Start by clarifying requirements and scale (e.g., daily uploads, viewership, regions) to ground your design. Then walk through the end-to-end pipeline: upload, transcoding, storage, CDN delivery, metadata/search, user interactions, and recommendations. Finally, dive into cross-cutting concerns like caching, rate limiting, multi-region, encryption, and cost trade-offs, justifying each choice with Apple’s priorities (privacy, performance, efficiency).
Pro tip: Emphasize how you would leverage Apple’s ecosystem (e.g., iCloud, APNs, Apple Silicon for transcoding) and privacy-preserving techniques (on-device processing, differential privacy) to differentiate your design. Also, proactively discuss cost modeling and trade-offs (e.g., storage vs. compute) to show business acumen.
Ask questions to understand expected traffic (daily uploads, concurrent viewers, storage growth), geographic distribution, and key features (e.g., live streaming, monetization). Estimate scale to inform design decisions.
Outline the upload flow (resumable uploads, chunking), transcoding pipeline (parallel jobs, multi-bitrate outputs), storage strategy (object store for videos, CDN for delivery), and metadata service (database, search index).
Describe APIs for likes, comments, subscriptions, and a recommendations interface (e.g., feed generation, personalization). Discuss how to handle high read/write throughput and eventual consistency.
Explain caching layers (CDN, Redis), rate limiting (token bucket, per-user quotas), multi-region availability (active-active, data replication), encryption (at rest, in transit, DRM), and cost trade-offs (storage tiers, spot instances).
Recap the architecture, highlighting key trade-offs (e.g., consistency vs. availability, cost vs. latency) and how they align with Apple’s values (privacy, performance, efficiency).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and scale, then propose a high-level architecture that separates the write path (ingestion, versioning, moderation) from the read path (search, browse, caching). Dive into data modeling for categories, attributes, and variants, and explain how you maintain consistency between the primary store and search index while supporting multi-tenancy and schema evolution.
Pro tip: Emphasize idempotency and eventual consistency in ingestion and indexing, and discuss how you would handle failures and backfills without downtime. Show awareness of Apple's scale and the need for low-latency reads globally.
Ask about expected QPS, data volume, number of tenants, latency requirements, and consistency needs. Define functional and non-functional requirements to scope the design.
Model categories as a hierarchy, attributes as flexible schemas, and product variants as combinations of attributes. Choose a primary store (e.g., SQL for transactions, NoSQL for scale) and discuss multi-tenant isolation strategies.
Outline an ingestion pipeline that validates, deduplicates, versions, and moderates items. Use queues for decoupling, idempotent writes, and a versioning scheme to track changes.
Explain how to build and maintain search indexes (e.g., Elasticsearch) with incremental updates, localization, and consistency with the primary store. Discuss caching strategies for read APIs.
Describe how to handle schema changes without downtime, including versioned schemas, dual writes, and backfill strategies using change data capture or batch jobs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.