Popular Restaurant: Find Top-K Restaurants by Weighted Rating and Review Count

Question

## Problem

You are given a list of restaurants. Each restaurant has a `name`, a `rating` (float, 1.0-5.0), and a `review_count` (int). Return the top `k` most popular restaurants.

Popularity score is defined as: `score = rating * log(review_count + 1)`.

Break ties alphabetically by name (ascending).

```python
def top_restaurants(restaurants: list[dict], k: int) -> list[str]:
    # restaurants: [{"name": str, "rating": float, "review_count": int}]
    # return: list of k restaurant names
    pass
```

**Example:**
```
Input:
  restaurants = [
    {"name": "Zen Ramen", "rating": 4.8, "review_count": 200},
    {"name": "Bite Me",   "rating": 4.5, "review_count": 500},
    {"name": "Cafe A",    "rating": 4.9, "review_count": 10}
  ], k = 2

Scores:
  Zen Ramen: 4.8 * log(201) ~ 25.5
  Bite Me:   4.5 * log(501) ~ 27.8
  Cafe A:    4.9 * log(11)  ~ 11.7

Output: ["Bite Me", "Zen Ramen"]
```

## Follow-ups

1. How would you handle a restaurant with 0 reviews?
2. What if the scoring formula changes — how do you make this extensible?
3. If the dataset has 10 million restaurants and k is small, what data structure avoids sorting everything?
4. How would you handle real-time updates to ratings without resorting the full list?

Popular Restaurant: Find Top-K Restaurants by Weighted Rating and Review Count

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