Start by clarifying requirements and scale (e.g., number of users, file sizes, consistency needs), then design the core data model and APIs for file operations, folders, and sharing. Next, dive into the critical components: chunked resumable uploads, versioning, sync protocol, and search, discussing trade-offs and bottlenecks. Finally, tie it together with a high-level architecture diagram and address scalability, reliability, and security.
Pro tip: Emphasize the sync protocol early—it's the hardest part and often overlooked. Discuss how you'd handle conflicts (e.g., vector clocks or operational transforms) and why you chose a particular approach.
Ask questions to understand scale (users, files, QPS), consistency requirements, and key features. Define functional and non-functional requirements to guide the design.
Define entities: users, files, folders, permissions, versions. Design RESTful APIs for CRUD operations, sharing, and search. Discuss how to represent hierarchical folders (e.g., materialized paths or adjacency lists).
Outline services: metadata service, block storage, upload service, sync service, search service. Explain how they interact and scale. Cover chunked resumable uploads, deduplication, and versioning.
Detail the sync protocol: how clients detect changes, push/pull updates, and resolve conflicts. Discuss trade-offs between consistency models (e.g., strong vs eventual) and conflict resolution strategies.
Highlight key trade-offs (e.g., consistency vs availability, latency vs durability). Explain how to scale each component (sharding, caching, CDN) and ensure security (encryption, access control).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: atomicity from the user's perspective, consistency of permissions, and performance constraints. Then propose a design that separates the logical move from the physical permission propagation, using indirection and asynchronous processing to achieve both goals.
Pro tip: Emphasize that 'atomic' means the user sees an instantaneous change, not that the underlying data is updated in one transaction. This distinction shows you understand real-world trade-offs and can design for user experience while managing backend complexity.
Ask about the expected scale, consistency requirements, and whether the move must be truly atomic or just appear atomic to the user. Confirm that permissions are inherited from the parent and that rewriting 50,000 rows synchronously is unacceptable.
Propose updating a single pointer or metadata record to change the folder's parent, making the move appear instantaneous. Use a transaction to ensure the pointer update is atomic and consistent.
Instead of updating each file's permissions, compute effective permissions dynamically by traversing the folder hierarchy. Alternatively, use an asynchronous job to propagate changes in the background, ensuring eventual consistency.
Discuss how to handle reads during propagation: either serve stale permissions until the job completes or use a versioning scheme to ensure correct access. Implement idempotent and retryable background jobs to handle failures.
Compare approaches: dynamic permission calculation (simpler but potentially slower reads) vs. asynchronous propagation (faster reads but eventual consistency). Mention caching and indexing strategies to mitigate performance impacts.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Walk through the sync protocol end-to-end: how each client tracks changes offline, how the server detects concurrent edits on reconnect, and the conflict resolution strategy. Then describe the user experience—what UI signals a conflict and what options the user has to resolve it.
Pro tip: Acknowledge that perfect automatic merge is impossible for all file types; show maturity by proposing a hybrid approach (auto-merge for text, manual resolution for binary) and explaining how you'd measure conflict rates to improve the system.
Explain how each client records local edits while offline—e.g., using a version vector, operation log, or file hash—so the server can later identify concurrent modifications.
Describe the handshake: each client sends its base version and changes; the server compares versions and detects that both clients edited from the same base, indicating a conflict.
Outline the resolution strategy: attempt automatic merge (e.g., three-way merge for text), fall back to conflict markers or manual resolution for binary or overlapping edits.
Detail what the user sees: a conflict notification, side-by-side diff, or merged file with markers, plus options to choose a version or edit manually.
Mention scenarios like simultaneous reconnects, large files, or partial sync failures, and how the protocol ensures consistency (e.g., server serializes updates, uses locks).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Content-addressing chunks by hash is the key insight here.
Start by clarifying the requirements and constraints (file size, edit frequency, network conditions). Then propose a chunking strategy (e.g., content-defined chunking) and describe how to track chunk versions in the data model. Finally, outline the upload path changes: client computes chunk hashes, sends only missing chunks, and server updates metadata atomically.
Pro tip: Mention that content-defined chunking (like Rabin fingerprinting) is more robust than fixed-size chunking for insertions/deletions, and discuss how to handle chunk deduplication across files to save storage.
Ask about file size, edit patterns, network reliability, and consistency requirements to tailor the solution.
Choose between fixed-size and content-defined chunking (CDC), explaining trade-offs. CDC handles shifts better but has higher CPU overhead.
Add chunk-level metadata: chunk hashes, sizes, order, and versioning. Consider a manifest that maps file versions to chunk lists.
Client computes chunk hashes, compares with server manifest, uploads only missing chunks, and sends a new manifest to commit the version.
Ensure atomic updates, handle partial uploads, and implement garbage collection for orphaned chunks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Convergent encryption is the answer they were looking for and I knew it existed but could not remember the name or explain it precisely under pressure.
Start by clarifying the scale and sensitivity of the data, then propose a content-addressed deduplication system with per-user encryption and access controls. Explain how to safely deduplicate while mitigating privacy and security risks like side-channel attacks and data leakage.
Pro tip: Emphasize that deduplication must be done on encrypted data with per-user keys to avoid cross-user information leakage, and mention that you'd use a keyed hash (HMAC) instead of a plain hash to prevent confirmation attacks.
Ask about data volume, sensitivity, compliance needs (e.g., GDPR, HIPAA), and whether cross-user deduplication is even allowed. This shows you consider legal and privacy implications before technical solutions.
Propose content-defined chunking (e.g., Rabin fingerprinting) to identify duplicates at block level, and store chunks in a content-addressed store. Use convergent encryption with per-user keys to encrypt chunks before deduplication.
Explain that plaintext hashes enable confirmation attacks (an attacker can check if a file exists). Mitigate by using keyed hashes (HMAC) with a secret key, or by only deduplicating within a user's own data.
Discuss risks like side-channel attacks (timing, storage usage) that can reveal file existence, and poisoning attacks where a malicious user injects a chunk to corrupt others' data. Mitigate with access controls, integrity checks, and per-user encryption.
Acknowledge that perfect cross-user dedup may be impossible without some information leakage. Offer alternatives like client-side dedup with user-specific salts, or limiting dedup to within a tenant/organization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with a multi-region setup using synchronous replication to a standby with automatic promotion.
Start by defining the failure scenario and the failover mechanism, then quantify RPO and RTO with concrete numbers and assumptions. Finally, walk through client-visible symptoms during cutover, emphasizing trade-offs between consistency and availability.
Pro tip: Proactively discuss how you would measure and monitor RPO/RTO in production, and mention any mitigations like client-side retries or read-only fallbacks to reduce user impact.
Describe the region failure and how failover is triggered—automatically via health checks or manually. Mention the replication setup (e.g., synchronous vs asynchronous) and the promotion of a standby replica.
State expected RPO (e.g., near-zero for sync replication, seconds/minutes for async) and RTO (e.g., minutes for automated failover). Justify with architecture details and note any dependencies like DNS TTL.
Detail what clients might experience: increased latency, errors, stale reads, or lost writes. Differentiate between read and write operations and mention any client-side retry logic.
Highlight trade-offs between consistency and availability, and propose mitigations like idempotent writes, conflict resolution, or degraded read-only mode to minimize impact.
Recap the failover story, RPO/RTO, and client impact. Suggest testing via game days or chaos engineering to validate assumptions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.