Have you ever wondered how your ride-sharing app finds riders only nearby instead of the entire city? How does this happen at an algorithmic level?<\/p>\n\n\n\n
You might think the system simply compares your (the user’s) geographical coordinates with those of drivers. But this approach requires scanning the coordinates of all drivers in the database to then find the ones around you.<\/p>\n\n\n\n
And scanning each of those coordinates is a two-dimensional search because geographical coordinates consist of latitudes\/longitude pairs.<\/p>\n\n\n\n
So the database has to check both axes separately, which further slows down the operation. Why? Because most databases can only handle 1-dimensional data. Pretty complex and time-consuming, I know.<\/p>\n\n\n\n
That\u2019s the problem Gustavo Niemeyer noticed back in 2008 and presented the geohashing algorithm.<\/p>\n\n\n\n
Geohashing takes a 2D pair of coordinates and turns them into a short alphanumeric string called a geohash. Since this string is linear, databases have to query less data to find nearby points of a given point.<\/p>\n\n\n\n
This isn\u2019t the only benefit of geohashing. It offers a lot more, but not without introducing a few inefficiencies along the way.<\/p>\n\n\n\n
Read this comprehensive guide on geohashing to explore the good and the bad about geohashing. But more especially, read this blog to learn how the algorithm works. I have explained and demonstrated the steps to give you a thorough understanding.<\/p>\n\n\n\n
Let\u2019s start.<\/p>\n\n\n\n
Table of Contents<\/p>\n<\/span><\/div>\n