Welcome back on the Docker and AWS series by TinyStacks

In this Article, we will create a simple CRUD API using a Flask Application , Docker, Postgres

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  • Create a folder
  • Create requirements.txt
  • Create app (~50 loc)
  • Create Dockerfile
  • Create docker-compose.yml
  • Run database
  • Check database
  • Run python app
  • Check that the table has been created
  • Test endpoints (Postman)
  • Test Get All endpoint
  • Create a record(x3)
  • Get a record
  • Update record
  • Delete record

Create folder and step into it

You can create a folder in anyway that you prefer. If you use a terminal you can type:

mkdir  flask-crud-api

Then, step into the folder:

cd flask-crud-api

Then, open this folder with your favorite IDE. If you use Visual Studio Code, you can type:

code .

Now we’re ready to get started coding our Flask Crud API application with the help of GitHub Copilot!

Create requirements.txt

First of all, we need to define the dependent Python libraries for our application. The standard method in Python is to create a requirements.txt file and list our dependencies there.

So create this file called "requirements.txt". (If you have the Material Icon Theme, it will show a nice little Python icon. It’s a nice to spot typos!)

Then we can type the dependencies for our project:


Those dependencies are:

  • flask: The Python Framework
  • psycopg2-binary: To create the connection with the Postgres Database
  • Flask-SQLAlchemy: Help generate SQL queries without writing them manually

Create app (~50 loc)

At the root level, create a file called app.py. We will write our crud API app in about 50 lines of code!

Let’s specify the libraries we’ll use:

from flask import Flask, request, jsonify
from flask_sqlalchemy import SQLAlchemy
import os

Next, define the Flask app and how to run it:

app = Flask(__name__)

if __name__ == '__main__':

Define an environment variable as a string and initialize the SQLAlchemy instance to handle the Postgres database:

app.config['SQLALCHEMY_DATABASE_URI'] = os.environ.get('DATABASE_URL')
db = SQLAlchemy(app)

Now let's define our data model. We’ll create a class named Item with just title and content as properties. We’ll also add an auto-incremental Integer named id. This will act as the primary key for our table.

class Item(db.Model):
  id = db.Column(db.Integer, primary_key=True)
  title = db.Column(db.String(80), unique=True, nullable=False)
  content = db.Column(db.String(120), unique=True, nullable=False)

  def __init__(self, title, content):
    self.title = title
    self.content = content

Now a little bit of magic: with this line we let SQLAlchemy to synchronize with the Postgres database. This will create our databasetable automatically for us!


Define REST endpoints

Now we need to implement our CRUD endpoints. CRUD stands for:

  • READ

These are the basic functions of every application.

To retrieve a singleitem, we define this function:

@app.route('/items/<id>', methods=['GET'])
def get_item(id):
  item = Item.query.get(id)
  del item.__dict__['_sa_instance_state']
  return jsonify(item.__dict__)

To get all the items in the database, we define this function:

@app.route('/items', methods=['GET'])
def get_items():
  items = []
  for item in db.session.query(Item).all():
    del item.__dict__['_sa_instance_state']
  return jsonify(items)

To create a new item:

@app.route('/items', methods=['POST'])
def create_item():
  body = request.get_json()
  db.session.add(Item(body['title'], body['content']))
  return "item created"

To update an existing item:

@app.route('/items/<id>', methods=['PUT'])
def update_item(id):
  body = request.get_json()
    dict(title=body['title'], content=body['content']))
  return "item updated"

To delete an existing item:

@app.route('/items/<id>', methods=['DELETE'])
def delete_item(id):
  return "item deleted"

That's it! and in less than 60 lines of coding (included new lines)!

Create Dockerfile

A Dockerfile is a text file to define a set of commands to create an image. Starting from this image, we will run our python containers

Let's create a file called Dockerfile (capital D, no extension).

We could create of course a file with a different name.But this is the default one that Docker uses. If we use it, we don't have to specify a name for the file when we build our Docker container image.

This is the final file:

FROM python:3.6-slim-buster

COPY requirements.txt .

RUN pip install -r requirements.txt

COPY . .


CMD ["flask", "run", "--host=", "--port=80"]

Let's explain briefly what's going on here:

  • FROM: Set the baseImage to use for subsequent instructions. FROM must be the first instruction in a Dockerfile.
  • COPY: Copy files or folders from source to the dest path in the image's filesystem. The first COPY copies the requirements.txt file inside the filesystem of the image; the second one copies everything else.
  • RUN: Execute any commands on top of the current image as a new layer and commit the results. In this case, we are runningpip` to install the Python libraries we need.
  • EXPOSE: Informs Docker of the port we will use at runtime. (PRO tip: this line is not really needed. It makes the intent of the Dockerfile clear and facilitates the translation to the docker-compose.yml file)
  • CMD: Provide defaults for an executing container. If an executable is not specified, then ENTRYPOINT must be specified as well. There can only be one CMD instruction in a Dockerfile.

