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https://github.com/phaniteja5789/containerized-data-ingestion-api-with-fastapi-and-mongodb

Developed an API using FastAPI that gets the data and stores the data in MongoDB. The entire process runs in containers using Docker
https://github.com/phaniteja5789/containerized-data-ingestion-api-with-fastapi-and-mongodb

docker docker-image docker-volumes dockercompose dockerfile fastapi mongodb

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Developed an API using FastAPI that gets the data and stores the data in MongoDB. The entire process runs in containers using Docker

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# FastAPI_Docker

Developed an API using FastAPI that gets the data and stores the data in MongoDB. The entire process runs in containers using Docker 



**Steps**

  1.) 

      Developed an API that is used to get the details from a Public API to retrieve the data.

      The Public API used is **"https://randomuser.me/api/"**



   2.)

       Once the details is fetched, we will store the details in MongoDB.

       

   3.) 

       Run the application inside container using **Docker**

      

 Installed Modules

  

    1.) pymongo ==> Mongo DB Driver for Python

    2.) fastapi ==> API is developed using FastAPI

    3.) requests ==> In order to get the data from the Public API

    4.) uvicorn ==> To run the fastapi on the Uvicorn server

    

    All the modules are present in the "Requirements.txt"

    

    Dockerfile and DockerCompose files are present in the Code Repository

    

  Dockerfile

  

    FROM python:latest ==> Fastapi is developed in python. So using Python as Base Image



    WORKDIR /code ==> To make "code" as a directory inside container as working directory



    COPY ./requirements.txt /code/requirements.txt ==> copying the requirements file from local to container working directory



    RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt ==> Installing all the modules present in requirements.txt in docker container



    COPY ./app /code/app ==> copying all the source code from local to docker container file system



    CMD ["uvicorn","app.sourcecode:app","--host","0.0.0.0","--port","8000"] ==> Running the fastAPI server using CMD with options

    

  Docker Commands

  

    **docker build -t fastapi .** ==> fastapi == Image name, . == Dockerfile to build an image

   

  Docker Compose

      

      version: '3'

      services:

        mongodb:

          image: mongo

          ports:

            - 27017:27017

          volumes:

            - mongovolume:/var/lib/mongodb/data

        fastcontainer:

          image: fastapi

          ports:

            - 8000:8000  

      volumes:

        mongovolume: {}

    

    mongodb and fastcontainer are the names of the containers

    mongo,fastapi are the image names

    ports to bind the localhost with docker container ports

    volumes to persist the data, so that data is persisted even the container got deleted

    

   Docker Command

   

    docker-compose -f dockercompose.yaml up ==> To start the containers present inside the dockercompose.yaml file

    docker-compose -f dockercompose.yaml down ==> To stop the containers present inside the dockercompose.yaml file

    

    docker exec -it container_name bash ==> To run the container in bash mode

    

    docker ps ==> To list the containers

    

  Output of the details is fetched it will be stored in MongoDB. The data is persisted using **Docker Volumes**

  

  The Data stored in MongoDB

  

image