Reducing search iteration over millions of data The problem goes like this, with a story.

You have an application that contains a search field. When you search some input, there's an auto-complete component that pops up showing up similar results to your input. 

Each result is a location in the database and this is the structure of location:

id | name                    | lat  | lon
-----------------------------------------
0  | New york street test 51 |34.123| 38.245


Every location has lat and lon coordinates.

Your system has a function that knows how to receive 2 locations and return the distance in km.

You also have a table called stores which contains id, name, lat, lon of the shop.

When you select a place, your application finds 20 closest stores to the location that you have selected.

The problem

Everything works fine for a start, but now the application has grown and filled up with new stores in many locations and you have over one 500 million stores registered in the database. Every search is heavy and overloading the system and it takes a few seconds to a minute to get a result.

How can you make it better, efficient? 

Spoiler - My idea to answer the question


   My idea is to split the world map into regions matrix of size Constant where constant is set to 10km x 10km. Each region will have its ID and every shop you add through your platform will get the region id of where you add it to. When you search for lat and lon, the system will go through all regions searching what region contains that coordinate and return the region id, and then you can grab all shops of that region id. But wait, you have a problem, what if there's a closer store in the neighbor regions? Simple, you have 4 points of your region's corners, you can look up for all neighbor regions and compare if there's a closer store. And if you don't have enough stores and you must get 10 stores out of all regions, use the 4 coordinates of the corners as-well and go recursive on all regions until you reach 10 closest.


I am open and happy to hear everyone's thoughts. Note this is not a real story, it was asked in some interview and I took it very interesting to see if there are better and more interesting solutions.
You are on the right track. In an interview situation, this should be a good answer.  Another answer would be to pre-calculate list of 20 stores in ETL (or CRUD services or DB triggers) and store list of N closest stores in master-detail table.

I have implemented these two solutions in projects :


In database, create indices on lat and lon columns AND add a where clause with a bounding box. 

SELECT distance_function(cur_lat,cur_lon,lat,lon) as distance

WHERE (lat between cur_lat -1 and cur_lat +1) AND (lon between cur_lon
      -1 and cur_lon +1)

(+Order by, top 20 rows clause depending on SQL dialect)


This solution cuts search space to a bounding box. This operation in log(n) in time complexity (due to B+ tree indices in DB)

For 100 million rows, results should be available in less than 1 ms. 

Choice of +-1 four bounding box is arbitrary in this example. In reality, you will have to store this box with each row (1000s of stores in 1 mile in NYC, London etc; 1 store in 100 miles in other locations)


Implement QuadTree https://en.wikipedia.org/wiki/Quadtree . In this context it is similar to your solution but takes care of extreme cases where density of stores varies across geographical regions.