Level 1 felt straightforward, just a hash map from filename to size.
Start by clarifying requirements and constraints, then design a simple key-value store mapping file names to sizes, using a hash map for O(1) operations. Implement the operations with proper validation and error handling, and discuss potential extensions like concurrency and scalability.
Pro tip: Demonstrate production thinking by discussing how to handle concurrent uploads and copies, and how to extend the design to a distributed system with sharding and replication.
Ask about expected scale, consistency needs, and whether the system should be distributed. Confirm the exact behavior for edge cases like duplicate names and invalid sources.
Define a File class with name and size, and a CloudFileSystem class with methods: upload(name, size), getSize(name), copy(source, target). Use a hash map to store files by name.
For upload, check if name exists; if so, reject. For getSize, return size or error if not found. For copy, validate source exists and target does not, then create a new file with the same size.
All operations are O(1) time and O(n) space. Discuss edge cases: empty names, negative sizes, concurrent modifications, and error handling strategies.
Talk about how to scale to distributed storage: sharding by file name, replication for fault tolerance, and consistency models. Mention potential features like file deletion, listing, and metadata.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where things got more interesting.
Start by clarifying requirements and constraints, then design a data model that associates users with storage quotas and tracks usage. Extend the file system operations to enforce quota checks atomically, and discuss trade-offs around consistency, concurrency, and scalability.
Pro tip: Emphasize the need for atomic quota enforcement to prevent race conditions, and discuss how to handle partial failures in distributed operations.
Ask questions to understand scope: Is this a single-node or distributed file system? What are the consistency and availability requirements? How is user capacity measured (bytes, files)? Are there admin overrides?
Define entities: User (with capacity, used space), File/Directory (with owner, size), and operations (UPLOAD, COPY). Consider how to track usage efficiently and support hierarchical quotas if needed.
Modify UPLOAD and COPY to first check if the operation would exceed the user's remaining capacity. Ensure checks are atomic with the operation to avoid race conditions, using transactions or locks.
Discuss how to handle concurrent operations from the same user: use optimistic locking, distributed locks, or atomic counters. Consider trade-offs between strong consistency and performance.
Talk about scaling quota tracking (e.g., sharding by user), caching usage data, and handling failures (e.g., rollback on partial upload). Compare centralized vs. distributed quota enforcement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Snapshotting sounds easy until you realize restoring means you have to diff what was there before vs now and adjust usage accordingly.
Start by clarifying the requirements: what constitutes a snapshot, how often they occur, and the expected scale. Then design a data structure that supports efficient snapshot creation and restoration, focusing on how to manage capacity (e.g., reference counting, copy-on-write, or versioning). Finally, discuss trade-offs between time and space efficiency, and how to handle concurrent operations.
Pro tip: Emphasize that snapshots should be immutable and restoration should be atomic; this shows you understand the importance of consistency and fault tolerance in real systems.
Ask about snapshot frequency, size of data, concurrency needs, and whether snapshots are incremental or full. This ensures you design for the right constraints.
Select a structure like a versioned file system, copy-on-write B-tree, or log-structured storage that allows efficient snapshots and restores.
Define how a snapshot is created (e.g., capturing metadata, marking blocks as read-only) and how restore reverts state while freeing or reclaiming capacity.
Explain how to free or reclaim space when snapshots are deleted or restored, using techniques like reference counting, garbage collection, or block reuse.
Compare approaches (e.g., time vs. space, simplicity vs. performance) and address concurrency, failure recovery, and scalability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty standard once the data model is solid.
Clarify the requirements first: what does 'across the system' mean (single machine vs distributed), expected scale, and whether the query is one-off or continuous. Then propose a solution using a min-heap of size N for efficient top-N selection, with a custom comparator that orders by size descending and name ascending. Discuss trade-offs between in-memory and distributed approaches, and how to handle ties and updates.
Pro tip: Mention that you would use a bounded min-heap to avoid sorting the entire dataset, and that for distributed systems you can use a two-phase approach: local top-N per node, then merge globally. This shows awareness of scalability and efficiency.
Ask about data volume, distribution, update frequency, and whether the result must be exact or approximate. Confirm the tie-breaking rule and output format.
Select a min-heap of size N for top-N selection, with a comparator that prioritizes larger size and then lexicographically smaller name. Explain why this is O(M log N) for M files.
If data is distributed, propose a map-reduce style approach: each node computes its local top-N, then a reducer merges them. Discuss partitioning and fault tolerance.
Address ties, fewer than N files, and dynamic updates. Suggest maintaining a heap or using a database with appropriate indexes for frequent queries.
Compare heap-based selection vs full sort, and in-memory vs distributed. Discuss time/space complexity and when to use each approach.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.