Create docker-compose.yml

Now that we have created the Dockerfile, let's create the docker-compose.yml file to make our life easier.

This is the final file:

version: '3.9'

container_name: pythonapp
image: pythonapp
build: .
- "80:80"
- DATABASE_URL=postgresql://postgres:postgres@db:5432/postgres
- db

container_name: db
image: postgres:12
- "5432:5432"
- POSTGRES_USER=postgres
- POSTGRES_DB=postgres
- pgdata:/var/lib/postgresql/data

pgdata: {}

Let's explain what's happening line by line:

  • version: '3.9' is the current version of the docker-compose.yml file.
  • services: The top-level entry of our docker-compose.yml file. The services are basically the containers.
  • pythonapp: The Python application we just wrote
  • container_name: Defines a custom name for our application. It’s the equivalent of using the --name option at the command line when we run docker run.
  • image: Rhe image for this service (container). Here, we are defining a custom name just to use it locally. If we want to push our containerto a public or private registry (a place to store Docker Images, e.g. Docker hub), we need to change the tag of the image (basically the name). We don’t need to do that now.
  • build: We need this option if we are using our custom image and not an existing one. The dot after the semicolon is the path of the Dockerfile, and it means "here is where I’mrunning the docker-compose.yml file". Please note that the docker-compose.yml file and the Dockerfile are at the same level.
  • ports: A list of ports we want to expose to the outside. A good practice is to make the content a quoted string.
  • environment: Key-value pairs. Here, we use them to define our custom URL to connect to the Postgres database.
  • depends_on: Express dependency between services. Service dependencies cause the following behaviors:
    • docker-compose up starts services in dependency order. In the following example, db and redis are started before web.
    • docker-compose up automatically includes a service’s dependencies. In the example below, docker-compose up web also creates and starts dband redis.
    • docker-compose stop stops services in dependency order. In the following example, web is stopped before db and redis.
  • db: Service for the Postgres database.
  • container_name: The default name for this service, also called db
  • image: postgres:12 : We will not use our custom image in this case but an existing one, the one the Postgres team has created and pushed for us on Docker Hub.
  • ports: A list of ports we want to expose to the outside. A good practice is to wrap this content in a quoted string.
  • environment: Here we define three environment variables for the Postgres service. The keys are not arbitrary, but are the ones defined in the official Postgres image. We can, of course, define the values of these environment variables (this is why the Postgres team has given them to us, to use them!).
  • volumes: Here we use a named volume called pgdata. the part before the ':' is the name of the volume, and the part on the right of the ':' is the destination path

At the end of the file, we define the actual volume named pgdata.

Run the database service locally

To run the database service locally, we can type:

docker compose up -d db

the -d option stands for detached, to leave out terminal available after running this container.

You can check the status of the running container by typing:

docker ps

Check the database

To step inside the Postgres container we will use 2 different approaches.

First approach: directly from the Command line

docker exec -it db psql -U postgres

But if we type


We will see this:

did not find any relations

This is correct, because we haven't run our Python container yet.

To exit the psql process, type:


Or alternatively, just


Run Python app

To run your Python application, type:

docker compose up --build pythonapp

The --build option is to build your application before running it. It's not useful the first time you run this command because Docker is going to build the image anyway. It becomes useful after you run docker compose up multiple times and you’ve made some changes on your app.

Note: If you build an image using the same tag (name), the previous image will become a so-called "dangling image", with <none> <none> as the repository and tag. To remove them, you can type docker image prune and then y to confirm.

if you see something like this, you have successfully launched your Python Flask application:

Now you can once again check the running containers:

docker ps -a

(Side note: don't mind the "created" value, it's just me removing/stopping the containers for demo-purposes! You should see them both running with a status of some minutes ago.)

Check the table has been created

If you step again inside the Postgres container now, using the command:

docker exec -it db psql -U postgres

  • and you type:


  • you will see the table has been created automatically, without calling any endpoint!

This was possible because of the line (around 22) on the app.py file:


Test endpoints (using Postman)

Let's test this simple application! We will use Postman, but you can use any REST API testing tool that you prefer.

Get All

Let's get all the items:

Create one (x3)

Now let's create some new items

Apologies for my lack of imagination :)

Get one

To get a single item, you can just make a GET request at the endpoint /item/<id>, where <id> is the unique ID of the item that you previously created.

For example, to get the item with id 2:

(Please note that we are not handling errors correctly in this example. If that id doesn't exist, we’ll get an error directly from the application and we won’t show an error message to the end-user.)

Update one

To update an existing item, you can make a PUT request using the <id> of the item in the body:

Delete one

Finally, we can delete an existing item from the database. We can make a DELETE request and appending an existing <id> at the end of the url:

If you get all the items again, this will be the result:

Test the final status using the prompt

You can test the final status also directly on the db:

docker exec -it db psql -U postgres

and then run the psql command (don't forget the final ';'):

select * from item;

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