FastAPI vs Flask (2024): A Head-to-Head Comparison

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Okay, here’s a comprehensive article comparing FastAPI and Flask, updated for 2024, with a word count approaching 5000 words:

FastAPI vs Flask (2024): A Head-to-Head Comparison

The landscape of Python web frameworks is rich and diverse, offering developers a plethora of options for building everything from simple APIs to complex web applications. Among the most popular choices, two frameworks stand out: Flask and FastAPI. While both are powerful and versatile, they cater to slightly different needs and philosophies. This in-depth comparison will delve into the intricacies of FastAPI and Flask, examining their features, performance, use cases, and overall suitability for various projects in 2024.

1. Introduction: The Rise of Microframeworks and Asynchronous Programming

Both Flask and FastAPI belong to the category of “microframeworks.” This term doesn’t imply weakness; rather, it emphasizes a minimalist, unopinionated core. Microframeworks provide the essential tools for building web applications without imposing rigid structures or unnecessary dependencies. This allows developers to have greater control over their project’s architecture and choose the components they need.

Flask, created by Armin Ronacher, has been a mainstay in the Python web development world since 2010. Its simplicity and extensibility have made it a favorite for both beginners and experienced developers. Flask’s core is intentionally small, focusing on providing the basics: routing, request handling, and template rendering (via Jinja2). For more advanced features like database integration, form validation, or authentication, Flask relies on extensions. This “batteries not included” approach offers flexibility but requires developers to make choices and integrate additional libraries.

FastAPI, created by Sebastián Ramírez (tiangolo), is a much newer framework, first released in 2018. It was designed from the ground up with modern Python features in mind, specifically type hints and asynchronous programming (using async and await). FastAPI leverages these features to provide automatic data validation, serialization, and interactive API documentation (powered by OpenAPI and JSON Schema). It’s built on top of Starlette for the web parts and Pydantic for data validation, resulting in a framework that is both fast and easy to use.

The rise of asynchronous programming is a key factor in FastAPI’s popularity. Traditional web frameworks like Flask are primarily synchronous, meaning they handle requests one at a time. This can lead to performance bottlenecks under heavy load, as the server waits for one request to complete before processing the next. Asynchronous frameworks, using Python’s asyncio library, can handle multiple requests concurrently, significantly improving performance for I/O-bound operations (like network requests or database queries). FastAPI embraces this asynchronous paradigm, making it inherently well-suited for building high-performance APIs.

2. Core Features and Design Philosophies

Let’s break down the core features and design principles of each framework:

2.1 Flask:

  • Simplicity and Extensibility: Flask’s core is deliberately minimal. It provides the essentials for routing, request handling (using Werkzeug), and template rendering (using Jinja2). This simplicity makes it easy to learn and get started with. For more advanced features, Flask relies on a rich ecosystem of extensions.
  • Werkzeug: Flask is built upon the Werkzeug WSGI toolkit. Werkzeug provides the underlying infrastructure for handling HTTP requests and responses, routing, and debugging.
  • Jinja2: Flask uses Jinja2 as its default templating engine. Jinja2 allows developers to create dynamic HTML templates with placeholders for data, control structures (loops and conditionals), and template inheritance.
  • Extensions: Flask’s extensibility is one of its greatest strengths. There are extensions for almost every conceivable need, including:
    • Flask-SQLAlchemy: Provides integration with SQLAlchemy, a powerful Object-Relational Mapper (ORM) for working with databases.
    • Flask-WTF: Simplifies form creation and validation using WTForms.
    • Flask-Login: Handles user authentication and session management.
    • Flask-RESTful: Helps build RESTful APIs. (Note: While useful, Flask-RESTful hasn’t kept pace with modern API development practices as well as FastAPI).
    • Flask-Mail: Provides email sending capabilities.
    • And many, many more.
  • WSGI Compliance: Flask is a WSGI (Web Server Gateway Interface) compliant framework. WSGI is a standard interface between web servers and Python web applications, ensuring portability across different web servers (like Gunicorn, uWSGI, and Waitress).
  • Development Server: Flask includes a built-in development server for testing and development purposes. This server is not suitable for production use.
  • Large Community and Mature Ecosystem: Flask has a large and active community, which means ample documentation, tutorials, and support resources are available. The ecosystem of extensions is also very mature and well-maintained.

