This is basically five questions rolled into one.
Start by clarifying requirements and scale (e.g., 100M users, 1B repos, 10M events/day), then sketch a high-level architecture with core services and data stores. Dive into the data model and API design, then systematically address each component: sharding, caching, feed fan-out, search, and blob storage, explaining trade-offs and scaling strategies.
Pro tip: Emphasize the read-heavy nature of the platform and how caching and denormalization are used to meet latency SLAs, while ensuring write scalability through sharding and asynchronous processing. Show awareness of consistency trade-offs, like eventual consistency for feeds vs. strong consistency for repository metadata.
Ask questions to understand expected scale (users, repos, events), read/write ratio, latency requirements, and consistency needs. Define functional and non-functional requirements.
Outline core services (User, Repo, Issue/PR, Feed, Search) and their interactions. Design the data model with entities like User, Repository, Issue, PullRequest, Follow, Star, and Activity, considering relationships and access patterns.
For each component, explain sharding strategy (e.g., by user_id or repo_id), read/write paths, caching layers (e.g., Redis for hot data), feed fan-out approach (push vs. pull), search indexing (e.g., Elasticsearch), and blob storage (e.g., S3 with CDN).
Define key API endpoints (e.g., GET /users/{id}, POST /repos, GET /feed) and discuss trade-offs such as consistency vs. availability, normalization vs. denormalization, and cost vs. performance.
Identify potential bottlenecks (e.g., hot shards, feed fan-out storms) and propose solutions like consistent hashing, rate limiting, and asynchronous processing. Summarize how the system scales to high throughput.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.