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Start by clarifying requirements and constraints, then design a simple data structure like a hash map to store files with their metadata. Implement the operations with attention to edge cases and discuss potential optimizations for scalability and concurrency.
Pro tip: Mention that you would use a hash map for O(1) average-time operations, but also discuss how you would handle collisions and resizing to demonstrate depth. Additionally, proactively bring up concurrency control (e.g., locks) since it's an in-memory system that could be accessed by multiple threads.
Ask questions to understand expected file sizes, number of files, concurrency needs, and whether operations should be thread-safe. Confirm the scope: only add, copy, and get size for now.
Propose using a hash map (dictionary) to map file names to file objects containing content and size. Discuss trade-offs of alternative structures like trees or tries for prefix searches.
Write pseudocode or actual code for addFile, copyFile, and getFileSize. Handle edge cases: duplicate file names, non-existent files, and copying to an existing name.
Discuss thread-safety using locks or concurrent data structures. Mention potential bottlenecks and how to scale (e.g., sharding, consistent hashing) if the system grows.
Outline test cases for correctness and performance. Suggest optimizations like lazy copying (copy-on-write) or storing file content externally if memory is limited.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Sorting by two criteria tripped me up for a second.
Clarify the interface and data characteristics, then propose an efficient data structure (e.g., a trie for prefix search combined with a suffix index) and a sorting strategy. Walk through the algorithm, analyze time/space complexity, and discuss trade-offs and edge cases.
Pro tip: Mention that you would first check if the storage system already supports indexing or if you need to build one, and consider whether the search should be case-sensitive or handle Unicode, as these details often matter in production systems.
Ask about the expected data volume, update frequency, whether prefix/suffix filters are independent or combined, and the desired output format. Confirm if the search is case-sensitive and if the file list is static or dynamic.
Propose a trie for efficient prefix matching and a reversed trie or suffix array for suffix matching. Alternatively, suggest a combined index or filtering approach if the dataset is small.
Outline how to traverse the trie(s) to collect matching file names, then sort the results by size descending and lexicographically ascending for ties. Use a custom comparator.
Discuss time and space complexity of the chosen approach, compare with naive filtering, and mention potential optimizations like caching or parallel processing.
Address empty results, ties in size, special characters, and ensure output is formatted as name(size). Mention testing and validation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and constraints of the user and capacity system, then propose a data model that integrates users with the existing storage system. Walk through the key operations (adding files on behalf of users, updating capacity with automatic file removal, and enforcing ownership/capacity in copy_file) while discussing trade-offs and edge cases.
Pro tip: Emphasize idempotency and atomicity in capacity updates and file removals to avoid race conditions and data inconsistencies. Also, consider how to handle partial failures gracefully, such as when a file removal fails during capacity update.
Ask questions to understand the expected scale, consistency requirements, and how users interact with the system. Clarify what 'automatic file removal' means (e.g., oldest files first, or based on some priority) and whether capacity is per-user or global.
Propose a schema that associates files with owners and tracks each user's capacity and current usage. Consider adding fields like owner_id to files and capacity, used_capacity to users, and possibly a separate table for user-file relationships.
Describe how to add files on behalf of users, ensuring ownership is set and capacity is checked. For updating capacity, outline a transaction that adjusts the limit and removes files if necessary, possibly in a loop until usage is within the new limit.
Modify copy_file to verify that the source file is owned by the requesting user and that the destination user (if different) has enough capacity. Handle cases where the copy would exceed capacity, possibly by rejecting or triggering automatic removal.
Address potential issues like concurrency (using locks or transactions), performance impact of file removal, and how to handle failures during multi-step operations. Mention alternatives like soft deletes or asynchronous cleanup.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Floor division for compress size is a small thing but you have to get it right.
Clarify the storage system's existing API and constraints, then design compress and decompress as atomic operations that update file metadata and enforce capacity checks. Use a hash map to track file sizes and names, and simulate the operations with careful handling of edge cases like insufficient space or missing files.
Pro tip: Emphasize atomicity and consistency: ensure that if compression fails due to capacity, the original file remains unchanged. Also, discuss how you would handle concurrent operations to avoid race conditions.
Ask about the storage system's API, file representation, and capacity constraints. Confirm that compression halves the size with floor division and renames the file, and decompression doubles the size and removes the suffix.
Choose a data structure to store files and their sizes, such as a hash map mapping filenames to sizes. Consider how to efficiently check total used capacity and update it.
For a given file, compute new size as floor(original size / 2). Check if renaming to .COMPRESSED is valid (e.g., no name conflict). Update metadata and total capacity, ensuring atomicity.
For a .COMPRESSED file, compute new size as original size * 2. Check if there is enough free capacity to accommodate the increase. If yes, rename back and update metadata; else, return an error.
Consider missing files, already compressed/decompressed files, insufficient capacity, and concurrent access. Discuss error handling and potential optimizations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.