The scope was massive and I think I spent too long on the upload pipeline before realizing I hadn't even touched search.
Start by clarifying functional and non-functional requirements (e.g., scale, latency, consistency) and then design a high-level architecture covering video ingestion, storage, search, and streaming. Dive into key components like CDN, transcoding pipeline, and search indexing, discussing trade-offs (e.g., SQL vs NoSQL, push vs pull for uploads).
Pro tip: Emphasize trade-offs and justify decisions based on requirements; for example, explain why you might choose a NoSQL database for metadata vs a relational one, and how you'd handle hot content with CDN caching.
Ask about scale (users, videos, uploads per second), latency expectations, consistency needs, and supported features (e.g., recommendations, user-generated content).
Sketch the main components: client apps, API gateway, video upload service, transcoding pipeline, storage (object store), metadata DB, search service, CDN, and streaming service.
Detail the upload flow (chunked uploads, resumability), transcoding (formats, parallel processing), search (indexing, querying), and streaming (adaptive bitrate, CDN integration).
Discuss partitioning, replication, caching, and fault tolerance. Explain how to handle spikes (e.g., popular content) and ensure high availability.
Compare design choices (e.g., monolithic vs microservices, SQL vs NoSQL, push vs pull for uploads) and justify selections based on requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with a REST-ish approach for the client side and a separate upload API with multipart support for large files.
Start by clarifying the platform's core entities (content, uploaders, clients) and then design two distinct API surfaces: one for content uploaders (write-heavy, authenticated, with chunked uploads and metadata) and one for clients (read-heavy, public, with caching and search). Emphasize RESTful resource design, versioning, and security (OAuth for uploaders, API keys for clients).
Pro tip: Show awareness of HubSpot's API-first culture by mentioning developer experience: consistent error formats, pagination, rate limiting, and SDKs. Also, discuss how you'd evolve the APIs without breaking clients (e.g., additive changes, deprecation policies).
Ask clarifying questions about scale, content types, and client needs. Identify core entities: Content, Uploader, Client, and relationships.
Define endpoints for uploading content (e.g., POST /contents with multipart or chunked upload), updating metadata, and managing uploads. Include authentication (OAuth 2.0) and validation.
Define read-oriented endpoints (e.g., GET /contents, GET /contents/{id}) with filtering, pagination, and search. Consider caching headers and CDN integration for performance.
Cover versioning (e.g., /v1/), rate limiting, error handling, and monitoring. Discuss how to secure each API surface differently.
Explain how you'd handle changes (e.g., additive changes, deprecation) and trade-offs between REST, GraphQL, or gRPC for different use cases.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through a relational DB for video metadata and user records, object storage for raw and encoded video files, and a search index for the catalog.
Start by clarifying the scope and requirements of the system, then describe a high-level data model that captures the core entities and relationships. For each component, justify your storage technology choice based on access patterns, consistency needs, and scalability, and discuss trade-offs. Conclude by summarizing how the choices align with the overall architecture and business goals.
Pro tip: Show that you understand the trade-offs between different storage technologies and that you can make pragmatic decisions based on the specific use case, rather than defaulting to a single database for everything. Mention how you would evolve the data model and storage choices as the system grows.
Ask clarifying questions about the system's scale, read/write patterns, consistency requirements, and latency expectations. This ensures your answer is tailored to the actual problem.
Describe the main entities, their attributes, and relationships. Use a simple diagram or verbal description to convey the structure, focusing on key access patterns.
For each part of the system (e.g., transactional data, user profiles, analytics, search), propose a suitable storage technology (e.g., relational DB, document store, key-value store, data warehouse, search engine) and justify why it fits.
Explain the trade-offs of your choices (e.g., consistency vs. availability, latency vs. cost) and mention alternative technologies you considered and why you rejected them.
Recap how your data model and storage choices support the system's requirements and business objectives, and mention potential future evolutions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was actually the part I felt best about.
Start by clarifying requirements and scale, then walk through the pipeline stage by stage: ingestion, storage, transcoding, packaging, and delivery. Emphasize trade-offs at each stage, such as synchronous vs. asynchronous processing, storage tiers, and encoding formats, and tie decisions back to user experience and cost.
Pro tip: Show you understand that video processing is a long-running, failure-prone workflow by discussing idempotency, retries, and dead-letter queues. Also mention how you'd monitor pipeline health and handle backpressure during traffic spikes.
Ask about scale (uploads per day, file sizes), latency expectations, supported formats, and budget. This frames the design and shows you avoid premature optimization.
Describe how the raw file is uploaded (e.g., direct-to-S3 with pre-signed URLs), validated (format, size, virus scan), and stored in a landing zone. Mention chunked/resumable uploads for large files.
