Request validation in Flask - Mini Project: Build & Apply

Request Validation in Flask

📖 Scenario: You are building a simple web application using Flask. Users will submit their name and age through a form. You want to make sure the data received is valid before processing it.

🎯 Goal: Build a Flask app that validates incoming request data for name and age. The app should check that name is a non-empty string and age is a positive integer.

📋 What You'll Learn

Create a Flask app with a route to accept POST requests

Extract name and age from the request JSON

Add validation to check name is not empty and age is a positive integer

Return a JSON response indicating success or error

💡 Why This Matters

🌍 Real World

Validating user input is essential in web apps to prevent errors and security issues. This project shows how to check data before using it.

💼 Career

Backend developers often write request validation to ensure APIs receive correct data. This skill is fundamental for building reliable web services.

1

Set up Flask app and route

Import Flask and request from flask. Create a Flask app called app. Define a route /submit that accepts POST requests and a function submit() that returns a JSON response with {"message": "Ready"}.

Flask

# Import Flask and request
# Create Flask app called app
# Define POST route /submit with function submit
# Return JSON {"message": "Ready"}

Hint

Use Flask(__name__) to create the app. Use @app.route decorator with methods=['POST']. Return JSON using jsonify.

2

Extract data from request JSON

Inside the submit() function, get the JSON data from the request using request.get_json() and store it in a variable called data. Then extract name and age from data.

Flask

from flask import Flask, request, jsonify

app = Flask(__name__)

@app.route('/submit', methods=['POST'])
def submit():
    data = request.get_json()
    # Extract name and age from data

Hint

Use request.get_json() to get JSON data. Use data.get('name') and data.get('age') to extract values.

3

Add validation for name and age

Add validation inside submit() to check if name is a non-empty string and age is an integer greater than 0. If validation fails, return JSON with {"error": "Invalid input"} and status code 400.

Flask

from flask import Flask, request, jsonify

app = Flask(__name__)

@app.route('/submit', methods=['POST'])
def submit():
    data = request.get_json()
    name = data.get('name')
    age = data.get('age')
    # Validate name and age

Hint

Use isinstance() to check types. Use name.strip() to check for empty strings. Return JSON error with status 400 if invalid.

4

Return success message with validated data

If validation passes, return a JSON response with {"message": "Data received", "name": name, "age": age}.

Flask

from flask import Flask, request, jsonify

app = Flask(__name__)

@app.route('/submit', methods=['POST'])
def submit():
    data = request.get_json()
    name = data.get('name')
    age = data.get('age')
    if not isinstance(name, str) or not name.strip():
        return jsonify({"error": "Invalid input"}), 400
    if not isinstance(age, int) or age <= 0:
        return jsonify({"error": "Invalid input"}), 400
    # Return success JSON with name and age

Hint

Return JSON with keys message, name, and age using jsonify.

Practice

1. What is the main purpose of request validation in a Flask application?

easy

A. To store user data permanently

B. To check incoming data for correctness and safety

C. To style the HTML forms automatically

D. To speed up the server response time

Solution

1. Step 1: Understand what request validation means

   Request validation means checking the data sent by users to make sure it is correct and safe to use.

2. Step 2: Identify the main goal of validation

   The main goal is to prevent bad or harmful data from causing errors or security problems.

3. Final Answer:

   To check incoming data for correctness and safety -> Option B

4. Quick Check:

   Request validation = check data safety [OK]

Hint: Validation means checking data is safe and correct [OK]

Common Mistakes:

• Thinking validation speeds up the server
• Confusing validation with styling
• Assuming validation stores data

2. Which of the following is the correct way to access JSON data from a Flask request object?

easy

A. request.get_json()

B. request.json_data

C. request.json()

D. request.data.json

Solution

1. Step 1: Recall Flask's method to get JSON data

   Flask provides the method get_json() to parse JSON from the request body.

2. Step 2: Check each option for correctness

   request.json() uses parentheses but json is a property, not a method. request.json_data and D are not valid Flask attributes.

3. Final Answer:

   request.get_json() -> Option A

4. Quick Check:

   Use get_json() to read JSON data [OK]

Hint: Use request.get_json() to read JSON in Flask [OK]

Common Mistakes:

• Calling request.json as a function
• Using non-existent attributes like json_data
• Trying to access JSON via request.data.json

3. Given this Flask route code, what will be the output if the JSON request body is {"age": 17}?

from flask import Flask, request, jsonify
app = Flask(__name__)

@app.route('/check', methods=['POST'])
def check_age():
    data = request.get_json()
    if data and data.get('age', 0) >= 18:
        return jsonify({'status': 'allowed'})
    else:
        return jsonify({'status': 'denied'})

medium

A. {"status": "allowed"}

B. Empty response

C. 500 Internal Server Error

D. {"status": "denied"}

Solution

1. Step 1: Analyze the JSON input and condition

   The input JSON has "age": 17. The code checks if age is 18 or more.

2. Step 2: Determine which branch runs

   Since 17 is less than 18, the else branch runs, returning {'status': 'denied'}.

3. Final Answer:

   {"status": "denied"} -> Option D

4. Quick Check:

   Age 17 < 18 = denied [OK]

Hint: Check if age >= 18 to allow, else deny [OK]

Common Mistakes:

• Assuming 17 is allowed
• Expecting server error for valid JSON
• Thinking response is empty

4. What is wrong with this Flask code snippet for validating a required 'username' field in JSON?

data = request.get_json()
if 'username' in data:
    return 'Username received'
else:
    return 'Missing username', 400

medium

A. It does not check if data is None before accessing

B. It uses wrong HTTP status code for error

C. It should use request.json instead of get_json()

D. It should return JSON response, not plain text

Solution

1. Step 1: Understand get_json() behavior

   request.get_json() can return None if no JSON is sent or invalid JSON.

2. Step 2: Identify the error in code

   The code accesses 'username' in data without checking if data is None, causing a crash if data is None.

3. Final Answer:

   It does not check if data is None before accessing -> Option A

4. Quick Check:

   Check data is not None before key access [OK]

Hint: Always check if get_json() returned data before use [OK]

Common Mistakes:

• Ignoring None check on JSON data
• Confusing status codes
• Thinking request.json is better than get_json()
• Assuming plain text is always wrong

5. You want to validate a JSON request in Flask to ensure it has a 'name' string and an 'age' integer above 0. Which approach best combines validation and clear error messages?

hard

A. Manually check keys and types, return custom error messages on failure

B. Use request.get_json() and assume data is always correct

C. Use a library like marshmallow to define a schema and validate automatically

D. Skip validation and handle errors later in the code

Solution

1. Step 1: Consider manual validation drawbacks

   Manual checks can be error-prone and verbose, making code hard to maintain.

2. Step 2: Understand benefits of marshmallow

   Marshmallow lets you define clear schemas and automatically validates input, returning helpful error messages.

3. Step 3: Evaluate other options

   Assuming data is correct or skipping validation risks bugs and security issues.

4. Final Answer:

   Use a library like marshmallow to define a schema and validate automatically -> Option C

5. Quick Check:

   Use marshmallow for clear, automatic validation [OK]

Hint: Use marshmallow for easy, clear validation [OK]

Common Mistakes:

• Assuming data is always valid
• Writing complex manual checks
• Ignoring validation completely