2.2 FastAPI:

  • Modern Python: FastAPI embraces modern Python features, particularly type hints (introduced in Python 3.5) and asynchronous programming (async/await).
  • Type Hints for Validation and Documentation: FastAPI leverages type hints to automatically validate request data, serialize responses, and generate interactive API documentation. This is a significant departure from Flask, where validation and documentation are typically handled separately.
  • Pydantic: FastAPI uses Pydantic for data validation and settings management. Pydantic models define the structure and types of data, and FastAPI automatically uses these models to validate incoming requests and serialize outgoing responses. This eliminates the need for manual validation logic and reduces boilerplate code.
  • Starlette: FastAPI is built on top of Starlette, a lightweight ASGI (Asynchronous Server Gateway Interface) framework. Starlette provides the underlying asynchronous web handling capabilities, making FastAPI inherently fast and efficient.
  • Automatic API Documentation (OpenAPI and JSON Schema): FastAPI automatically generates interactive API documentation based on your code’s type hints and Pydantic models. This documentation is compliant with OpenAPI (formerly known as Swagger) and JSON Schema, making it easy to understand and use your API. The documentation includes:
    • Interactive API Explorer (Swagger UI): Allows you to try out your API endpoints directly in the browser.
    • Alternative API Documentation (ReDoc): Provides a more visually appealing and user-friendly documentation interface.
  • Dependency Injection: FastAPI has a powerful dependency injection system. This allows you to easily manage dependencies (like database connections, configuration settings, or authentication services) and inject them into your route handlers. This promotes code reusability and testability.
  • ASGI Compliance: FastAPI is an ASGI compliant framework. ASGI is the successor to WSGI, designed for asynchronous web applications. This allows FastAPI to take full advantage of asynchronous web servers like Uvicorn, Daphne, and Hypercorn.
  • Built-in Security Features: FastAPI includes built-in support for common security features like:
    • OAuth2 with Password (and JWT tokens): Provides a standard way to implement user authentication and authorization.
    • API Keys: Allows you to authenticate clients using API keys.
    • HTTP Basic Authentication: Supports basic authentication.
    • Security utilities for handling CORS, CSRF, and other common web vulnerabilities.
  • Performance: FastAPI is designed for high performance. Its asynchronous nature, combined with the use of Pydantic and Starlette, makes it significantly faster than Flask in many scenarios, especially for I/O-bound operations.
  • Growing Community and Ecosystem: While newer than Flask, FastAPI has a rapidly growing community and a burgeoning ecosystem of plugins and tools.

3. Code Examples: A Side-by-Side Comparison

Let’s illustrate the differences between Flask and FastAPI with some concrete code examples. We’ll create a simple API that handles creating, reading, updating, and deleting (CRUD) operations for “items.”

3.1 Flask Example:

“`python
from flask import Flask, request, jsonify

app = Flask(name)

In-memory “database” (replace with a real database in production)

items = {}
next_id = 1

@app.route(‘/items’, methods=[‘GET’])
def get_items():
return jsonify(items)

@app.route(‘/items/‘, methods=[‘GET’])
def get_item(item_id):
item = items.get(item_id)
if item:
return jsonify(item)
else:
return jsonify({‘message’: ‘Item not found’}), 404

@app.route(‘/items’, methods=[‘POST’])
def create_item():
global next_id
data = request.get_json()
# Basic validation (you’d typically use a library like Flask-WTF)
if not data or ‘name’ not in data:
return jsonify({‘message’: ‘Invalid data’}), 400
item = {‘id’: next_id, ‘name’: data[‘name’]}
items[next_id] = item
next_id += 1
return jsonify(item), 201

@app.route(‘/items/‘, methods=[‘PUT’])
def update_item(item_id):
data = request.get_json()
if not data or ‘name’ not in data:
return jsonify({‘message’: ‘Invalid data’}), 400
if item_id in items:
items[item_id][‘name’] = data[‘name’]
return jsonify(items[item_id])
else:
return jsonify({‘message’: ‘Item not found’}), 404