Explain how an event triggers a workflow (e.g., SQS, Step Functions) that transcodes the video into multiple resolutions/bitrates, generates thumbnails, and extracts metadata. Highlight idempotency and retry logic.
Cover how transcoded outputs are packaged (HLS/DASH), stored in a CDN-backed origin, and made available via a manifest. Discuss storage classes and lifecycle policies.
Describe how the video is served to users (CDN, adaptive bitrate streaming) and how you monitor pipeline health, errors, and performance metrics. Mention alerting and auto-scaling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Multi-region CDN with edge caching for popular content, origin shield to reduce back-pressure, and cache key design around rendition and segment.
Start by clarifying requirements: global audience, low-latency streaming (live or VOD), and scale. Then propose a multi-tier CDN architecture with edge caching, origin shielding, and adaptive bitrate streaming, while discussing trade-offs like cost, consistency, and cache invalidation.
Pro tip: Emphasize that for live streaming, latency is dominated by the segment size and protocol (e.g., LL-HLS vs. DASH), so caching strategy must align with segment duration and TTLs. Also, mention that HubSpot’s global presence likely requires a multi-CDN approach to avoid vendor lock-in and improve resilience.
Ask about stream type (live vs. VOD), expected concurrency, geographic distribution, and latency targets (e.g., sub-5s for live). This shapes the entire design.
Propose a multi-tier CDN with edge PoPs, mid-tier caching, and origin shielding. Use anycast or DNS-based routing to direct users to the nearest edge.
For VOD, cache segments at edge with long TTLs and versioned URLs. For live, use short TTLs (e.g., 2-6s) and cache segments only until the next segment is available; consider push-based caching for popular streams.
Use HTTP-based streaming (HLS/DASH) with adaptive bitrate to handle variable network conditions. For low latency, consider LL-HLS or CMAF with chunked transfer.
Cover cost vs. performance (multi-CDN, edge compute), cache invalidation for live (e.g., purge on segment completion), and monitoring (QoE metrics, cache hit ratio).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about eventual consistency for catalog updates being acceptable, strong consistency for billing or access control, and regional failover for streaming.
Start by clarifying the system's requirements and constraints, then systematically discuss how reliability, fault tolerance, and scaling interact and often conflict. Use concrete examples from your experience to illustrate trade-offs and propose a balanced design that aligns with business goals.
Pro tip: Frame trade-offs in terms of business impact and user experience, not just technical metrics. This shows you understand that engineering decisions serve product and customer needs.
Ask questions to understand the system's scale, SLAs, budget, and consistency needs. This ensures your trade-off analysis is grounded in real priorities.
Specify target availability (e.g., 99.9%), recovery time objectives (RTO), and recovery point objectives (RPO). Discuss how redundancy, replication, and failover mechanisms achieve these.
Consider vertical vs. horizontal scaling, read vs. write scaling, and data partitioning. Explain how scaling choices affect reliability and fault tolerance (e.g., more nodes increase complexity but improve availability).
Discuss specific trade-offs like consistency vs. availability (CAP theorem), latency vs. durability, and cost vs. resilience. Use examples to show how you'd decide based on requirements.
Recommend an architecture that meets the most critical goals, and describe mitigations for the trade-offs (e.g., circuit breakers, graceful degradation, chaos testing).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Covered request-level rate limiting at the API gateway, metrics on stream start failures and buffering ratios, and flagging abnormal upload volumes as a basic abuse signal.
Start by clarifying the service's scale, critical user journeys, and business impact of abuse, then outline a layered defense-in-depth strategy covering observability, rate limiting, and abuse prevention. Emphasize trade-offs between security, user experience, and operational cost, and tie your choices to HubSpot's multi-tenant SaaS context.
Pro tip: Frame your answer around protecting the customer experience and revenue, not just blocking attacks—show you understand that overly aggressive limits can hurt legitimate users and that monitoring must drive iterative tuning.
Ask about scale (requests per second, tenants), critical endpoints, compliance needs, and what 'abuse' means for this product. This ensures your strategy is grounded in real constraints.
Propose metrics (latency, error rates, request rates per tenant/IP), logging with structured events, and distributed tracing. Include alerting thresholds and dashboards for real-time visibility.
Choose algorithms (token bucket, sliding window) and enforcement points (API gateway, service mesh, application). Discuss per-user, per-IP, and per-tenant limits, and how to handle bursts and return proper 429 responses.
Layer defenses: authentication/authorization, CAPTCHAs, IP reputation, anomaly detection, and machine learning for behavioral analysis. Include incident response and automated mitigation (e.g., temporary bans).
Explain how you'll measure effectiveness, tune limits based on false positives/negatives, and balance security with user experience and cost. Mention gradual rollouts and feature flags.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.