Clearly communicate your data structures and walk through the algorithm before coding.
Pro tip: Proactively mention real-world concerns like handling relative vs. absolute URLs, URL normalization (trailing slashes, query params), and the fact that the link-fetching function likely involves I/O — signaling awareness of concurrency and rate-limiting opportunities that matter in production crawlers like those at Nooks.
Ask about edge cases: should query parameters be treated as distinct URLs, how should relative URLs be handled, and is the provided function synchronous or asynchronous? Confirm that 'same hostname' means exact host match (e.g., excluding subdomains).
Choose a queue (deque) for BFS traversal and a hash set for tracking visited URLs. Explain that the set gives O(1) lookup to ensure each URL is visited at most once.
Initialize the queue and visited set with the start URL, then loop: dequeue a URL, call the provided link function, filter results to the same hostname and unvisited URLs, add them to the visited set, and enqueue them.
Parse each discovered URL to extract its hostname (using urllib.parse or equivalent) and compare it to the start URL's hostname. Normalize URLs by stripping trailing slashes or fragments to avoid duplicate visits.
State that time complexity is O(V + E) where V is reachable pages and E is total hyperlinks, and space is O(V) for the visited set. Then discuss how this could be extended with concurrent workers, a distributed queue, or politeness delays for a production crawler.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Answered with a visited set, nothing fancy.
Start by explaining the need for a visited set to track URLs and avoid duplicates. Then describe the data structure choices (e.g., hash set, Bloom filter) and how they integrate with the crawler's frontier. Finally, discuss trade-offs and scalability considerations.
Pro tip: Mention that using a Bloom filter can save memory but may introduce false positives, so you might combine it with a hash set for exact tracking of important URLs. Also, consider normalization of URLs to avoid duplicates that differ only by query parameters or fragments.
Explain that without deduplication, the crawler may waste resources and overload servers by revisiting the same URL. This can lead to infinite loops and inefficient crawling.
Discuss using a hash set for exact tracking, which offers O(1) lookups but uses more memory. For large-scale crawls, consider a Bloom filter for probabilistic checking with lower memory footprint.
Describe how to check the visited set before adding a URL to the frontier. If the URL is already visited, skip it; otherwise, add it to the set and enqueue it.
Mention that URLs should be normalized (e.g., removing fragments, sorting query parameters, lowercasing host) to ensure that semantically identical URLs are treated as the same.
For distributed crawlers, discuss using a distributed cache like Redis or a database to share the visited set across nodes. Also, consider persistence for resuming crawls.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked for a moment on how to frame the variables cleanly.
Start by clearly defining the parameters of your crawler solution, such as the number of pages (N), average links per page (L), and the data structures used. Then, break down the time and space complexity for each major component (e.g., fetching, parsing, storing) and express them in Big-O notation. Finally, discuss any trade-offs or optimizations that affect these complexities.
Pro tip: Always relate the complexity to real-world constraints like network latency, memory limits, and concurrency, and mention how you might optimize for the common case. This shows you understand that theoretical complexity must be balanced with practical engineering.
Clearly state the input size variables (e.g., N = number of pages, L = average links per page) and any assumptions about the crawler's behavior (e.g., single-threaded, BFS order).
Break down the time complexity for each phase: fetching (network I/O), parsing (HTML processing), and URL management (deduplication, queue operations). Sum them up and simplify to Big-O.
Consider the space needed for the frontier queue, visited set, and any in-memory storage of page content. Express as a function of N and L.
Mention how concurrency, distributed crawling, or using external storage (e.g., databases) can change the complexity, and the trade-offs involved.
Provide a concise summary of the overall time and space complexity, and reiterate any key assumptions or optimizations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.