I started with pod replicas and node pools, talked about using a Deployment with a reasonable replica count and keeping stateless app nodes separate from anything touching persistent workloads.
Start by acknowledging the constraints (monolith, no microservices, no aggressive scaling) and emphasize simplicity, reliability, and cost-efficiency. Then walk through each area (deployment, networking, config/secrets, CI/CD, rollouts) with concrete choices and trade-offs, focusing on operational maturity over complexity.
Pro tip: Highlight that a monolith on Kubernetes still benefits from Kubernetes primitives like Deployments, Services, and ConfigMaps, but avoid over-engineering with service meshes or complex autoscaling. Emphasize observability and safe rollouts as key to stability.
Restate the scenario: single monolith, no microservices, no aggressive scaling. Define goals: simplicity, reliability, cost-efficiency, and developer productivity.
Use a Kubernetes Deployment with multiple replicas for high availability, spread across nodes/availability zones. Consider a single container per pod, with resource requests/limits and liveness/readiness probes.
Expose the monolith via a Service (ClusterIP) and an Ingress controller for external traffic. Use TLS termination at Ingress, and consider a CDN for static assets if applicable.
Use ConfigMaps for non-sensitive config and Secrets for sensitive data, mounted as volumes or env vars. Consider external secret management (e.g., Vault, AWS Secrets Manager) for rotation and audit.
Use a CI pipeline to build, test, and push container images. Deploy via GitOps (e.g., ArgoCD) or Helm. Use rolling updates with maxSurge/maxUnavailable, and consider blue-green or canary for critical changes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through HPA vs VPA and why VPA is actually more interesting for a monolith since you're not splitting load across services.
Structure your answer around the three pillars: resource management, autoscaling, and stateful dependencies. For each, explain how you'd configure Kubernetes primitives (requests/limits, HPA/VPA, StatefulSets/Operators) and the trade-offs involved, tying decisions back to Figma's real-time, latency-sensitive workload.
Pro tip: Emphasize that stateful services like databases and caches are often better run outside Kubernetes or via operators, and that autoscaling must consider downstream bottlenecks—not just pod metrics.
Explain how you'd right-size CPU/memory requests based on profiling, set limits to prevent noisy neighbors, and use tools like Vertical Pod Autoscaler for recommendations.
Describe using Horizontal Pod Autoscaler (HPA) with custom metrics (e.g., request latency, queue depth) and Cluster Autoscaler for node scaling, while considering cold-start and scaling delays.
Discuss running databases/caches via Operators (e.g., Postgres Operator, Redis Operator) or managed services, and object storage via CSI drivers or external services like S3.
Cover PersistentVolumes, StatefulSets for stable identities, and headless services for direct pod access, ensuring data durability and low-latency connectivity.
Highlight the importance of observability (metrics, logs, tracing) to tune resource settings and autoscaling policies continuously based on real usage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining observability for a monolith as the three pillars—metrics, logs, and traces—and explain how you'd instrument the system to gain insight into its internal state. Then, outline a structured incident response process (detect, triage, mitigate, resolve, learn) and a disaster recovery strategy (backups, replication, failover, testing). Emphasize how these practices reduce mean time to detection and recovery.
Pro tip: Tie observability directly to incident response by showing how high-quality telemetry enables faster root cause analysis and reduces downtime. Mention that you'd regularly test disaster recovery plans through game days or chaos engineering to ensure they work under real conditions.
Explain that observability is about understanding the system's internal state from its outputs. For a monolith, this means instrumenting the application to emit metrics (e.g., request rates, error rates, latency), structured logs, and distributed traces.
Describe how you would implement instrumentation using libraries like OpenTelemetry, and aggregate data in tools like Prometheus for metrics, ELK for logs, and Jaeger for traces. Ensure data is correlated (e.g., trace IDs in logs) for effective debugging.
Outline a clear incident response workflow: detection via alerts, triage to assess severity, mitigation to restore service, resolution to fix root cause, and a post-mortem to learn and improve. Include roles like incident commander and communication protocols.
Detail a DR plan: regular backups (full and incremental), replication across availability zones, automated failover, and a recovery time objective (RTO) and recovery point objective (RPO). Emphasize testing through drills.
Explain how you'd use observability data and incident post-mortems to iteratively improve system resilience, such as adding new alerts, refining runbooks, and conducting chaos experiments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Frame the decision as a context-dependent trade-off between operational simplicity and independent scalability, explicitly tying it to Figma's collaborative, real-time, multi-tenant product. Then describe concrete, measurable signals (e.g., deployment bottlenecks, scaling pain, team friction) that would indicate when evolution is necessary, and emphasize incremental, reversible steps rather than a big-bang rewrite.
Pro tip: Show that you understand Figma's specific architecture—a monolithic Ruby on Rails app with a separate C++ multiplayer service—and that they deliberately kept the monolith for most features while extracting only the real-time engine. This demonstrates you can apply the trade-off to their actual context, not just recite textbook pros and cons.
Start by acknowledging that the right answer depends on team size, product stage, scaling needs, and deployment frequency. Ask clarifying questions if needed, but for Figma, assume a fast-growing product with a small-to-medium engineering team and a real-time collaborative core.
Compare monolith vs. microservices on dimensions like development velocity, operational complexity, scalability, fault isolation, and team autonomy. Be specific: monoliths are simpler to develop and deploy early on but can become a bottleneck; microservices enable independent scaling and team ownership but add distributed system overhead.
List measurable pain points that justify moving to microservices: e.g., deploy times exceeding 30 minutes, inability to scale a single component independently, teams blocked on shared codebase, or a specific feature (like real-time collaboration) needing different performance characteristics.
Recommend extracting services only when a clear boundary and need exist, using patterns like strangler fig or modular monolith. Emphasize that evolution should be driven by business needs, not hype, and that you'd start with a pilot service to validate the approach.
Connect your answer to Figma's real-time collaboration, multi-player editing, and need for low-latency performance. Mention that they already have a hybrid model (monolith + C++ multiplayer service) and that this shows pragmatic evolution rather than dogma.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging that migration should be incremental and driven by clear signals, then outline a phased approach that prioritizes low-risk extractions. Focus on defining service boundaries using domain-driven design, establishing clear data ownership and API contracts, and adapting team workflows to align with the new architecture.
Pro tip: Emphasize the importance of a strangler fig pattern and feature flags to decouple deployment from release, allowing for safe rollbacks and gradual traffic shifting. Also, highlight the need for observability and automated testing to catch regressions early.
Use signals like high change frequency, scaling bottlenecks, or team autonomy needs to select a bounded context for extraction. Start with a low-risk, well-understood domain that has minimal dependencies.
Apply domain-driven design to define bounded contexts and ensure each service owns its data. Avoid shared databases; instead, use APIs or events for data exchange.
Establish clear, versioned API contracts (REST or gRPC) and consider asynchronous messaging for decoupling. Use consumer-driven contract testing to ensure compatibility.
Use the strangler fig pattern to gradually route traffic to the new service while keeping the monolith operational. Employ feature flags and canary releases to control rollout and enable quick rollback.
Align teams to service ownership, fostering autonomy and accountability. Establish cross-team collaboration for API evolution and shared concerns like observability and security.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.