Databricks·Software Engineer·Onsite - System Design / Architecture·Senior

Senior

May 2026

Summary

System design round at Databricks focused entirely on building a durable in-memory key-value cache with a Write-Ahead Log. Pretty deep dive, more than I expected for a single question.

Questions Asked (1)

Design an in-memory key-value cache that uses a Write-Ahead Log for durability and crash recovery. Walk through the write path, read path, recovery mechanism, WAL management, and the trade-offs between durability and write latency.

System DesignTechnical Trade-offs

Author's notes

This one sprawled in a way I wasn't fully prepared for.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (e.g., durability guarantees, latency targets, data size) and then walk through the write and read paths, emphasizing how the WAL ensures durability. Explain the recovery process and WAL management strategies, and conclude with trade-offs between durability and write latency, showing awareness of Databricks' focus on performance and reliability.

Pro tip: Mention that you would batch WAL writes and use group commit to amortize fsync costs, significantly improving write throughput while maintaining durability. Also, discuss how you would handle WAL corruption or partial writes during recovery.

1. Clarify Requirements and Assumptions

Ask about expected read/write ratio, data size, latency and durability requirements, and whether the cache needs to survive process crashes or machine failures. State your assumptions clearly.

2. Design the Write Path

Describe how writes are first appended to the WAL (with optional buffering and group commit) and then applied to the in-memory hash map. Discuss fsync policies and how they affect durability and latency.

3. Design the Read Path

Explain that reads are served directly from the in-memory map for low latency, and mention how you handle cache misses (e.g., return not found or fetch from backing store if applicable).

4. Recovery Mechanism

Detail how on startup the system replays the WAL to rebuild the in-memory state, handling partial writes and corruption (e.g., checksums, truncation). Mention checkpointing to reduce recovery time.

5. WAL Management and Trade-offs

Discuss WAL rotation, truncation after checkpointing, and compaction. Analyze trade-offs: synchronous vs asynchronous writes, fsync frequency, and their impact on durability and write latency.

Key Points to Mention

Write-Ahead Logging (WAL) ensures durability by persisting writes before applying to memory.
Group commit and batching reduce fsync overhead, improving write throughput.
Recovery involves replaying the WAL and possibly using checkpoints to speed up the process.
WAL rotation and truncation prevent unbounded growth; checkpoints allow safe truncation.
Trade-off: synchronous writes with fsync provide strong durability but higher latency; asynchronous writes lower latency but risk data loss on crash.
Consider using a log-structured approach for the WAL to optimize write performance.

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