I started with the data models and APIs which felt safe, but I spent way too long there and then had to rush through the execution pipeline.
Start by clarifying functional and non-functional requirements, then sketch a high-level architecture that separates the web app, submission queue, and sandboxed execution workers. Dive deep into the most challenging components—sandboxing, real-time leaderboards, and contest isolation—while discussing trade-offs and scalability.
Pro tip: Emphasize security and isolation in the code execution sandbox, as it's the highest-risk component; mention using containers with seccomp and cgroups, and consider ephemeral VMs for stronger isolation. Also, discuss how to handle contest spikes with autoscaling and queue prioritization.
Ask about scale (users, submissions per second), supported languages, contest frequency, and latency requirements. Define functional requirements: problem browsing, submission, verdicts, history, contests, leaderboards.
Outline components: API gateway, web servers, problem service, submission service, execution workers, result store, and leaderboard service. Use a message queue to decouple submission from execution.
Explain how to run untrusted code securely: containerization (Docker) with resource limits, seccomp, network isolation, and read-only filesystems. Discuss trade-offs between containers and VMs for security vs. performance.
Choose databases: relational for problems/submissions, Redis sorted sets for real-time leaderboards. Discuss sharding, caching, and eventual consistency for contest rankings.
Address scaling: autoscaling workers, queue prioritization during contests, rate limiting, and multi-region deployment. Discuss trade-offs like consistency vs. availability for leaderboards, and cost vs. isolation for sandboxing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is the part I actually felt decent about.
Start by clarifying the requirements: what languages, expected load, latency, and security guarantees. Then outline a layered architecture that isolates untrusted code using containers or microVMs, enforces resource limits, and scales horizontally with a job queue. Discuss trade-offs between isolation strength, performance, and cost.
Pro tip: Emphasize defense in depth: even with strong sandboxing, assume a breach and design for least privilege, network isolation, and rapid patching. Also, mention that you'd start with a simple, secure baseline (e.g., gVisor) and only optimize when needed.
Ask about supported languages, expected submission volume, latency requirements, and security/compliance needs. This shapes the entire design.
Outline stages: submission intake, compilation (if needed), sandboxed execution, result collection, and cleanup. Use a job queue to decouple and scale.
Compare options like containers (Docker), microVMs (Firecracker), and user-space kernels (gVisor). Discuss trade-offs in security, performance, and complexity.
Set CPU, memory, disk, and network limits. Apply seccomp, AppArmor, and read-only filesystems. Ensure no network access unless required.
Use a pool of workers with autoscaling. Implement monitoring, logging, and alerting for failures, abuse, and performance bottlenecks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by explaining the problem: large artifacts shouldn't flow through your API servers due to bandwidth, memory, and timeout constraints. Then describe a design where the client requests a signed URL from your backend, which grants temporary, scoped access to object storage (e.g., S3) for direct upload or download. Finally, discuss security considerations like expiration, permissions, and validation.
Pro tip: Mention that you can use presigned POST policies to enforce size limits and content-type restrictions, preventing abuse and ensuring data integrity. Also, highlight the importance of logging and monitoring signed URL usage for audit and debugging.
Explain why large artifacts (test data, logs) shouldn't be proxied through your API: high bandwidth costs, server memory pressure, and request timeouts.
Describe how the client requests a signed URL from your backend, which authenticates and authorizes the request, then generates a time-limited URL using your cloud provider's SDK.
Discuss setting short expiration times, restricting HTTP methods (PUT for upload, GET for download), and scoping to specific object keys or prefixes.
For uploads, the client uses the signed URL to PUT the file directly to storage; for downloads, the client uses a signed GET URL. Optionally, use multipart uploads for very large files.
After upload, the client notifies your backend (or you use storage events) to trigger processing. For downloads, ensure the client has proper permissions and the URL is generated on-demand.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where I wish I'd been more structured.
Start by clarifying the requirements and constraints of the high-traffic competition, then propose a scalable architecture that handles concurrent participants and submission spikes. Focus on horizontal scaling, caching, asynchronous processing, and database optimization, while discussing trade-offs and monitoring.
Pro tip: Emphasize the importance of load testing and gradual rollout to validate scalability, and mention specific techniques like sharding and rate limiting to handle spikes gracefully.
Ask about expected traffic patterns, peak concurrency, submission rate, latency requirements, and budget constraints to tailor your solution.
Propose a scalable architecture using load balancers, stateless services, and horizontal scaling to distribute traffic across multiple instances.
Introduce asynchronous processing with message queues (e.g., Kafka, RabbitMQ) to decouple submissions from processing, and use rate limiting to protect backend services.
Discuss database scaling strategies such as sharding, read replicas, and caching (e.g., Redis) to handle high read/write loads.
Explain how to monitor system health with metrics and logging, and discuss trade-offs between consistency, availability, and cost.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I talked about idempotent submission handling so retries don't double-judge, circuit breakers around the execution cluster, and structured logging with trace IDs per submission.
Start by framing the platform's core characteristics (live video commerce, real-time bidding, high concurrency) and then systematically address reliability, security, and observability concerns. For each area, identify the top risks and propose concrete trade-offs, emphasizing how you'd balance competing priorities like latency vs. consistency or security vs. user experience.
Pro tip: Tie every trade-off back to business impact—e.g., 'We'd accept slightly higher latency in the bidding service to ensure strong consistency and prevent overselling, because trust in the auction is paramount.' This shows you think like an owner, not just an engineer.
Briefly restate the platform's key features (live streaming, real-time auctions, payments) and ask clarifying questions about scale, latency requirements, and compliance needs. This ensures your answer is tailored and shows you gather requirements before diving in.
Discuss failure modes (e.g., stream outages, bid service downtime, payment failures) and strategies like redundancy, graceful degradation, and idempotency. Highlight trade-offs such as consistency vs. availability (CAP theorem) and cost vs. resilience.
Cover authentication/authorization, data encryption, fraud prevention, and secure payment handling. Trade-offs include security vs. user friction (e.g., MFA) and latency vs. thorough validation.
Propose logging, metrics, tracing, and alerting for key user journeys. Discuss trade-offs like sampling rate vs. cost, and real-time monitoring vs. batch analysis.
Conclude by prioritizing trade-offs based on business impact and user experience, showing you can make pragmatic decisions under constraints.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.