Spent the first few minutes just making sure I understood the output format correctly because 'name(size)' is easy to get wrong under pressure.
First, clarify the requirements and constraints, such as whether the file system is a trie or hash map, and whether findFiles should be case-sensitive. Then, propose an efficient data structure (e.g., a trie for prefix search) and outline the algorithm: traverse the trie to the prefix node, collect all files in the subtree, filter by suffix, and sort the results by size descending and name ascending. Finally, discuss time and space complexity and potential optimizations like caching or indexing.
Pro tip: Mention that you would store file metadata (name, size) in the trie nodes and use a custom comparator for sorting to handle ties correctly. Also, consider edge cases like empty prefix/suffix and duplicate file names.
Ask about the file system implementation (e.g., trie, hash map), whether file names are unique, case sensitivity, and expected input sizes to determine the optimal approach.
If not already implemented, suggest using a trie for efficient prefix search, with each node storing a list of files (or file metadata) that end at that node. Alternatively, if the system uses a hash map, discuss iterating over all files.
Traverse the trie to the node representing the prefix, then perform DFS to collect all files in the subtree. Filter files by suffix, and sort the results by size descending and then name ascending.
Calculate time complexity: O(P + K + M log M) where P is prefix length, K is number of nodes in subtree, and M is number of matching files. Space complexity: O(M) for storing results.
Mention potential improvements like caching frequent queries, using a balanced BST for sorted results, or maintaining a separate index for suffixes if suffix filtering is common.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scale and access patterns (e.g., file count, query frequency, read/write ratio) to ground the discussion. Then compare linear scan, trie, and suffix index across time/space complexity, implementation complexity, and operational overhead. Conclude with a pragmatic recommendation that balances current needs with future scalability, possibly suggesting a hybrid or phased approach.
Pro tip: Emphasize that the right choice depends on the workload: linear scan is fine for small or infrequent queries, but for high-throughput prefix or substring searches, an index is worth the complexity. Also mention that you'd prototype and measure before committing to a complex solution.
Ask about the scale (number of files, average file size), query patterns (exact match, prefix, substring, regex), and performance SLAs (latency, throughput).
Discuss its simplicity and low overhead, but highlight O(n) per query and poor scalability for large datasets or frequent searches.
Explain that a trie excels for prefix searches with O(k) lookup (k = key length) but uses significant memory; a suffix index (e.g., suffix tree/array) supports substring queries but has higher build cost and complexity.
Weigh time/space complexity, implementation effort, maintenance, and update costs (e.g., dynamic inserts/deletes). Consider hybrid approaches like in-memory indexes with periodic rebuilds.
Propose a solution based on the clarified requirements, e.g., start with linear scan for MVP, then introduce a trie for prefix-heavy workloads or a suffix index for substring search if justified by metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.