The sorting requirement is where I slipped up initially.
Start by clarifying requirements and constraints, then propose a data structure that supports efficient add, get, delete, and sorted listing. Implement the FileSystem class with a hash map for O(1) file operations and maintain a sorted structure (e.g., balanced BST or sorted list) for listing, discussing trade-offs.
Pro tip: Mention that you would use a hash map for O(1) file operations and a balanced BST (like a TreeMap) to keep files sorted by size descending and name ascending, ensuring O(log n) insertions and deletions. This shows you understand the need for both fast lookups and efficient sorted retrieval.
Ask about expected file sizes, number of files, concurrency needs, and whether file names are unique. Confirm the sorting order and any additional operations.
Propose using a hash map to store file names and sizes for O(1) add, get, and delete. For sorted listing, suggest a balanced BST (e.g., TreeMap) keyed by a composite of size (descending) and name (ascending).
Write methods for addFile(name, size), getSize(name), deleteFile(name), and listFiles(). Ensure each operation updates both the hash map and the sorted structure consistently.
State time and space complexity: O(1) for add, get, delete (amortized), O(log n) for insertion/deletion in sorted structure, and O(n) for listing. Space O(n).
Mention alternative approaches (e.g., sorted list with binary search, heap) and their trade-offs. Discuss handling concurrency, persistence, or additional operations like renaming.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty mechanical once the file dict was in place.
Clarify the existing FileSystem design and the expected search semantics (e.g., case sensitivity, prefix/suffix matching). Then propose a data structure like a trie for prefix search and a reverse trie or suffix array for suffix search, or a combined index, and discuss time/space trade-offs. Finally, outline the implementation and test with edge cases.
Pro tip: Mention that you would first check if the FileSystem already has an index or if you need to build one, and consider whether the search should be case-sensitive or support wildcards. Also, discuss how to handle updates (file additions/deletions) efficiently.
Ask about the expected scale (number of files, query frequency), whether prefix and suffix are matched independently or combined, and if case sensitivity matters.
Propose a trie for prefix search and a reverse trie for suffix search, or a single trie storing both forward and reversed strings. Discuss alternatives like suffix arrays or hash maps for specific cases.
Outline how to traverse the trie(s) to find all files matching the prefix and suffix, possibly intersecting the results if both are required.
State time complexity for insertion and search (e.g., O(L) for trie operations) and space complexity, and compare with naive approaches.
Discuss empty prefix/suffix, no matches, dynamic updates (insert/delete), and potential optimizations like caching frequent queries.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where things got genuinely tricky.
Start by clarifying requirements and constraints, then design a data model that tracks users, their storage capacity, and their files. Propose an efficient eviction strategy using a max-heap or balanced tree to identify the largest files, and discuss trade-offs between time and space complexity.
Pro tip: Mention that eviction should be triggered only when adding a file would exceed capacity, and consider whether to evict before or after adding the new file to avoid unnecessary evictions.
Ask about expected scale, file size distribution, concurrency needs, and whether eviction should be immediate or batched. Confirm if users can have zero capacity or if files can be shared.
Define classes for User (with capacity, used space, and a collection of files) and File (with size and metadata). Choose data structures that support efficient insertion, deletion, and retrieval of largest files.
When a user adds a file that would exceed capacity, repeatedly remove the largest file(s) until there is enough space. Use a max-heap or balanced BST to track file sizes for O(log n) operations.
Compare using a heap (fast eviction but slower deletion of arbitrary files) versus a balanced tree (supports both efficiently). Discuss time/space complexity and potential optimizations like lazy deletion.
Consider edge cases: file larger than capacity, multiple evictions, concurrent access, and persistence. Outline a testing strategy to validate correctness and performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: what does 'copyFile' mean in this context (OS-level operation, API design, or system design)? Then outline a design that handles file duplication, size preservation, and owner preservation, discussing trade-offs such as permissions, atomicity, and performance. Finally, walk through the implementation steps and potential edge cases.
Pro tip: Demonstrate awareness of security and operational constraints: preserving ownership often requires elevated privileges, and copying large files can impact system performance, so consider asynchronous operations or chunking.
Ask questions to understand the context: Is this a system call, a library function, or part of a larger system? What are the constraints (e.g., cross-platform, permissions, atomicity)?
Outline the high-level steps: open source file, create destination file, copy data, set metadata (size and owner). Consider using system calls like copy_file_range or sendfile for efficiency.
Explain how to preserve the original owner: retrieve the source file's UID/GID and apply them to the destination using chown. Note that this typically requires root privileges or CAP_CHOWN capability.
Ensure the copied file has the same size by copying all bytes and verifying with stat. Discuss potential issues like sparse files and how to handle them.
Cover trade-offs: performance vs. atomicity, error handling (e.g., disk full, permission denied), and concurrency (e.g., source file modified during copy). Mention alternatives like reflink if supported.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Compression was the most interesting piece.
Start by clarifying the requirements and constraints, such as file size limits, compression algorithms, and capacity tracking granularity. Then design a stateful system that tracks available capacity and enforces it during decompression, while handling compression and decompression operations atomically. Finally, discuss trade-offs around consistency, concurrency, and failure recovery.
Pro tip: Emphasize idempotency and atomicity: ensure that decompression either fully succeeds or rolls back without consuming capacity, and that repeated operations don't corrupt state. This shows you think about real-world reliability.
Ask about file size limits, compression algorithms, capacity units, and whether operations are synchronous or asynchronous. Confirm if capacity is per-user or global and how it's updated.
Define a data model to track user capacity, file metadata, and operation status. Consider using a database with transactions or an in-memory store with persistence for state.
Design compressFile to compress data, update metadata, and adjust capacity if needed (e.g., compressed size counts toward usage). Ensure atomic updates to avoid race conditions.
Design decompressFile to first check if the decompressed size fits within available capacity. If not, reject the operation; if yes, decompress and update capacity atomically.
Discuss concurrency control (locks, optimistic concurrency), failure recovery (rollback on error), and scalability (sharding, caching). Mention how to handle partial failures and idempotency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.