Bi Taksi Location Service

This project aims to find nearest drivers across given customer location. To do that, It's using 'haversine' algorithm after mongodb's geospatial query

Hexagonal Architecture

The idea of Hexagonal Architecture is to put inputs and outputs at the edges of our design. Business logic should not depend on whether we expose a REST or a GraphQL API, and it should not depend on where we get data from — a database, a microservice API exposed via gRPC or REST, or just a simple CSV file.

The pattern allows us to isolate the core logic of our application from outside concerns. Having our core logic isolated means we can easily change data source details without a significant impact or major code rewrites to the codebase.

Run on Local Machine

docker-compose up

Endpoints

GET /nearest-driver-location

Example Usages

# Request
curl --location --request GET 'http://localhost:8083/find-nearest' \
--header 'Content-Type: application/json' \
--data-raw '{
    "longitude": 40.94289771,
    "latitude": 28.0390297
}'

// Response
{
  "coordinates": [
    40.946104,
    28.035588
  ]
}

Introducing Dependencies

To store geolocations, I choose MongoDB.
For mocking I choose 'mocgken' package. It's official package and I can easily find a solution for that I'm facing a problem right now.
For unit testing I choose 'testify' package. It suits my usecase very-well and It's very simple to use tho. Also popular enough to worry about community-things.
For server framework I choose 'echo' package. It has great documentation and It's coming with built-in examples. I might use fasthttp over echo but fasthttp has some internal problems such as response object is not designed for concurrent usage.
For calculating distance I choose 'umahmood/haversine' package. It has Its own tests so I don't need to re-package it.

Deployment

The deployment is not in scope but if I wanted to do it then surely I use github actions for heroku

Author

👤 Emirhan Ataman

ashkan90 / bit-driver-location-service