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Databricks·Backend Engineer·Technical Phone Screen·Senior

Senior
May 2025Remote

Summary

Phone screen for a backend role at Databricks, early May. One system design question, classic distributed systems territory. Pretty standard round, nothing unexpected.

Questions Asked (1)

Q1

Design a distributed file system.

System DesignTechnical Trade-offsData Modeling
Author's notes

Classic problem that floats around on interview prep forums.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then design a high-level architecture that separates metadata management from data storage. Discuss trade-offs between consistency, availability, and partition tolerance, and dive into key components like chunking, replication, and fault tolerance.

Pro tip: Relate your design to Databricks' ecosystem (e.g., Delta Lake, Spark) and emphasize how your choices support analytics workloads and multi-cloud environments.

1. Clarify Requirements

Ask about scale (file sizes, number of files, clients), consistency needs, latency, and durability. Determine if the system is for analytics, general-purpose, or specific workloads.

2. High-Level Architecture

Propose a master-slave or peer-to-peer architecture. Separate metadata (namespace, permissions) from data storage. Consider using a distributed metadata store like ZooKeeper or etcd.

3. Data Storage and Chunking

Explain how files are split into chunks/blocks, stored across data nodes, and replicated for fault tolerance. Discuss chunk size trade-offs (e.g., 64MB vs 128MB).

4. Consistency and Fault Tolerance

Describe the consistency model (e.g., strong vs eventual). Explain replication strategies, handling node failures, and ensuring data durability (e.g., checksums, re-replication).

5. Scalability and Performance

Discuss how the system scales horizontally, handles metadata bottlenecks, and optimizes for throughput (e.g., data locality, caching). Mention monitoring and load balancing.

Key Points to Mention

  • Separation of metadata and data (e.g., GFS/HDFS architecture)
  • Chunking and replication strategies for fault tolerance
  • Consistency models and trade-offs (CAP theorem)
  • Metadata management and scalability (e.g., partitioning, caching)
  • Fault tolerance mechanisms (heartbeats, re-replication, checksums)
  • Integration with analytics frameworks (e.g., Spark, Delta Lake)

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.