Start by clarifying requirements and scale, then design the core platform (problem repository, code execution, submission evaluation) before tackling the contest mode with real-time leaderboards and anti-cheating. Focus on the unique challenges of secure code execution at scale and fair competition, and discuss trade-offs in your design.
Pro tip: Emphasize the importance of sandboxing and resource isolation for untrusted code execution, and propose a scalable architecture using container orchestration (e.g., Kubernetes) with autoscaling. Also, highlight the need for idempotent submissions and exactly-once processing to handle retries and ensure fairness.
Ask about expected user base, problem volume, submission frequency, and contest scale. Define functional and non-functional requirements, including latency, availability, and security.
Outline the main components: web/app servers, problem service, submission service, code execution service, database, cache, and message queue. Sketch a block diagram and data flow.
Explain how to securely run untrusted code: sandboxing (containers, gVisor, Firecracker), resource limits, and isolation. Discuss test case execution, result aggregation, and scaling the execution fleet.
Detail contest features: registration, problem set, timed submissions, real-time leaderboard, and scoring. Address challenges like concurrent submissions, leaderboard updates, and anti-cheating measures.
Discuss scaling strategies (horizontal scaling, caching, sharding), fault tolerance, and monitoring. Highlight trade-offs between consistency and availability, and between security and performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: what kind of code, who submits it, and what the expected scale is. Then propose a secure, isolated execution environment using sandboxing and resource limits, and discuss trade-offs between isolation strength, performance, and operational complexity.
Pro tip: Emphasize defense in depth: combine multiple isolation layers (e.g., containers, seccomp, network restrictions) and assume any single layer can fail. Also mention the importance of monitoring and auditing for abuse.
Ask about the nature of the code (e.g., scripts, full programs), expected load, latency requirements, and security constraints. This shapes the entire design.
Select an appropriate sandboxing technology (e.g., containers, VMs, gVisor, WebAssembly) based on the required isolation level and performance overhead.
Apply CPU, memory, disk, and network quotas to prevent resource exhaustion and denial-of-service. Use cgroups, ulimits, or similar mechanisms.
Restrict filesystem access, disable unnecessary syscalls, and isolate networking. Use read-only mounts, seccomp profiles, and network policies.
Implement logging, monitoring, and alerting for suspicious activity. Continuously update the sandbox as new threats emerge.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Coming from a frontend background, DB questions always make me sweat a little.
Start by clarifying the system's requirements—data model, read/write patterns, scale, consistency needs, and latency—then evaluate database options against those criteria. Recommend a primary database with justification, and mention complementary databases if needed, explaining trade-offs.
Pro tip: Acknowledge that no single database is perfect; show maturity by discussing how you'd evolve the choice as the system scales, and mention operational considerations like team expertise and managed services.
Ask questions to understand the system's data model, access patterns, scale, consistency, and latency requirements.
List 2-3 database types (e.g., relational, document, wide-column, graph) that could fit the requirements.
Compare candidates on scalability, consistency, query flexibility, and operational complexity.
Choose a primary database and justify it, noting any complementary databases for specific use cases.
Explain how the choice might change as the system grows, and mention monitoring and migration strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.