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

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Apr 2026

Summary

Databricks system design round, one meaty question about building a concurrent log writer. The kind of problem that sounds manageable until you're 20 minutes in and realizing you've been hand-waving the hard parts.

Questions Asked (1)

Design a thread-safe log writer that can handle many concurrent producers. It needs a log(level, message) API that doesn't block producers for long, batches and flushes messages in submission order to a file or stdout, survives high contention without losing messages on shutdown, and exposes flush() and close() methods. Walk through your synchronization approach, how you'd handle back-pressure if the queue fills up, and how you'd test for race conditions.

System DesignTechnical Trade-offsAPI & Integrations

Author's notes

I started with the obvious answer: a lock-protected queue and a single background writer thread draining it.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (e.g., expected throughput, latency bounds, shutdown semantics) and then propose a design with a lock-free or low-contention queue (e.g., MPSC) feeding a dedicated writer thread that batches and flushes in order. Discuss synchronization primitives (mutex vs. atomics), back-pressure strategies (bounded queue with blocking, dropping, or caller-provided policy), and a testing plan that includes stress tests, race detectors, and deterministic shutdown validation.

Pro tip: Emphasize that you would make back-pressure policy configurable (e.g., block, drop, or sample) because different use cases demand different trade-offs, and show you understand that 'thread-safe' doesn't mean 'lock-free'—sometimes a well-placed mutex is simpler and fast enough.

1. Clarify requirements and constraints

Ask about expected message rate, acceptable latency for log(), whether messages can be dropped, and shutdown guarantees. This shapes the design and shows you avoid premature optimization.

2. Design the core architecture

Propose a bounded concurrent queue (e.g., Michael-Scott queue or ring buffer) where producers enqueue and a single consumer thread dequeues, batches, and writes to the output. Explain how ordering is preserved (FIFO queue) and how flush() and close() coordinate with the consumer.

3. Detail synchronization and back-pressure

Describe the synchronization primitives (e.g., mutex + condition variable for blocking, or atomics for lock-free) and how back-pressure is applied when the queue is full. Discuss options: blocking the producer (with timeout), dropping the message, or invoking a user-supplied policy.

4. Handle shutdown and flush semantics

Explain how close() ensures all queued messages are written before returning, using a sentinel or atomic flag to signal the consumer. flush() should block until the queue is drained and the output is synced.

5. Outline testing for race conditions

Propose stress tests with many threads, use race detectors (e.g., ThreadSanitizer), and verify message integrity and ordering. Include tests for back-pressure, shutdown under load, and flush correctness.

Key Points to Mention

Use a bounded queue to prevent unbounded memory growth and to enable back-pressure.
Preserve submission order by using a FIFO queue and a single consumer thread.
Minimize producer blocking by using non-blocking enqueue when possible, or short critical sections.
Provide configurable back-pressure policies (block, drop, sample) to suit different use cases.
Ensure close() drains the queue and flushes before returning, using a sentinel or atomic shutdown flag.
Test with high concurrency, race detectors, and deterministic shutdown scenarios to catch race conditions.

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