← Databricks Interview Insights
I started with the API surface which felt safe, but then they immediately pushed on consistency guarantees and I kind of froze for a second.
Start by clarifying requirements (scale, consistency, latency, durability) and then propose a layered architecture: a sharding layer, a replication layer with consensus (e.g., Raft) for consistency, and a storage engine (e.g., LSM-tree) for persistence. Explain how TTL and CAS are implemented on top of this core, discussing trade-offs between consistency, availability, and performance.
Pro tip: Emphasize that CAS requires linearizability and thus a consensus protocol like Raft; mention that TTL can be implemented via lazy expiration with periodic compaction to avoid write amplification. Also, discuss how to handle clock skew and the importance of monotonic clocks for TTL.
Ask questions to understand expected scale (data size, QPS), consistency needs (strong vs eventual), latency targets, durability, and deployment environment. This shapes the design choices.
Propose a distributed system with sharding (e.g., consistent hashing) for scalability, replication for fault tolerance, and a consensus protocol (e.g., Raft) for strong consistency. Outline the data model: keys are strings, values are byte arrays, with optional TTL and CAS.
Choose a storage engine (e.g., LSM-tree for write-heavy workloads) and describe how data is stored: key -> (value, version, expiration timestamp). Explain how TTL is stored and enforced, and how CAS uses version numbers.
Detail TTL: lazy expiration on read plus background compaction to remove expired keys. Detail CAS: use version numbers or timestamps; CAS operation is a conditional write that checks the current version and updates atomically via consensus.
Discuss trade-offs: consistency vs latency (e.g., quorum reads/writes), TTL precision vs overhead, CAS contention. Cover failure scenarios: node failures, network partitions, and how the system recovers (e.g., Raft leader election, log replication).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with consistent hashing pretty quickly, drew the ring, talked about virtual nodes to avoid hotspots.
Start by clarifying the system's requirements (e.g., consistency, availability, data size) and then describe a partitioning strategy that minimizes data movement during rebalancing. Explain how you would handle node additions/removals using consistent hashing or range-based partitioning with virtual nodes, and discuss trade-offs between different approaches.
Pro tip: Mention that rebalancing should be incremental and throttled to avoid overwhelming the cluster, and highlight the importance of monitoring and metrics to detect hotspots during rebalancing.
Ask about data size, read/write patterns, consistency needs, and fault tolerance to tailor your partitioning strategy.
Discuss options like range partitioning, hash partitioning, or consistent hashing, and explain why one fits the scenario (e.g., consistent hashing minimizes rebalancing).
Describe how data is redistributed when nodes join/leave: e.g., using virtual nodes, consistent hashing rings, or dynamic range splits. Emphasize incremental and throttled movement.
Compare trade-offs (e.g., consistency vs. availability, movement cost vs. load balance) and explain how to handle failures during rebalancing (e.g., retries, rollback).
Mention the need for monitoring (e.g., hotspots, rebalancing progress) and potential optimizations like pre-splitting or adaptive rebalancing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the part I actually felt decent about.
Start by clarifying the system's requirements (e.g., consistency, availability, latency) and then compare the three strategies in terms of their mechanisms and tradeoffs. Conclude with a recommendation that aligns with the requirements, acknowledging that the choice depends on the specific use case.
Pro tip: Mention that in practice, systems often combine these approaches—for example, using CRDTs for certain data types and LWW for others—and that the choice should be driven by the application's tolerance for conflicts and need for causality tracking.
Ask about the system's consistency, availability, and partition tolerance needs, as well as the nature of the data and write patterns.
Briefly describe vector clocks, last-write-wins (LWW), and CRDTs, focusing on how they detect and resolve conflicts.
Compare the strategies in terms of metadata overhead, conflict resolution accuracy, complexity, and suitability for different scenarios.
Based on the requirements, suggest which strategy or combination would be most appropriate, and justify your choice.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the cluster's scale, consistency requirements, and failure model, then propose a layered detection strategy combining heartbeats, timeouts, and gossip-based protocols. Discuss trade-offs between accuracy, latency, and overhead, and how to integrate with existing systems like ZooKeeper or etcd for coordination.
Pro tip: Emphasize that failure detection is probabilistic and must handle false positives/negatives gracefully; mention using adaptive timeouts based on network conditions and phi accrual failure detector as a more robust alternative to fixed timeouts.
Ask about cluster size, network reliability, consistency needs, and whether the system is for a distributed database or compute cluster. This shapes the choice of detection mechanism.
Propose heartbeats with timeouts as a baseline, or gossip-based protocols for scalability. Discuss centralized vs. decentralized approaches and their trade-offs.
Explain how to mark nodes as failed, trigger re-replication or failover, and reintegrate recovered nodes. Address split-brain and network partitions.
Describe how to set timeouts adaptively, monitor false positive rates, and use metrics to adjust parameters. Mention logging and alerting for detection events.
Summarize trade-offs between detection speed, accuracy, and overhead. Justify your choices based on the requirements gathered in step 1.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the workload characteristics (read/write patterns, data size, latency requirements, consistency needs) before recommending a storage engine. Then compare options like LSM-trees (RocksDB) vs B-trees (InnoDB) vs columnar formats (Parquet/Delta), and justify your choice based on the specific use case and Databricks' ecosystem.
Pro tip: Mention how Databricks' Delta Lake builds on Parquet and adds a transaction log for ACID guarantees, showing you understand their stack. Also, discuss trade-offs like write amplification vs read performance, and how you'd benchmark or validate the choice.
Ask about workload: read-heavy vs write-heavy, latency, throughput, data volume, consistency, and query patterns. This ensures your recommendation is context-driven.
Categorize options: row-based (B-trees, LSM-trees) for OLTP, columnar (Parquet, ORC) for OLAP, and hybrid (Delta Lake). Explain their core data structures and trade-offs.
Compare write amplification, read performance, compression, and update capabilities. For example, LSM-trees excel at writes but may have read amplification; B-trees offer fast reads but slower writes.
Highlight how your choice integrates with Databricks: Delta Lake for ACID transactions on data lakes, Photon for vectorized query execution, and RocksDB for stateful streaming.
State your final choice with clear reasoning, acknowledging limitations and potential alternatives. Suggest how you'd validate it (e.g., benchmarks, load testing).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.