In this post, let’s explore another data structure to find nearby restaurants on Yelp or Google Maps.

A quadtree is a data structure that is commonly used to partition a two-dimensional space by recursively subdividing it into four quadrants (grids) until the contents of the grids meet certain criteria (see the first diagram). 

A quadtree is an in-memory data structure and it is not a database solution. It runs on each LBS (Location-Based Service, see last week’s post) server, and the data structure is built at server start-up time.

The second diagram explains the quadtree building process in more detail. The root node represents the whole world map. The root node is recursively broken down into 4 quadrants until no nodes are left with more than 100 businesses. 

How to get nearby businesses with quadtree?
- Build the quadtree in memory. 

- After the quadtree is built, start searching from the root and traverse the tree, until we find the leaf node where the search origin is. 

- If that leaf node has 100 businesses, return the node. Otherwise, add businesses from its neighbors until enough businesses are returned.

Update LBS server and rebuild quadtree
- It may take a few minutes to build a quadtree in memory with 200 million businesses at the server start-up time. 

- While the quadtree is being built, the server cannot serve traffic. 

- Therefore, we should roll out a new release of the server incrementally to a small subset of servers at a time. This avoids taking a large swathe of the server cluster offline and causes service brownout. 

If you enjoyed this post, you might like our system design interview books as well.

SDI-vol1: https://amzn.to/3tK0qQn

SDI-vol2: https://amzn.to/37ZisW9