@app.route(‘/items/‘, methods=[‘DELETE’])
def delete_item(item_id):
if item_id in items:
del items[item_id]
return jsonify({‘message’: ‘Item deleted’})
else:
return jsonify({‘message’: ‘Item not found’}), 404

if name == ‘main‘:
app.run(debug=True)
“`

Explanation of Flask Code:

  • Flask(__name__): Creates a Flask application instance.
  • @app.route(...): Decorators define the routes and HTTP methods for each endpoint.
  • request.get_json(): Parses JSON data from the request body.
  • jsonify(): Converts Python dictionaries to JSON responses.
  • Manual Validation: The code includes basic manual validation to check for required fields. In a real application, you’d likely use a library like Flask-WTF for more robust validation.
  • In-memory “database”: For simplicity, the example uses a Python dictionary as an in-memory “database.” In a production application, you’d connect to a real database (e.g., using Flask-SQLAlchemy).
  • app.run(debug=True): Starts the Flask development server in debug mode.

3.2 FastAPI Example:

“`python
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel

app = FastAPI()

Pydantic model for Item

class Item(BaseModel):
id: int
name: str

In-memory “database” (replace with a real database in production)

items = {}
next_id = 1

@app.get(‘/items’, response_model=list[Item])
async def get_items():
return list(items.values())

@app.get(‘/items/{item_id}’, response_model=Item)
async def get_item(item_id: int):
item = items.get(item_id)
if item:
return item
else:
raise HTTPException(status_code=404, detail=’Item not found’)

@app.post(‘/items’, response_model=Item, status_code=201)
async def create_item(item: Item):
global next_id
item.id = next_id #For this inmemory example, otherwise Pydantic takes the value from the request
items[next_id] = item
next_id += 1
return item

@app.put(‘/items/{item_id}’, response_model=Item)
async def update_item(item_id: int, item: Item):
if item_id in items:
# Convert existing item to dictionary and update with new data
existing_item = items[item_id].dict() # Assuming items[item_id] is a Pydantic model
updated_item_data = item.dict(exclude_unset=True) # Get data from the input item
existing_item.update(updated_item_data)

    # Create a new Pydantic model instance from the updated data
    items[item_id] = Item(**existing_item)
    return items[item_id]

else:
raise HTTPException(status_code=404, detail=’Item not found’)

@app.delete(‘/items/{item_id}’)
async def delete_item(item_id: int):
if item_id in items:
del items[item_id]
return {‘message’: ‘Item deleted’}
else:
raise HTTPException(status_code=404, detail=’Item not found’)
“`

Explanation of FastAPI Code:

  • FastAPI(): Creates a FastAPI application instance.
  • class Item(BaseModel):: Defines a Pydantic model for the Item data. This model specifies the data types and structure.
  • @app.get(...), @app.post(...), etc.: Decorators define the routes and HTTP methods, similar to Flask.
  • Type Hints: Type hints are used extensively (e.g., item_id: int, item: Item). FastAPI uses these type hints for:
    • Request Validation: Automatically validates that incoming request data matches the Item model.
    • Response Serialization: Automatically converts the Item model to a JSON response.
    • Documentation: Generates interactive API documentation based on the type hints and models.
  • response_model: Specifies the Pydantic model to use for the response. This ensures that the response data conforms to the model and is automatically serialized.
  • status_code: Allows to declare the status code.
  • async def: Defines asynchronous route handlers using async and await.
  • HTTPException: A convenient way to raise HTTP exceptions with specific status codes and details.
  • Automatic Documentation: FastAPI automatically generates interactive API documentation (Swagger UI and ReDoc) based on the code. You can access it by going to /docs or /redoc in your browser after running the application.
  • item.dict(exclude_unset=True): When updating an item, it is crucial to consider that the incoming item (of type Item) might not contain all the fields. The exclude_unset=True argument in the dict() method is vital here. It ensures that only the fields explicitly provided in the update request are included in the dictionary. This prevents accidentally overwriting existing fields with None if they were not part of the update request.

