Started with a HashMap keyed on file ID, felt pretty solid.
Start by clarifying requirements and constraints (e.g., expected scale, concurrency, persistence) before diving into the design. Then define the data model and API contracts, focusing on return values and error handling. Finally, discuss trade-offs and potential improvements like sharding or caching.
Pro tip: Explicitly state your assumptions about scale and concurrency, and mention that you'd use a thread-safe data structure like ConcurrentHashMap to handle concurrent requests. This shows you think about real-world production concerns.
Ask questions to understand the scope: expected number of files, size limits, concurrency needs, and whether persistence is required. This ensures your design meets the actual needs.
Specify the File object with fields: id (unique), name, size, metadata (map), and possibly content. Decide on ID generation strategy (e.g., UUID).
Define methods: addFile(file) -> returns file ID or full file; getFile(id) -> returns file or null; deleteFile(id) -> returns boolean or void. Specify return types and error conditions.
Decide on error handling: throw exceptions (e.g., FileNotFoundException) or return error objects. Consider duplicate IDs, missing files, invalid input, and concurrency issues.
Mention limitations of in-memory storage (e.g., data loss on restart, memory constraints) and how you might extend it (e.g., persistence, sharding, caching).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where I started second-guessing my data structures.
Start by clarifying requirements: scale, update frequency, and whether ties should be broken deterministically. Then propose a solution using a min-heap of size k for global queries and per-user heaps or a composite key approach, explicitly defining tie-breaking rules (e.g., by file ID or timestamp) to ensure consistency.
Pro tip: Mention that tie-breaking should be deterministic and documented, and consider using a composite key (size, file ID) to avoid ambiguity. Also, discuss how to handle updates efficiently, such as lazy deletion or maintaining heaps incrementally.
Ask about scale (number of files, users), query frequency, and whether results must be deterministic. Confirm if ties should be broken by a secondary attribute like file ID or creation time.
For global top-k, use a min-heap of size k. For per-user top-k, maintain a separate min-heap per user or use a composite key (userID, size) in a global heap. Consider memory and update costs.
Specify a deterministic tie-breaker, such as smaller file ID first, or lexicographic order. Ensure the same rule applies to both global and per-user queries for consistency.
Discuss how to handle file insertions, deletions, and size changes. Options include lazy deletion (mark as deleted and skip during queries) or maintaining heaps with decrease-key/increase-key operations.
Compare time and space complexity: heap approach O(n log k) for building, O(log k) per update. Discuss alternatives like sorting or quickselect for batch queries, and distributed solutions for scale.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Quota enforcement was fine, just track used bytes per user and reject adds that exceed the cap.
Start by clarifying requirements and scale, then design a quota system that enforces limits atomically at the point of file addition, considering both hard and soft limits. For account merging, define a clear conflict resolution policy (e.g., keep both, rename, or deduplicate) and implement it transactionally to avoid data loss.
Pro tip: Always discuss idempotency and failure handling—e.g., what happens if a quota check passes but the write fails, or if a merge is interrupted. This shows you think about production reliability, not just happy paths.
Ask about scale (users, files, storage per user), quota types (hard vs soft), and merge semantics (e.g., should files be combined, or should one account be primary?). Confirm whether identifiers are globally unique or per-user.
Propose a quota service that tracks usage per user and checks limits before accepting a file. Ensure atomicity via transactions or optimistic concurrency to prevent race conditions.
Outline a merge process: validate both accounts, combine files, and resolve conflicts based on a defined policy. Consider using a merge token or two-phase commit to handle failures.
For duplicate identifiers or names, choose a policy: keep both with renamed identifiers, keep the newer/older version, or prompt the user. Explain trade-offs (e.g., data loss vs. storage bloat).
Discuss handling large merges, quota recalculation after merge, and potential performance bottlenecks. Mention monitoring and rollback plans.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements: what types of files, backup frequency, and restore expectations. Then define snapshot semantics (point-in-time vs rolling) and justify your choice based on trade-offs in storage overhead and consistency. Finally, address conflict resolution for restores, proposing a clear policy (e.g., versioning, rename, or merge) and explaining how it handles files added after the snapshot.
Pro tip: Demonstrate awareness of real-world constraints by discussing incremental backups and deduplication to reduce storage overhead, and propose a conflict resolution strategy that minimizes data loss while being user-friendly.
Ask questions to understand the scope: file types, backup frequency, retention policy, and whether backups are local or cloud-based. State your assumptions explicitly.
Choose between point-in-time (full copy at a moment) and rolling (continuous or periodic incremental). Explain the trade-offs in consistency, complexity, and storage overhead.
Estimate storage overhead based on snapshot frequency, file change rate, and deduplication/compression. Discuss how to optimize (e.g., incremental backups, block-level dedup).
Define what happens when restoring a snapshot conflicts with files added/modified after the snapshot. Propose policies: overwrite, skip, rename, merge, or versioned restore.
Summarize trade-offs (storage vs consistency, simplicity vs flexibility) and address edge cases like concurrent modifications, partial failures, and user experience.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.