Reddit·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Reddit SWE interview that centered on a chat messaging function and quickly turned into a deep dive on caching. Not a lot of breadth but they really pushed on one topic.

Questions Asked (1)

What would you do if get_chat_messages(id) is being called too frequently and causing performance issues?

System DesignTechnical Trade-offsAPI & Integrations

Author's notes

Said cache pretty quickly and felt good about it, but then they kept going.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the scenario and gathering data to understand the call pattern and performance impact. Then propose a layered solution that addresses both immediate mitigation and long-term architectural improvements, emphasizing trade-offs and collaboration with stakeholders.

Pro tip: Show that you think about the problem holistically: not just caching or rate limiting, but also whether the API design itself encourages inefficient usage. Suggest working with clients to adopt better patterns, which demonstrates system thinking and empathy for other teams.

1. Clarify and Measure

Ask questions to understand the frequency, source, and impact of the calls. Propose instrumenting the endpoint to collect metrics like QPS, latency, and error rates.

2. Identify Root Cause

Determine why the calls are frequent: is it a polling client, a missing cache, a bug, or an inefficient API design? Analyze access patterns and client behavior.

3. Propose Short-Term Mitigations

Suggest immediate fixes such as caching (server-side or client-side), rate limiting, or batching to reduce load while a long-term solution is developed.

4. Design Long-Term Solutions

Recommend architectural changes like introducing a pub/sub model, WebSockets, or a more efficient API (e.g., GraphQL) to reduce polling. Discuss trade-offs of each option.

5. Implement and Monitor

Outline a plan to roll out changes incrementally, monitor impact, and iterate. Emphasize communication with clients and stakeholders throughout.

Key Points to Mention

Caching strategies (e.g., Redis, CDN, in-memory) and cache invalidation policies
Rate limiting and throttling mechanisms (e.g., token bucket, leaky bucket)
Batching and pagination to reduce the number of calls
Alternative architectures like WebSockets, Server-Sent Events, or push notifications
API design improvements: GraphQL, gRPC, or REST best practices
Monitoring and observability tools (e.g., Prometheus, Grafana, logging) to track performance
Trade-offs between consistency, latency, and cost
Collaboration with client teams to change usage patterns

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