← Cloudkitchens Interview Insights
Start by clarifying requirements and constraints, then propose a hash map-based design for O(1) operations. Walk through the implementation of each operation, discussing edge cases and potential optimizations.
Pro tip: Mention that in a real system, you'd consider concurrency and persistence, but for this in-memory version, focus on thread-safety if needed. Also, discuss how you'd handle large files (e.g., storing metadata vs. content).
Ask about expected file sizes, number of files, concurrency needs, and whether file content needs to be stored or just metadata.
Propose using a hash map (dictionary) to store file names as keys and file metadata (size, content pointer) as values, ensuring O(1) average time for add, get size, and delete.
Detail each operation: add checks for duplicates and inserts; get size returns the size or an error if not found; delete removes the entry and returns success/failure.
Discuss handling of null/empty names, very large files, and potential memory constraints. Mention error handling for non-existent files.
Briefly mention how to extend to support concurrency (locks), persistence (write-ahead log), or additional operations (list files).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and constraints, then propose a data model that associates files with users and supports efficient prefix/suffix filtering. Discuss indexing strategies (e.g., composite indexes or trie-based structures) to enable fast retrieval, and outline the query algorithm that filters, sorts, and limits results. Finally, analyze time/space complexity and trade-offs.
Pro tip: Mention that prefix and suffix filtering can be optimized by indexing on (user_id, name) for prefix and using a reversed name index for suffix, or by using a trie that supports both directions. Also, highlight that sorting by size descending with name tiebreaker can be done efficiently with a bounded priority queue (heap) of size N to avoid full sorting.
Ask about expected scale (number of users, files), read/write patterns, latency requirements, and whether prefix/suffix filters are mandatory or optional. Confirm if N is small or large, and if the query needs to be real-time.
Propose adding an owner_id field to the file metadata. Consider a table or document structure with fields: file_id, owner_id, name, size, and other metadata. Discuss normalization vs denormalization for query performance.
Suggest indexes to support efficient filtering: a composite index on (owner_id, name) for prefix queries, and a reversed name index or a separate suffix index for suffix queries. Alternatively, propose a trie or a combination of tries for prefix and suffix, or a suffix array.
Describe the steps: filter by owner_id, apply prefix and suffix conditions (using indexes), then retrieve matching files. Use a min-heap of size N to maintain top N by size descending and name ascending (lexicographic) as tiebreaker, avoiding full sort.
Discuss time complexity: index lookup O(log M + K) where K is number of matches, heap operations O(K log N). Space complexity: indexes add overhead. Mention trade-offs between index maintenance cost and query speed, and alternatives like full scan if filters are not selective.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
adjust_capacity and merge_users are the meaty parts.
Start by clarifying requirements and constraints, then design a data model that supports efficient capacity checks and file operations. Outline the core operations (user creation, file addition, capacity adjustment, user merge) and discuss algorithms and data structures to meet performance goals. Finally, address edge cases, concurrency, and scalability considerations.
Pro tip: Emphasize the importance of defining clear semantics for capacity limits and auto-deletion policies upfront, as ambiguous requirements can lead to costly rework. Also, consider using a priority queue or balanced tree to efficiently track largest files for deletion.
Ask questions to understand expected scale, consistency requirements, and exact semantics of capacity limits and auto-deletion. Confirm whether operations need to be atomic and how to handle concurrent requests.
Propose a schema for users and files, including capacity and current usage. Choose data structures (e.g., max-heap or balanced BST) to efficiently find and delete largest files when needed.
Explain step-by-step how to implement user creation, file addition with quota enforcement, capacity adjustment with auto-deletion, and user merge with capacity constraints. Discuss time/space complexity for each.
Identify potential edge cases (e.g., merging users with insufficient capacity, deleting files during merge) and propose solutions. Discuss locking or transactional strategies to maintain consistency.
Consider how the design scales with many users and files, and trade-offs between different data structures or consistency models. Mention possible optimizations like caching or sharding.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and constraints, then design a data model that tracks file metadata (size, compression state) and supports efficient operations. Implement backup/restore using snapshots or versioning, and handle compress/decompress with careful state management and capacity enforcement. Discuss trade-offs between time and space complexity, and consider edge cases like concurrent operations and failure recovery.
Pro tip: Demonstrate awareness of real-world constraints: compression may not always halve size exactly, so clarify assumptions and discuss how to handle non-ideal cases. Also, mention idempotency and atomicity for backup/restore to ensure data integrity.
Ask about file size limits, number of files, frequency of operations, and whether compression ratios are guaranteed. Clarify if backup/restore should be point-in-time or incremental, and how capacity enforcement interacts with decompression.
Propose a file system model with metadata (file ID, size, compression flag) and a backup store (e.g., versioned snapshots or a separate storage). Use a max-heap or balanced BST to efficiently find and delete largest files during capacity enforcement.
For compress: update file size to half and mark as compressed. For decompress: double size, mark as uncompressed, then if total size exceeds capacity, repeatedly delete largest files until within limit. For backup: create a snapshot of current state; for restore: revert to a snapshot.
Address scenarios like compressing an already compressed file, restoring after deletions, and concurrent operations. Discuss locking or transactional guarantees to maintain consistency.
Analyze time and space complexity for each operation (e.g., O(log n) for heap operations). Discuss trade-offs between storing full backups vs. incremental, and between eager vs. lazy capacity enforcement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.