This question is massive and I underestimated how much ground they'd want to cover.
Start by clarifying requirements and constraints, then design a scalable architecture that separates metadata and data planes, using partitioning and replication for scalability and durability. Focus on trade-offs between consistency, latency, and cost, and explain how each component handles the range of object sizes.
Pro tip: Emphasize how you would handle large objects (e.g., multipart uploads) and the metadata layer's role in enabling efficient LIST operations, as these are often overlooked but critical for S3-like systems.
Ask about scale (number of objects, requests per second), consistency requirements, durability targets, and cost constraints. Confirm the operations and object size range.
Propose a separation of metadata and data planes. Use a distributed metadata store (e.g., sharded RDBMS or NoSQL) and a scalable object store (e.g., chunked blobs on distributed file system or cloud storage).
Design the metadata schema (buckets, objects, versions) and partition by bucket and object key hash to distribute load. Discuss indexing for efficient LIST operations.
Detail how PUT/GET/DELETE work for small and large objects, including multipart uploads for large objects, chunking, and parallel transfers. Explain how LIST is implemented with pagination.
Describe replication (e.g., cross-AZ), erasure coding for durability, and consistency model (e.g., eventual for LIST, strong for GET after PUT). Discuss trade-offs and failure handling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Jumped to consistent hashing too fast without acknowledging the hot-bucket problem.
Start by clarifying requirements like object size, access patterns, and consistency needs. Then propose a scalable metadata architecture using sharded, distributed databases with a hierarchical namespace, and discuss partitioning strategies such as consistent hashing or range-based sharding. Finally, address trade-offs around consistency, availability, and operational complexity.
Pro tip: Emphasize that metadata operations are often the bottleneck in object storage systems, so design for low-latency lookups and high-throughput writes by decoupling metadata from data and using caching layers.
Ask about object size distribution, read/write ratio, latency SLAs, consistency requirements, and geographic distribution to scope the problem.
Propose a distributed, sharded database (e.g., a NoSQL store like Cassandra or a custom system) that stores object metadata (ID, path, size, checksums, ACLs) and supports efficient range and point queries.
Discuss partitioning by hash of object ID for even distribution, or by hierarchical path for range scans; consider consistent hashing to minimize rebalancing when scaling.
Explain replication for durability, sharding for horizontal scale, and techniques like caching, bloom filters, and asynchronous replication to handle trillions of objects.
Compare consistency vs. availability (e.g., eventual consistency for metadata), discuss indexing strategies, and mention monitoring and rebalancing mechanisms.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through erasure coding across availability zones and why pure replication gets expensive at exabyte scale.
Start by clarifying that eleven nines (99.999999999%) durability means an annual failure probability of 10^-11, which is beyond what any single system can achieve. Then describe a multi-layered strategy combining erasure coding, replication across fault domains, and continuous integrity verification, while acknowledging the trade-offs between durability, cost, and availability.
Pro tip: Emphasize that durability is about preventing data loss, not just achieving uptime—distinguish it from availability, and mention that at this scale, even rare correlated failures (e.g., software bugs, operator errors) must be mitigated through techniques like immutable backups and formal verification.
Define what eleven nines means in terms of annual failure probability and ask about data volume, access patterns, and cost constraints. This shows you understand the magnitude and can tailor the solution.
Use erasure coding (e.g., Reed-Solomon) with high redundancy factors and distribute fragments across multiple availability zones, regions, and possibly cloud providers. Ensure no single fault domain can cause data loss.
Continuously checksum data, scrub for bit rot, and automatically repair corrupted fragments using surviving redundancy. This prevents silent data corruption from degrading durability over time.
Mitigate software bugs, operator mistakes, and malicious attacks through immutable backups, versioning, and strict access controls. Consider formal verification for critical components.
Model durability using probability theory (e.g., Markov models) and monitor real-world failure rates. Use this to validate that the design meets the target and to adjust redundancy as needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Actually felt like the strongest part of my interview.
Start by outlining the multipart upload flow at a high level: initiate, upload parts in parallel, and complete. Then dive into failure handling by discussing retries, idempotency, and resumability. Emphasize how you'd design for reliability and efficiency at scale.
Pro tip: Mention that you'd use checksums per part and for the final object to detect corruption, and that you'd store upload state persistently to allow resumption after client or server failures.
Describe how the client requests an upload session from the server, which returns an upload ID and possibly pre-signed URLs for each part.
Explain how the large object is split into smaller parts (e.g., 5-10 MB) that are uploaded independently, potentially in parallel, each with a part number and checksum.
Discuss how to detect failed part uploads (e.g., via timeouts or error responses) and retry them with exponential backoff, ensuring idempotency to avoid duplicate parts.
Once all parts are uploaded, the client sends a complete request with the list of part numbers and ETags; the server assembles the parts into the final object and verifies integrity.
Mention aborting incomplete uploads to free resources, and how clients can resume by querying the server for uploaded parts if the upload is interrupted.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Kept it relatively high level: request signing for authn, policy evaluation for authz with bucket-level and object-level ACLs.
Start by clarifying requirements and scale, then separate authentication (identity verification) from authorization (permission enforcement). Propose a design that uses centralized identity management (e.g., OAuth, SSO) and a scalable authorization model (e.g., RBAC or ABAC) with policy enforcement points at the API gateway and object storage layer. Discuss trade-offs between simplicity, flexibility, and performance.
Pro tip: Emphasize the principle of least privilege and how you would handle cross-team or external sharing scenarios, as Dropbox deals with complex sharing permissions. Also mention the importance of auditing and logging for security and compliance.
Ask about scale (number of users, objects, buckets), types of access (internal, external, public), and compliance needs. This shows you understand the problem context before diving into solutions.
Describe how users and services authenticate, e.g., using OAuth 2.0, SAML, or API keys. Mention token validation, session management, and integration with identity providers.
Choose an authorization model (RBAC, ABAC, or ReBAC) and explain how permissions are defined and evaluated. Discuss how to represent objects, buckets, and actions, and how policies are stored and updated.
Explain where enforcement happens: at the API gateway, service layer, and storage layer. Describe how to ensure consistent enforcement and handle caching for performance.
Discuss trade-offs between centralized vs. decentralized enforcement, latency vs. consistency, and how to handle sharing, delegation, and revocation. Mention auditing and monitoring.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Short discussion at the end of the interview when time was running low.
Start by clarifying the workload characteristics (read/write ratio, object size, access patterns) and then propose a multi-tier caching architecture with CDN at the edge, regional caches, and origin storage. Address hot-key scenarios by discussing techniques like request coalescing, key sharding, and local in-memory caches, while balancing consistency, latency, and cost trade-offs.
Pro tip: Mention that hot keys often indicate a suboptimal key design or a need for application-level caching, and propose monitoring cache hit ratios and hot-key detection to dynamically adjust TTLs and shard counts.
Ask about object size, read/write ratio, latency SLOs, consistency requirements, and geographic distribution to tailor the caching strategy.
Propose a hierarchy: CDN edge caches for static content, regional Redis/Memcached clusters for dynamic objects, and an origin store like S3. Explain cache invalidation and TTL policies.
Discuss techniques such as request coalescing (singleflight), key sharding (adding a random suffix), local in-memory caches on application servers, and rate limiting to prevent overload.
Describe strategies for cache invalidation (TTL, write-through, write-behind, versioning) and how to balance consistency with performance, especially for mutable objects.
Explain how to track cache hit ratio, latency, and hot keys using metrics and logging, and how to use that data to auto-scale caches and adjust TTLs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.