Design personalized news aggregation service

# System Design: Personalized News Aggregation Service Design a large-scale news aggregation system similar to Google News or other news aggregator products. The key functional requirements are: - The system should **collect news articles** from many different news providers (e.g., CNN, BBC, local newspapers) using: - Web crawlers (for sites without APIs). - RSS feeds or publisher APIs when available. - The system should **normalize and store** collected articles with consistent metadata: - Title, body, URL, publish time, source, author. - Category (e.g., politics, sports, tech) and language. - The system should support **logged-in users** who have: - Subscriptions to specific publishers/sources. - Category/topic preferences (e.g., more sports, less politics). - For each logged-in user, the system should display a **personalized news feed**, taking into account: - User’s subscriptions. - User’s category/topic preferences. - Freshness and popularity of articles. Non-functional requirements and constraints (you may make reasonable assumptions, but be explicit): - Large scale: potentially tens of millions of daily active users. - High read throughput: most requests are for reading the news feed. - Reasonable freshness: new articles should appear in user feeds within a few minutes of being published. - High availability and low latency for feed retrieval (e.g., p95 < 200–300 ms). In your design, cover at least the following aspects: 1. **Requirements and APIs** - Clarify functional and non-functional requirements. - Define main APIs or endpoints for clients (web/mobile) to fetch the news feed and manage preferences. 2. **High-level Architecture** - Major components and services (e.g., crawler, content ingestion pipeline, storage, feed/personalization service). - How data flows from publishers to the end-user feed. 3. **Data Storage and Indexing** - How you will store articles, metadata, and user preferences. - How to support efficient querying (by category, recency, popularity, user interests). 4. **Crawling & Ingestion Pipeline** - How crawlers/RSS/API consumers are scheduled and scaled. - How content is parsed, deduplicated, categorized, and filtered. 5. **Personalization & Ranking** - How to build a personalized feed based on user subscriptions and category preferences. - Basic ranking logic (you can assume heuristic or ML-based ranking, but describe the approach conceptually). 6. **Scalability, Caching, and Availability** - Strategies to handle high read traffic and keep latency low. - Use of caching, CDNs, sharding, and replication. 7. **Freshness, Consistency, and Trade-offs** - How to balance freshness of news with system load and cache efficiency. - Any relevant consistency or CAP-theorem trade-offs you would make. Explain your design step-by-step and justify key trade-offs.