← Atlassian Interview Insights
I started with cache locality which felt safe, but then they kept pushing and I realized there were like four other angles I hadn't touched.
Start by explaining why round-robin load balancing works for stateless requests but fails for collaborative editing, which requires session affinity and shared state. Then discuss the specific problems: inconsistent document state, lost updates, and poor user experience. Finally, propose solutions like sticky sessions, a shared data store, or a pub/sub system to synchronize edits.
Pro tip: Acknowledge that sticky sessions alone are insufficient for true collaboration; you also need a mechanism to broadcast edits to all users editing the same document, regardless of which server they are connected to. Mentioning operational complexity and failure modes (e.g., server crash losing unsaved edits) shows depth.
Explain that collaborative editing involves multiple users concurrently modifying the same document, requiring real-time synchronization and consistent state across all participants.
Point out that round-robin distributes each request independently, so subsequent requests from the same user may hit different servers, leading to inconsistent views and lost edits.
List issues such as stale data, edit conflicts, inability to maintain WebSocket connections, and difficulty in implementing operational transformation or CRDTs.
Suggest alternatives like sticky sessions with a shared session store, a centralized collaboration service, or a distributed pub/sub system to propagate changes.
Compare solutions in terms of scalability, latency, consistency, and complexity, and mention how to handle server failures without data loss.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through consistent hashing on doc ID to keep requests for the same document landing on the same server.
Start by briefly recapping the specific issues you identified with round-robin routing for this workload, then propose targeted solutions that address each issue. Structure your answer by walking through each solution, explaining the trade-offs and why it's appropriate for the given context (e.g., Atlassian's scale, multi-tenancy, and API integrations).
Pro tip: Acknowledge that no single solution is perfect; show maturity by discussing how you would measure the impact of your changes and iterate. Mention that you'd validate with load testing and monitor key metrics like latency, error rates, and fairness across tenants.
Briefly summarize the problems with round-robin for this workload, such as ignoring server capacity, session affinity, or tenant isolation. This sets the stage for your solutions.
For each issue, suggest a concrete alternative or enhancement, e.g., weighted round-robin, least connections, consistent hashing, or adaptive load balancing. Explain how it addresses the issue.
For each solution, outline the trade-offs: complexity, latency, consistency, and operational overhead. Relate them to Atlassian's context (e.g., multi-tenant SaaS, high availability).
Explain which solution you would implement first and why, considering factors like impact, effort, and risk. Describe a phased rollout or A/B testing approach.
Describe how you would measure success (e.g., latency percentiles, error rates, tenant fairness) and how you would iterate based on monitoring and feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.