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

Senior

Jun 2026

Summary

Databricks system design round, one big question about building a durable multi-producer log writer. Dense topic and they clearly wanted you to go deep on every sub-problem, not just sketch an architecture and move on.

Questions Asked (1)

Design a log writer that supports concurrent producer threads and guarantees each accepted log entry is durably written to disk (fsynced) before returning success. Cover your locking strategy, how you'd batch writes to amortize fsync cost, ordering guarantees across threads, back-pressure when the disk can't keep up, and crash safety.

System DesignTechnical Trade-offs

Author's notes

This question is basically five questions jammed into one.

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Suggested Approach

Start by clarifying requirements and constraints, then propose a design with a single writer thread that batches entries from concurrent producers into a group commit, using a bounded queue for back-pressure. Explain how fsync is performed once per batch, how ordering is preserved via sequence numbers, and how crash safety is ensured with checksums and recovery.

Pro tip: Emphasize that group commit is the key to amortizing fsync cost, and that back-pressure is essential to avoid unbounded memory growth; also mention that you'd measure and tune batch size and timeout based on workload.

1. Clarify requirements and constraints

Ask about expected throughput, latency requirements, durability guarantees (e.g., fsync per entry vs. per batch), and whether ordering across threads is required. This shows you understand the problem space before diving into design.

2. Design the core architecture

Propose a single writer thread that consumes from a concurrent queue (e.g., lock-free MPSC queue) where producers enqueue log entries. The writer batches entries and writes them to disk with a single fsync per batch.

3. Detail locking and batching strategy

Explain how producers enqueue without blocking (or with minimal blocking) and how the writer thread groups entries. Use a condition variable or timed wait to trigger batch flush when either batch size or timeout is reached.

4. Address ordering, back-pressure, and crash safety

Assign sequence numbers to entries for ordering, use a bounded queue to apply back-pressure (blocking or rejecting producers when full), and ensure crash safety with checksums and a recovery process that replays the log from the last consistent point.

5. Discuss trade-offs and optimizations

Compare group commit vs. per-entry fsync, blocking vs. non-blocking back-pressure, and potential optimizations like double-buffering or using O_DIRECT. Mention how you'd test and tune parameters.

Key Points to Mention

Group commit: batching multiple log entries into a single fsync to amortize disk latency.
Bounded queue for back-pressure: prevents unbounded memory usage and provides flow control.
Ordering guarantees: use sequence numbers or a single writer to ensure total order across threads.
Crash safety: write-ahead logging with checksums, and recovery that truncates incomplete batches.
Locking strategy: minimize contention with lock-free enqueue or fine-grained locks, and avoid holding locks during I/O.
Trade-offs: latency vs. throughput, durability vs. performance, and how to tune batch size and timeout.

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