Key Differences Highlighted in the Examples:

  • Data Validation: Flask requires manual validation or the use of external libraries. FastAPI provides automatic validation based on Pydantic models and type hints.
  • Serialization: Flask uses jsonify for manual JSON serialization. FastAPI automatically handles serialization based on the response_model.
  • Asynchronous Handling: Flask is primarily synchronous (although you can use extensions like Flask-Executor for limited asynchronous tasks). FastAPI is built for asynchronous programming.
  • Documentation: Flask requires manual documentation or the use of extensions like Flask-RESTful or Flasgger. FastAPI automatically generates interactive API documentation.
  • Type Hints: FastAPI heavily relies on type hints, while Flask uses them less extensively.
  • Status Code: You can declare status codes directly in FastAPI.

4. Performance Benchmarks: Synchronous vs. Asynchronous

Performance is a crucial consideration when choosing a web framework. While synthetic benchmarks should always be interpreted with caution (as real-world performance depends heavily on specific use cases), they can provide a general indication of the relative performance characteristics of different frameworks.

4.1 Testing Methodology

To compare the performance of Flask and FastAPI, we’ll use a simple benchmark that measures the number of requests per second (RPS) that each framework can handle. We’ll use the following tools:

  • wrk: A modern HTTP benchmarking tool.
  • Gunicorn: A WSGI HTTP server for running Flask (synchronous).
  • Uvicorn: An ASGI HTTP server for running FastAPI (asynchronous).

We’ll create a very basic endpoint in both Flask and FastAPI that simply returns a “Hello, World!” response. We’ll run the benchmark with different numbers of worker processes and connections to simulate varying levels of concurrency.

4.2 Flask Benchmark Code:

“`python
from flask import Flask

app = Flask(name)

@app.route(‘/’)
def hello():
return ‘Hello, World!’
“`

Running Flask with Gunicorn:

bash
gunicorn -w 4 -k sync app:app

  • -w 4: Specifies 4 worker processes.
  • -k sync: Specifies synchronous workers (the default for Gunicorn).
  • app:app: Specifies the WSGI application (module:application object).

4.3 FastAPI Benchmark Code:

“`python
from fastapi import FastAPI

app = FastAPI()

@app.get(‘/’)
async def hello():
return ‘Hello, World!’

“`
Running FastAPI with Uvicorn:

bash
uvicorn app:app --workers 4

  • --workers 4: Specifies 4 worker processes. Uvicorn uses asynchronous workers by default.
  • app:app: Specifies the ASGI application.

4.4 Benchmark Results (Illustrative)

The following results are illustrative and will vary depending on the hardware, operating system, and specific benchmark configuration. The key takeaway is the relative performance difference between the synchronous and asynchronous approaches.

Framework Workers Connections RPS (approximate)
Flask 1 10 1,500
Flask 4 10 4,500
Flask 4 100 5,000
FastAPI 1 10 5,000
FastAPI 4 10 18,000
FastAPI 4 100 25,000+

Observations:

  • FastAPI (Asynchronous) is Significantly Faster: In this simple benchmark, FastAPI consistently outperforms Flask, especially under higher concurrency (more connections). This is due to FastAPI’s asynchronous nature, which allows it to handle multiple requests concurrently without blocking.
  • Scaling with Workers: Both frameworks can scale by increasing the number of worker processes. However, FastAPI benefits more from increasing workers due to its ability to handle more concurrent requests within each worker.
  • I/O-Bound Operations: The performance difference will be even more pronounced in real-world scenarios involving I/O-bound operations (like database queries, network requests, or file I/O). FastAPI’s asynchronous capabilities allow it to handle these operations without blocking the main thread, leading to much higher throughput.
  • Flask with gevent or eventlet: It’s important to note that Flask can be made asynchronous using libraries like gevent or eventlet. These libraries use “green threads” (cooperative multitasking) to achieve concurrency. While this can improve Flask’s performance, it’s generally more complex to set up and manage than FastAPI’s built-in asynchronous support, and might not always achieve the same level of performance. It also requires careful consideration of your entire codebase to ensure it’s compatible with the chosen concurrency model.

5. Use Cases: When to Choose Flask or FastAPI

Given the differences in features, performance, and design, Flask and FastAPI are better suited for different types of projects.

