Welcome back! In the previous lesson, we explored defining schemas and serializing data with Marshmallow. Now, we will take it a step further by handling incoming data from a request and automatically validating it using Marshmallow. This is crucial for ensuring that the data your application receives adheres to expected formats and standards.

By the end of this lesson, you will be able to create a Flask endpoint that handles incoming user data, validates it using Marshmallow schemas, and adds it to a mock database.

Here’s a reminder of the initial setup, including the Flask app instance and our mock database:

Python
Copy
1from flask import Flask
2
3# Initialize a Flask app instance
4app = Flask(__name__)
5
6# Mock database as a list of dictionaries
7database = [
8    {"id": 1, "username": "cosmo", "email": "cosmo@example.com"},
9    {"id": 2, "username": "jake", "email": "jake@example.com"},
10    {"id": 3, "username": "emma", "email": "emma@example.com"}
11]

To handle incoming data properly, we need to specify what valid data looks like using a Marshmallow schema. Let's focus on making certain fields mandatory, like username and email.

Here's an updated version of our schema that enforces these requirements:

Python
Copy
1from marshmallow import Schema, fields
2
3# Define a Marshmallow schema for user data
4class UserSchema(Schema):
5    id = fields.Int()
6    username = fields.Str(required=True)
7    email = fields.Email(required=True)
8
9# Create an instance of the User schema
10user_schema = UserSchema()

In this schema, setting required=True for the username and email fields ensures that both fields must be provided and follow their respective data types (string and email format).

On the other hand, the id field is not marked as required because it will be generated automatically when a new user is added. This setup helps keep our user data accurate and complete.

Now that we've defined our schema, we need to validate incoming data against this schema!

We'll use Marshmallow's load method to load and validate incoming JSON data. If the data is invalid, Marshmallow will raise a ValidationError. Let's see how this works in the context of a Flask route:

Python
Copy
1from flask import request, jsonify
2from marshmallow import ValidationError
3
4# Define a route to handle user creation
5@app.route('/users', methods=['POST'])
6def create_user():
7    try:
8        # Validate the incoming JSON data using the User schema
9        user_data = user_schema.load(request.get_json())
10    except ValidationError as err:
11        # Return validation errors as a JSON response
12        return jsonify(error = err.messages), 400

• request.get_json() retrieves the incoming JSON data from the request body.
• user_schema.load(request.get_json()) attempts to load and validate this data against the UserSchema.
• If validation fails, a ValidationError is raised, and we catch it in the except block, returning the error messages as a JSON response with a 400 status code.

Once the data is validated, we can proceed to add it to our mock database. Here is the complete code snippet:

Python
Copy
1from flask import request, jsonify
2from marshmallow import ValidationError
3
4# Define a route to handle user creation
5@app.route('/users', methods=['POST'])
6def create_user():
7    try:
8        # Validate the incoming JSON data using the User schema
9        user_data = user_schema.load(request.get_json())
10    except ValidationError as err:
11        # Return validation errors as a JSON response
12        return jsonify(error = err.messages), 400
13
14    # Generate a new ID by finding the maximum existing ID and adding 1
15    new_id = max(user['id'] for user in database) + 1
16    user_data["id"] = new_id
17
18    # Add the new user to the mock database
19    database.append(user_data)
20
21    # If validation is successful, return a positive status and the created user
22    return jsonify(new_user), 201

• If validation is successful, the validated data is added to the mock database.
• A unique id is generated and assigned to the new user.
• User is appended to the database list.
• The created user is returned as a JSON response with a 201 status code.

If the incoming request does not meet the validation criteria defined in the UserSchema, the ValidationError exception will be raised and the response will detail the specific validation errors.

For example, if both username and email are missing, the response will look like this:

JSON
Copy
1{
2    "error": {
3        "email": [
4            "Missing data for required field."
5        ],
6        "username": [
7            "Missing data for required field."
8        ]
9    }
10}

This response is returned with a 400 status code, indicating invalid data provided by the client.

Additionally, if the email field contains an invalid email address, the response will look like this:

JSON
Copy
1{
2    "error": {
3        "email": [
4            "Not a valid email address."
5        ]
6    }
7}

This response is also returned with a 400 status code, indicating invalid data provided by the client.

In this lesson, we extended our Marshmallow skills by focusing on handling incoming data and its validation. Specifically, we:

• Defined a UserSchema with required fields using Marshmallow.
• Demonstrated how to validate incoming JSON data within a Flask route.

With these concepts in mind, you're now ready to tackle the practice exercises ahead. These exercises will provide hands-on experience and help solidify your understanding.

Keep practicing, and happy coding!