Stack-based approach, pretty standard if you've seen it before.
Use a stack to process each component of the path, handling '.', '..', and empty segments appropriately. Then reconstruct the canonical path from the stack, ensuring it starts with a single slash and has no trailing slash (except for root).
Pro tip: Clarify edge cases upfront (e.g., multiple slashes, trailing slash, root path) and discuss time/space complexity. Mention that the solution should run in O(n) time and O(n) space, and that using a stack is optimal.
Restate the problem in your own words and list edge cases: empty path, root '/', multiple consecutive slashes, '.' and '..' components, trailing slash, and paths that go above root.
Decide to split the path by '/' and use a stack (or list) to keep track of valid directory names. Explain why a stack is suitable for handling '..' (pop) and '.' (ignore).
Iterate over the split components: skip empty strings and '.', pop from stack for '..' if stack is not empty, otherwise push the directory name onto the stack.
Join the stack elements with '/' and prepend a '/' to form the absolute path. If the stack is empty, return '/'.
State that time complexity is O(n) where n is the length of the path, and space complexity is O(n) for the stack. Walk through a few test cases to verify correctness.
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This is where it got interesting and also where I started to sweat a little.
Start by clarifying requirements: what defines a 'normalized path' and how to handle edge cases. Then outline a streaming algorithm using a hash map to count occurrences, applying normalization rules consistently. Discuss trade-offs of different normalization choices and how they affect correctness and performance.
Pro tip: Mention that normalization should be idempotent and that you'd use a well-tested library for percent-decoding to avoid security issues like path traversal. Also, consider using a trie or count-min sketch for memory efficiency if the cardinality is high.
Ask whether normalization should be case-insensitive, how to treat trailing slashes, and whether to decode percent-encoded segments. Confirm if the path includes query parameters or fragments.
Specify rules: e.g., lowercase the path, remove trailing slash (except root), decode percent-encoded characters, and resolve dot segments. Ensure rules are applied consistently.
Use a hash map to count occurrences: for each URL, extract the path, normalize it, and increment its count. Discuss memory considerations and potential need for approximate counting.
Discuss trade-offs: case sensitivity may be server-dependent; trailing slash removal can merge distinct resources; decoding may introduce ambiguity. For large scale, consider distributed counting or sketches.
Recap the approach, emphasizing clarity on normalization and efficiency. Mention testing with edge cases like encoded slashes (%2F) and empty paths.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First clarify the current design's assumptions about paths (e.g., absolute, canonical) and then explain how to extend it to handle relative paths and symlinks by introducing resolution logic. Discuss the complexity implications in terms of time (e.g., path resolution, cycle detection) and space (e.g., caching, recursion depth).
Pro tip: Mention real-world examples like how Unix resolves symlinks with a maximum depth to prevent infinite loops, and how caching resolved paths can mitigate repeated resolution costs.
State the current design's handling of paths (e.g., assumes absolute, canonical paths) and define what relative paths and symlinks mean in this context.
Outline an algorithm to resolve relative paths (e.g., using current working directory) and symlinks (e.g., following links with cycle detection).
Break down time and space complexity: resolution time proportional to path depth and symlink chain length; space for caching or recursion stack.
Propose caching resolved paths, limiting symlink depth, or using iterative resolution to avoid stack overflow, and discuss trade-offs.
Summarize how these extensions affect overall system performance, scalability, and correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.