The base setup felt straightforward, a couple of hashmaps and some bookkeeping.
Start by clarifying requirements and constraints, then design a data model with a global file registry and per-user storage tracking. Implement the operations with careful handling of edge cases like duplicate files, capacity limits, and ownership validation. Discuss trade-offs and potential optimizations.
Pro tip: Emphasize the importance of defining clear semantics for each operation, especially around error conditions and capacity enforcement, as this demonstrates attention to detail and robustness. Also, mention how you would test the system to ensure correctness.
Ask questions to understand constraints: Are file names globally unique or per-user? What happens when a user exceeds capacity? Should operations be atomic? What are the expected sizes and performance requirements?
Propose a data model: a global map from file names to file metadata (size, owner), and a per-user map from file names to file objects, along with a per-user total size counter. Consider using hash maps for O(1) lookups.
Implement add_user (initialize user with capacity), add_file_by (add file with specified owner, checking capacity and uniqueness), and add_file (add file for the current user, similar checks). Handle edge cases like duplicate files, insufficient capacity, and non-existent users.
Talk about time/space complexity, potential concurrency issues, and possible optimizations like lazy deletion or sharding. Mention how to extend the design for features like file deletion or renaming.
Outline test cases: adding files within capacity, exceeding capacity, duplicate file names, adding files for non-existent users, and concurrent operations. Explain how you would verify correctness and performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Tripped up briefly because I forgot the copy inherits the original owner, not some default.
Start by clarifying the requirements and assumptions, such as the storage model and quota enforcement. Then design the copy_file operation with atomicity and consistency in mind, ensuring that quota checks are performed before the copy. Finally, discuss trade-offs and potential edge cases.
Pro tip: Emphasize idempotency and failure handling: if the copy fails midway, the system should not leave partial files or incorrectly deduct quota. Also, consider concurrency: multiple simultaneous copies could race on quota checks, so you need a mechanism like transactions or locks.
Ask about the storage system (e.g., distributed file system, database), how quotas are tracked, and whether the operation must be atomic. Confirm that the new file should have the same owner and size as the original.
Define how files and quotas are represented. The quota check should verify that the owner's current usage plus the file size does not exceed their quota limit.
Outline the steps: validate source exists, check quota, create new file entry, copy data, and update quota usage. Use transactions or two-phase commit to ensure atomicity across these steps.
Discuss locking or optimistic concurrency control to prevent race conditions on quota. Describe rollback or cleanup if any step fails, ensuring no partial state.
Mention trade-offs like performance vs. consistency, and possible optimizations such as lazy copying or deduplication. Also consider how this integrates with existing APIs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the input format and constraints, then propose an efficient solution using a min-heap of size N to track the largest files while filtering by prefix and suffix. Handle ties by comparing names in ascending order, and format the output as 'name(size)'.
Pro tip: Mention that you would use a min-heap to achieve O(M log N) time where M is the number of matching files, which is optimal for large datasets, and discuss edge cases like N larger than the number of matches or empty results.
Ask about input format (e.g., list of files, directory path), expected output format, and constraints like file count, N size, and memory limits.
Iterate through files and check if the name starts with the prefix and ends with the suffix. Consider case sensitivity and whether prefix/suffix can overlap.
Use a min-heap of size N to keep the largest N files by size. For ties, compare names in ascending order (i.e., smaller name is 'larger' in priority).
Extract elements from the heap, sort them in descending order by size (and ascending by name for ties), then format each as 'name(size)'.
Discuss time and space complexity, and handle edge cases such as N=0, no matching files, or N greater than the number of matches.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one was the most interesting to think through.
Clarify the requirements and constraints first, then design a solution that efficiently finds and deletes the largest files until usage fits. Discuss data structures and algorithms, considering time and space complexity, and handle edge cases. Also, mention trade-offs and potential optimizations.
Pro tip: Demonstrate awareness of real-world system constraints: deleting files is I/O intensive, so batch deletions and consider asynchronous processing. Also, discuss how to maintain a data structure for quick access to largest files, like a max-heap or balanced BST.
Ask about the file system model, whether files have sizes only, if deletions are permanent, and if there are constraints on time/memory. Confirm the goal: reduce usage to <= new cap by deleting largest files.
Select a data structure to efficiently retrieve the largest file repeatedly, such as a max-heap or a balanced binary search tree. Consider if files are static or dynamic.
Outline steps: compute excess = current_usage - new_cap; while excess > 0, remove largest file, subtract its size from usage and excess. Ensure termination and correctness.
Discuss time complexity: building heap O(n), each deletion O(log n), total O(k log n) where k is number of deletions. Space complexity O(n). Compare with sorting approach O(n log n).
Consider cases: new cap >= current usage (no deletion), no files to delete but still over cap (error), very large number of files. Discuss trade-offs: heap vs sorting, in-memory vs external, concurrency.
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, especially around file ownership, capacity limits, and failure modes. Then design the data model and operations, focusing on atomicity, consistency, and trade-offs between simplicity and performance. Finally, discuss how to handle edge cases and ensure the system remains robust under concurrent operations.
Pro tip: Emphasize idempotency and failure recovery: if decompression fails due to capacity, the file should remain compressed and unchanged. This shows you think about real-world reliability, not just happy paths.
Ask questions to understand the file system model: Is it hierarchical? How is ownership determined? What is the capacity limit per owner? Are operations atomic? This ensures you address the right problem.
Specify how files, owners, and capacities are represented. Key invariants: total size per owner ≤ capacity, compressed files have a suffix and half size, decompressed files have original name and double size.
Detail compress_file: check if file exists and is not already compressed, halve size, rename with suffix. For decompress_file: check if file is compressed, ensure owner has capacity for doubled size, then double size and restore name. Handle errors gracefully.
Discuss how to handle concurrent operations: use locks or transactions to prevent race conditions, ensure that capacity checks and updates are atomic, and consider rollback on failure.
Talk about trade-offs: e.g., simplicity vs. performance, strict capacity enforcement vs. soft limits. Mention possible extensions like compression levels, different suffixes, or hierarchical capacity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.