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

Senior

Jun 2026

Summary

Netflix SWE interview with a strong engineering focus. The questions pushed past surface-level design into real production scenarios, which I wasn't fully prepared for.

Questions Asked (1)

If a server goes down in production, how do you handle the cache to prevent data loss?

System DesignTechnical Trade-offs

Author's notes

This is where I started sweating.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the scenario—what type of cache (e.g., write-through, write-behind, read-through) and what 'data loss' means (cache loss vs. source-of-truth loss). Then explain your strategy: treat the cache as ephemeral, ensure the source of truth (database) is durable, and design for cache failure without data loss, using patterns like cache-aside and write-through with proper fallbacks.

Pro tip: Emphasize that caches should never be the source of truth; data loss in cache is acceptable if the database is durable. At Netflix, they use EVCache with write-through and async replication, so a server down doesn't lose data—it just reduces cache capacity.

1. Clarify the cache architecture

Ask whether the cache is write-through, write-behind, read-through, or cache-aside, and whether it's a distributed cache like Redis or a local cache. This determines the risk of data loss.

2. Identify the source of truth

Confirm that the database or primary storage is the source of truth and is durable. The cache should only hold a copy of data, so its failure doesn't cause permanent loss.

3. Handle cache failure gracefully

Explain how the system falls back to the database when the cache is unavailable, using patterns like circuit breakers, retries, and cache-aside to repopulate the cache.

4. Prevent data loss in write-behind caches

If using write-behind, discuss strategies like write-ahead logging, replication, or synchronous writes to ensure data is persisted before acknowledging writes.

5. Monitor and recover

Describe monitoring for cache health, alerting on failures, and automated recovery to rebuild the cache from the database without data loss.

Key Points to Mention

Cache is not the source of truth; database is durable and authoritative.
Write-through vs. write-behind trade-offs: write-through ensures data is written to DB before cache, write-behind risks loss if cache fails before flush.
Cache-aside pattern: application reads from cache, on miss reads from DB and populates cache.
Replication and sharding in distributed caches (e.g., Redis Cluster, EVCache) to tolerate node failures.
Fallback mechanisms: circuit breakers, retries, and degraded mode when cache is down.
Monitoring and alerting for cache hit ratio, latency, and node health to detect issues early.

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