Find Closest DashMart - Locate Nearest Convenience Store for a Dasher

Question

## Round 1 - Coding

## Problem

DashMart is DoorDash's convenience store network. Given a dasher's current location and a list of DashMart locations with their open/closed status and inventory, find the closest open DashMart that carries all items in a given shopping list.

```python
def find_closest_dashmart(
    dasher_location: tuple[float, float],
    dashmarts: list[dict],
    shopping_list: list[str]
) -> dict | None:
    # dashmarts[i] = {
    #   "id": int,
    #   "location": (float, float),
    #   "is_open": bool,
    #   "inventory": list[str]  # product names in stock
    # }
    # Returns the dict of the closest eligible DashMart, or None if none qualify
    ...

def euclidean(a: tuple, b: tuple) -> float: ...
```

```
Example:
dasher_location = (0, 0)
dashmarts = [
    {"id": 1, "location": (1,1), "is_open": True,  "inventory": ["milk", "eggs"]},
    {"id": 2, "location": (2,2), "is_open": True,  "inventory": ["milk", "eggs", "bread"]},
    {"id": 3, "location": (0,1), "is_open": False, "inventory": ["milk", "eggs", "bread"]},
]
shopping_list = ["milk", "bread"]
# DashMart 3 is closed; DashMart 1 lacks bread; DashMart 2 qualifies
find_closest_dashmart(...) -> dashmarts[1]  # id=2
```

## Follow-ups

1. How do you handle partial inventory? Should you return the store with the most matching items?
2. How would you cache inventory data given it changes frequently?
3. How would you extend this to multi-stop routing (dasher visits two DashMarts for different items)?
4. What data structure speeds up the "carries all items" check for large inventories?

Find Closest DashMart - Locate Nearest Convenience Store for a Dasher

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