euginelethal / docker-nginx-heroku

This is a simple search app built with Django, PostgreSQL, Docker, Gunicorn.Demo

This is search app built with django, postgres Demo .

Want to learn how to build this?

Project workflow;

- Dockerize a Django app
- Deploy and run a Django app in a Docker container on Heroku
- Configure GitLab CI to deploy Docker images to Heroku
- Manage static assets with WhiteNoise
- Configure Postgres to run on Heroku
- Create a production Dockerfile that uses multistage Docker builds
- Use the Heroku Container Registry and Build Manifest for deploying Docker to Heroku

# pull official base image
FROM python:3.8-alpine

# set work directory
WORKDIR /app

# set environment variables
ENV PYTHONDONTWRITEBYTECODE 1
ENV PYTHONUNBUFFERED 1
ENV DEBUG 0

# install psycopg2
RUN apk update \
    && apk add --virtual build-deps gcc python3-dev musl-dev \
    && apk add postgresql-dev \
    && pip install psycopg2 \
    && apk del build-deps

# install dependencies
COPY ./requirements.txt .
RUN pip install -r requirements.txt

# copy project
COPY . .

# add and run as non-root user
RUN adduser -D myuser
USER myuser

# run gunicorn
CMD gunicorn hello_django.wsgi:application --bind 0.0.0.0:$PORT

Development

Run locally:

$ docker build -t web:latest .
$ docker run -d --name django-heroku -e "PORT=8765" -e "DEBUG=1" -p 8007:8765 web:latest

Verify http://localhost:8007/ works as expected:

This project uses the trigram similarity search approach which is supported by Postgres consecutive characters. You can measure the similarity of two strings by counting the number of trigrams that they share. This approach turns out to be very effective for measuring the similarity of words in many languages. In order to use trigrams in PostgreSQL, you will need to install the pg_trgm extension first. Execute the following command from the shell to connect to your database:

psql cities

Then, execute the following command to install the pg_trgm extension:

CREATE EXTENSION pg_trgm;

Let's edit the view and modify it to search for trigrams.

class SearchResultsView(ListView):
    model = City
    template_name = 'search_results.html'
    
    def get_queryset(self): 
        query = self.request.GET.get('q')
        object_list1= City.objects.annotate(similarity=TrigramSimilarity('name', query),).filter(similarity__gt=0.1).order_by('-similarity')
        object_list2=City.objects.annotate(similarity=TrigramSimilarity('state', query),).filter(similarity__gt=0.1).order_by('-similarity')
        object_list= object_list1|object_list2   # merge querysets
        return object_list

Open http://127.0.0.1:8000/ in your browser and test different searches for trigrams. The following example displays a hypothetical typo in the django term which exists in my database, showing search results for yango : Search results for the term "yango" Now you have a powerful search engine built into your project. You can find more information about full-text search at https://docs.djangoproject.com/en/3.0/ ref/contrib/postgres/search/ .

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Stop then remove the running container once done:

$ docker stop django-heroku
$ docker rm django-heroku

Production

Uses the Build Manifest approach for Heroku deployments.

Deployment Process

Deploying Django to Heroku With Docker

set the Stack of your app to container:

$ heroku stack:set container -a secret-spire-12445

Add a heroku.yml file to the project root:

build:
  docker:
    web: Dockerfile

Here, we're just telling Heroku which Dockerfile to use for building the image.

Along with build, you can also define the following stages:

setup is used to define Heroku addons and configuration variables to create during app provisioning.
release is used to define tasks that you'd like to execute during a release.
run is used to define which commands to run for the web and worker processes.

build:
      docker:
        web: Dockerfile
    run:
      web: gunicorn hello_django.wsgi:application --bind 0.0.0.0:$PORT
    release:
      image: web
      command:
        - python manage.py collectstatic --noinput
         

We are using Gunicorn a Python WSGI HTTP Server for production deployment

With that, initialize a Git repo and create a commit.

Then, create the Heroku app:

$heroku create

Push the code up to Heroku to build the image and run the container:

$ git push heroku master