Design Uber Eats-style search function

Design the search function for a large-scale food delivery platform similar to Uber Eats. A user opens the app and types queries such as: - Restaurant names (e.g., "Joe's Pizza"), - Cuisines (e.g., "Thai", "Sushi"), - Dish names (e.g., "cheeseburger", "pad thai"). The system must return a ranked list of relevant restaurants (and possibly dishes) that can deliver to the user's location. ### Requirements **Functional requirements** 1. Users can search by: - Free-text query (restaurant name, cuisine, or dish). - Current location; results should be only from restaurants that deliver to that area. 2. Support filters and sort options, such as: - Open now / closed. - Price range, rating, delivery time estimate, distance. 3. Results should be ranked by: - Text relevance. - Distance / delivery ETA. - Restaurant popularity and rating. - (Optionally) user personalization. 4. Data changes that affect search: - New restaurants and menus being added. - Menu or availability changes (e.g., items out of stock, restaurant closed/open) should reflect in search reasonably quickly. **Non-functional requirements** (assume a large city / multi-city deployment) 1. Low latency: P95 search latency under ~200 ms from the service (excluding network to client). 2. High availability: search should continue to work even if some components fail. 3. Scalability: support millions of users, tens/hundreds of thousands of restaurants, and high queries per second. Describe the **high-level design** of such a system, covering: - Overall architecture and major components. - Data modeling and indexing strategy. - Query flow from the client to returned results. - How you keep the search index fresh when restaurants/menus/availability change. - Approaches to ranking and personalization. - Scaling and reliability considerations.