Start by clarifying requirements (file size limits, concurrency, persistence) and then design a simple in-memory key-value store mapping file IDs to metadata (size, content pointer). Discuss trade-offs of different storage backends (in-memory vs. disk) and how to handle operations efficiently.
Pro tip: Mention that you'd use a hash map for O(1) operations and discuss how you'd extend it to a distributed system with sharding and replication, showing you think beyond the basics.
Ask about expected scale, file size limits, concurrency needs, and persistence requirements to scope the design appropriately.
Propose a File object with id, size, and storage location, and a mapping from file ID to File object.
Implement add_file (store metadata and content), get_file_size (return size from metadata), and delete_file (remove metadata and content).
Discuss how to handle large files, concurrency (locking), and persistence (write to disk or use a database).
Compare in-memory vs. disk-based storage, and mention potential extensions like versioning, deduplication, or distributed storage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The sorting criteria is the thing to nail down explicitly before coding.
Clarify requirements first: whether the storage system is in-memory or disk-based, expected N, prefix filtering semantics, and update frequency. Then propose a data structure that supports efficient prefix filtering and top-N retrieval, such as a trie augmented with heaps or a balanced BST keyed by (name, size), and analyze trade-offs between query time, update time, and memory.
Pro tip: Mention that you would first check if the system already maintains a sorted index or if you need to build one; also discuss how to handle ties and whether the prefix filter should be case-sensitive, showing attention to edge cases.
Ask about data size, update frequency, expected N, prefix matching rules (case sensitivity, exact prefix vs. substring), and whether the result must be exact or approximate.
Propose a trie for prefix filtering combined with a max-heap or sorted list per node, or a balanced BST keyed by (name, size) to support range queries and ordered retrieval.
Outline steps: traverse trie to prefix node, collect all files under that subtree, then use a min-heap of size N to find the N largest by size, with tie-breaking by name ascending.
Discuss time complexity for query (O(P + K log N) where P is prefix length, K is number of matching files) and update (O(L) for trie insertion), and compare with alternatives like scanning all files.
Handle empty prefix, N larger than matching files, ties in size, and concurrent updates; suggest caching frequent queries or maintaining a global sorted structure if updates are infrequent.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The back-pointer thing is what trips people up.
Start by clarifying requirements and constraints, then design a data model that efficiently supports user capacity limits and file ownership. Implement the operations with careful attention to edge cases like capacity overflow and merging users with existing files, and discuss trade-offs between different data structures and algorithms.
Pro tip: Demonstrate awareness of real-world concerns by mentioning concurrency control and transactional integrity, especially for merge_user which must atomically combine users and transfer files without data loss.
Ask about expected scale, whether users can have zero capacity, if files have sizes, and if merge_user should handle cases where combined files exceed new capacity. Confirm if operations need to be thread-safe.
Propose a User class with capacity and used space, and a File class with size and owner reference. Consider using a hash map for user lookup and a list or set for files per user.
For add_user, initialize capacity and zero used space. For add_file_by, check if user exists, if file size plus used space exceeds capacity, and if so, fail; otherwise, add file and update used space.
Combine two users by summing capacities and transferring all files from one to the other. Update used space accordingly. Decide which user to keep (e.g., the one with larger capacity or by ID) and remove the other.
Talk about time complexity, potential for fragmentation, and how to handle failures during merge. Mention alternative designs like using a database with transactions.
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, then propose a design that handles conflicts by either renaming or rejecting the restore. Discuss trade-offs between consistency, user experience, and implementation complexity, and suggest a preferred approach with justification.
Pro tip: Show awareness that this is a classic conflict resolution problem; mention that the best solution depends on product priorities (e.g., data integrity vs. user convenience) and that you'd validate assumptions with stakeholders.
Ask questions to understand the system: Are filenames globally unique? Can users share filenames? What are the consistency requirements? Is the restore atomic? This ensures you address the right problem.
Enumerate cases: the restoring user's backup contains a filename that now belongs to another user. Consider if the other user's file is active or also backed up, and whether the conflict is detected at restore time.
Present options: (a) reject the restore and notify the user, (b) rename the conflicting file (e.g., append a suffix), (c) merge or skip the file, or (d) allow overwrite with warning. Discuss pros and cons of each.
Choose a strategy based on likely product goals. For example, prioritize data integrity by rejecting the restore, or prioritize user convenience by renaming. Explain how it handles edge cases and maintains system consistency.
Outline how to implement the chosen approach: e.g., use a transaction to check for conflicts and apply changes atomically. Mention indexing, locking, or versioning to handle concurrency at scale.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.