The partial-word thing is where I fumbled a bit.
Start by clarifying requirements: case-insensitivity, whole-word matching, and handling of punctuation and multiple occurrences. Then propose a solution using tokenization or regular expressions with word boundaries, and discuss trade-offs between approaches. Finally, walk through an example and consider edge cases like tags with special characters.
Pro tip: Mention that using regex word boundaries (\b) can be tricky with tags containing non-word characters (e.g., 'c++'), so tokenizing the sentence into words and using a set for lookup is often more robust and efficient for large inputs.
Ask about case sensitivity, punctuation handling, tags with special characters, and whether the sentence can contain multiple occurrences of the same word. Confirm that 'whole-word' means the tag must match a complete word, not a substring.
Decide between tokenization (split sentence into words, normalize case, check membership) or regex with word boundaries. Discuss trade-offs: tokenization is simple and efficient for large tag lists; regex is concise but may fail with special characters.
If tokenizing: split the sentence on non-alphanumeric characters (or whitespace), convert to lowercase, and use a set for O(1) lookups. If using regex: build a pattern like \b(tag1|tag2|...)\b with case-insensitive flag, escaping special characters.
Walk through a sample sentence and tag list, verifying that partial matches are excluded and case is ignored. Test edge cases: tags with punctuation, multiple spaces, and empty inputs.
State time complexity: O(n + m) for tokenization where n is sentence length and m is number of tags, versus O(n * m) for naive substring checks. Mention space complexity and potential optimizations like using a trie for very large tag sets.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and scope, then model the file system as a tree of nodes with separate directory and file types. Implement core operations (mkdir, ls, write, read) using a map-based structure for O(1) child lookup, and discuss trade-offs like path resolution and concurrency.
Pro tip: Mention that you'd use a trie-like tree with hash maps for children to achieve O(1) lookups, and proactively discuss edge cases like path normalization and concurrent access to show production-level thinking.
Ask about expected operations, path format (absolute vs relative), file size limits, and concurrency needs. Confirm whether persistence or permissions are required.
Define a Node base class with name, type, and metadata; DirectoryNode holds a map of children; FileNode holds content and size. Use a tree structure rooted at '/'.
Write methods for mkdir (create directory), ls (list children), write (set file content), and read (get file content). Include path resolution that splits on '/' and traverses the tree.
Address duplicate names, invalid paths, writing to directories, reading non-existent files, and path normalization (e.g., '..', '.'). Return appropriate errors.
Talk about time/space complexity, potential concurrency control (locks per node), and alternatives like inode-based designs or using a flat map with full paths.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.