This is where I spent most of the time and also where I fumbled most visibly.
Start by clarifying requirements and scale, then propose a layered isolation architecture using containers or microVMs for compute, per-tenant volumes for file systems, and network policies for traffic separation. Finally, detail a quota enforcement mechanism with cgroups, rate limiting, and monitoring to prevent noisy neighbors.
Pro tip: Emphasize that isolation and quotas must be enforced at the infrastructure level, not just in application code, and discuss the trade-offs between stronger isolation (e.g., microVMs) and lower overhead (e.g., containers) based on tenant trust levels.
Ask about tenant types (trusted vs. untrusted), expected concurrency, and performance SLAs to tailor the design. This shows you avoid over-engineering and focus on real constraints.
Propose using containers (e.g., Docker with gVisor) or microVMs (e.g., Firecracker) per tenant session, with each execution in a separate sandbox. Discuss trade-offs between isolation strength and startup latency.
Use per-tenant persistent volumes or ephemeral storage mounted only in their sandbox, and enforce network policies (e.g., deny-all by default, allow only necessary egress) via CNI plugins or service meshes.
Apply cgroups (CPU, memory, disk I/O) and network bandwidth limits per sandbox, and use a quota manager to track and throttle usage. Mention monitoring and alerting for quota breaches.
Discuss techniques like overcommitment with limits, priority classes, and autoscaling to handle load spikes. Highlight the need for a control plane to orchestrate sandboxes and quotas across nodes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked for a second on the injection mechanism.
Start by clarifying the platform's constraints (multi-tenancy model, scale, compliance needs) and then propose a layered architecture that separates secret storage, configuration management, and runtime injection. Emphasize trade-offs between isolation, operational complexity, and cost, and describe how you'd handle rotation, auditing, and failure modes.
Pro tip: Show you understand that secrets and config are different beasts: secrets need encryption, rotation, and audit trails, while config can be versioned and cached. Also mention that per-tenant isolation must extend to the control plane, not just data plane, to avoid cross-tenant leakage.
Ask about tenant scale, isolation requirements (e.g., regulatory, noisy neighbor), and whether tenants bring their own secrets or the platform generates them. This shapes the entire design.
Decide between a centralized secret store (e.g., HashiCorp Vault, AWS Secrets Manager) with per-tenant paths, or a sidecar/agent model that injects secrets at runtime. Consider encryption at rest, access control, and audit logging.
Use a hierarchical config system (e.g., global defaults, tenant overrides) stored in a versioned, auditable store. Ensure config changes are validated and rolled out safely (e.g., canary, feature flags).
Inject secrets and config into tenant workloads via environment variables, mounted volumes, or APIs, ensuring strict namespace isolation and least-privilege access. Avoid hardcoding or leaking secrets in logs.
Plan for rotation, revocation, disaster recovery, and monitoring. Automate rotation and provide self-service for tenants where appropriate, while maintaining audit trails.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pre-warming a pool of idle sandboxes was my first answer and they seemed to expect it.
Start by clarifying the scale and requirements, then propose a multi-layered architecture that separates tenant isolation from resource pooling. Focus on techniques like pre-warming, snapshotting, and lightweight sandboxes to reduce cold-start latency, and discuss trade-offs between isolation, cost, and performance.
Pro tip: Emphasize that cold-start optimization is not just about faster boot times but also about predictive scaling and workload-aware scheduling. Mention that you'd measure p99 latency and cost per tenant to guide decisions.
Ask about tenant isolation level, expected traffic patterns, latency SLOs, and budget. This shows you understand the problem space before jumping to solutions.
Propose a tiered isolation model (e.g., shared pools for small tenants, dedicated for large) using containers, microVMs, or gVisor. Discuss how to route and schedule tenants efficiently.
Describe techniques like keeping a pool of pre-initialized sandboxes, using memory snapshots (e.g., CRIU), and lazy-loading dependencies. Highlight trade-offs between memory overhead and latency.
Use historical data and ML to predict tenant demand, pre-scale resources, and cache common dependencies. Mention the importance of monitoring and feedback loops.
Summarize trade-offs: isolation vs. density, cost vs. latency, complexity vs. maintainability. Propose key metrics (cold-start p99, cost per tenant, resource utilization) to validate the design.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start with a high-level diagram of the editor architecture, then drill into each component: LSP integration, file synchronization, and terminal streaming. Emphasize the bidirectional communication and how you handle state consistency and latency.
Pro tip: Mention that you would use a single WebSocket connection for all real-time communication to reduce overhead, and discuss how you handle reconnection and state recovery.
Describe the main components: browser-based editor, backend server, language server, and terminal process. Explain how they interact via WebSockets and HTTP.
Explain how the backend acts as a proxy between the browser and the language server. Discuss initialization, capability negotiation, and message routing.
Detail how file changes are captured in the editor, sent to the backend, and persisted. Mention debouncing, conflict resolution, and using a virtual file system.
Describe how terminal processes are spawned on the backend, and how their output is streamed to the browser via WebSockets. Cover handling of binary data and flow control.
Discuss how you would scale the architecture, handle multiple users, and ensure reliability with reconnection logic and state recovery.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.