← Databricks Interview Insights
Started with chunked storage and replication, which felt solid.
Start by clarifying requirements and scale, then design a distributed file system with a metadata service and data nodes, focusing on efficient deletion. Propose a lazy deletion mechanism using tombstones and background garbage collection, and discuss trade-offs with alternatives like eager deletion.
Pro tip: Emphasize that deletion efficiency at scale often means decoupling logical deletion from physical reclamation, and highlight how this affects consistency and performance. Mention that Databricks' Delta Lake uses a similar approach with deletion vectors and vacuuming.
Ask about expected file sizes, number of files, deletion frequency, consistency requirements, and latency expectations. This shapes the design and trade-offs.
Propose a distributed architecture with a metadata service (e.g., master nodes) and data nodes storing file chunks. Discuss partitioning and replication for scalability and fault tolerance.
Design deletion as a two-phase process: mark files as deleted in metadata (tombstones) and asynchronously reclaim space via background garbage collection. Explain how this avoids costly synchronous operations.
Compare lazy deletion with eager deletion, discussing impacts on latency, consistency, storage overhead, and complexity. Mention how to handle concurrent reads/writes during deletion.
Explain how the design scales with metadata sharding, data partitioning, and replication. Discuss failure recovery for deletion operations and garbage collection.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging that CAP theorem forces a choice between consistency and availability during network partitions, and that the right tradeoff depends on the specific data and operations. Then, differentiate metadata (which typically requires strong consistency for correctness) from data (where availability and eventual consistency may be acceptable), and propose a hybrid approach using appropriate technologies and patterns.
Pro tip: Emphasize that in real systems, the choice is not binary: you can achieve strong consistency for critical metadata while maintaining high availability for data by using consensus protocols for metadata and eventual consistency for data, and by carefully defining consistency boundaries.
Define what CAP means in the context of the system: during a network partition, you must choose between consistency (linearizability) and availability. Ask clarifying questions about the system's SLAs, user expectations, and the cost of inconsistency for metadata vs. data.
Explain that metadata (e.g., schema, permissions, table locations) often requires strong consistency to avoid corruption or security issues, while data (e.g., table contents) can tolerate eventual consistency for higher availability.
Suggest using a CP system (e.g., Raft, Paxos, or ZooKeeper) for metadata to ensure strong consistency, and an AP system (e.g., eventually consistent storage like S3 or Cassandra) for data to ensure availability. Highlight that this separation allows optimizing each independently.
Acknowledge that even metadata may need availability in some cases; discuss techniques like quorum reads/writes, read-your-writes consistency, or conflict-free replicated data types (CRDTs) for metadata where possible. Also mention monitoring and alerting for consistency violations.
Tie the answer to Databricks' architecture: for example, the Databricks Lakehouse uses a metadata layer (e.g., Unity Catalog) that may require strong consistency, while data storage on cloud object stores is eventually consistent. Mention how Delta Lake provides ACID transactions on top of eventually consistent storage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Structure your answer as a chronological walkthrough of the delete pipeline, from API request to storage reclamation, highlighting key stages and design considerations. Emphasize how the system ensures correctness, durability, and efficiency, and mention trade-offs and failure handling.
Pro tip: Show awareness of real-world constraints like eventual consistency, garbage collection delays, and the importance of idempotency and auditability. Discuss how you balance immediate user expectations with backend efficiency.
Describe how the delete API call is received, authenticated, validated, and translated into an internal delete operation. Mention idempotency and request tracking.
Explain how metadata is updated to mark the object as deleted (e.g., tombstone) and how this affects subsequent reads. Discuss transactional guarantees and consistency.
Detail how the delete request is queued for asynchronous processing, possibly via a message queue or job scheduler, to decouple from the API response and handle retries.
Describe the process of physically deleting data from storage, including any background garbage collection, compaction, or vacuuming. Mention how storage is actually reclaimed and any delays.
Explain how the pipeline is monitored, how failures are handled and retried, and how audit logs are maintained for compliance and debugging.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Genuinely the hardest part of the whole interview.
Start by defining the core tension: deletes and garbage collection (GC) must respect snapshot isolation and replication consistency. Then walk through how you would design the system to handle deletes without breaking snapshots or replication, using mechanisms like tombstones, versioning, and coordinated GC. Conclude by discussing trade-offs and how you would validate correctness.
Pro tip: Emphasize that GC should be delayed until all snapshots and replicas that might need the deleted data are no longer active, and mention using a low-watermark mechanism to track this safely.
Restate the problem: ensure deletes are propagated correctly, snapshots remain consistent, and replication doesn't resurrect deleted data. Identify key constraints like snapshot retention, replication lag, and consistency models.
Explain how deletes are represented (e.g., tombstones, delete markers) and how they are versioned. Discuss how snapshots see a consistent view (e.g., snapshot isolation) and how deletes are applied relative to snapshot creation.
Describe GC strategy: only reclaim data when no snapshot or replica needs it. Introduce a low-watermark based on the oldest active snapshot and replication checkpoint. Discuss how GC interacts with compaction and storage layers.
Detail how deletes and GC are replicated: either replicate tombstones and let replicas GC independently, or coordinate GC across replicas. Address idempotency, ordering, and handling of replica lag.
Cover trade-offs: storage overhead vs. GC aggressiveness, latency vs. consistency. Mention failure cases like replica down during GC, and how to recover (e.g., re-replication, snapshot restore).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.