5.1 When to Choose Flask:

  • Small to Medium-Sized Projects: Flask’s simplicity makes it an excellent choice for smaller projects where you don’t need the advanced features of FastAPI.
  • Projects with Existing Synchronous Code: If you have a large codebase that is already written in a synchronous style, migrating to Flask might be easier than rewriting it for FastAPI’s asynchronous model.
  • Projects Requiring Specific Extensions: If your project relies heavily on specific Flask extensions that don’t have direct equivalents in FastAPI, sticking with Flask might be more practical.
  • Learning and Prototyping: Flask’s simplicity makes it a great choice for learning web development concepts and quickly prototyping ideas.
  • Websites with Templating: If you’re building a traditional website with server-side rendering using Jinja2 templates, Flask is a natural fit.
  • Projects Where Performance is Not the Primary Concern: For applications that don’t require high concurrency or handle a lot of I/O-bound operations, Flask’s performance is often sufficient.
  • Preference for a “Batteries Not Included” Approach: If you prefer a framework that gives you complete control over your project’s structure and dependencies, Flask’s minimalist core and reliance on extensions might be appealing.

5.2 When to Choose FastAPI:

  • High-Performance APIs: FastAPI is ideal for building RESTful APIs that require high performance and scalability, especially those involving I/O-bound operations.
  • Microservices: FastAPI’s speed and efficiency make it a great choice for building microservices, which often need to handle a large number of requests with low latency.
  • Modern API Development: If you want to take advantage of modern Python features like type hints and asynchronous programming, FastAPI is the clear winner.
  • Automatic Data Validation and Serialization: FastAPI’s built-in data validation and serialization (using Pydantic) can significantly reduce development time and improve code quality.
  • Interactive API Documentation: FastAPI’s automatic generation of interactive API documentation (Swagger UI and ReDoc) is a major productivity boost.
  • Projects Requiring Asynchronous Operations: If your application needs to perform a lot of asynchronous tasks (like making requests to external APIs, interacting with databases, or processing data streams), FastAPI’s asynchronous capabilities are essential.
  • Machine Learning Model Serving: FastAPI is increasingly popular for serving machine learning models. Its performance and support for asynchronous operations make it well-suited for handling inference requests.
  • Real-time Applications (with WebSockets): FastAPI has excellent support for WebSockets, making it suitable for building real-time applications like chat applications or online games (Starlette provides the WebSocket support).

6. Learning Curve and Community Support

6.1 Flask:

  • Learning Curve: Flask has a relatively gentle learning curve, especially for developers familiar with basic Python concepts. Its simplicity and well-written documentation make it easy to get started.
  • Community Support: Flask has a large and active community, which means ample documentation, tutorials, blog posts, and forums are available. You’re likely to find answers to your questions quickly.
  • Maturity: Flask is a mature framework with a proven track record. It’s been around for over a decade and is used by many large companies.

6.2 FastAPI:

  • Learning Curve: FastAPI’s learning curve is slightly steeper than Flask’s, primarily due to the need to understand type hints and asynchronous programming. However, the official documentation is exceptionally well-written and includes numerous examples and tutorials. Once you grasp the core concepts, FastAPI becomes very intuitive to use.
  • Community Support: While newer than Flask, FastAPI has a rapidly growing and enthusiastic community. The documentation is excellent, and there are increasing numbers of tutorials, blog posts, and community resources available.
  • Modernity: FastAPI is a modern framework designed for current Python best practices. It’s actively maintained and continuously evolving to incorporate new features and improvements.

7. Ecosystem and Integrations

Both Flask and FastAPI have rich ecosystems of libraries and tools that extend their functionality.

7.1 Flask Ecosystem:

As mentioned earlier, Flask relies heavily on extensions. Here are some of the most popular Flask extensions:

  • Database Integration:
    • Flask-SQLAlchemy
    • Flask-MongoEngine
    • Flask-Peewee
  • Form Handling:
    • Flask-WTF
  • Authentication and Authorization:
    • Flask-Login
    • Flask-Security-Too
    • Flask-Principal
  • RESTful APIs:
    • Flask-RESTful
    • Flask-Marshmallow (for serialization)
  • Email:
    • Flask-Mail
  • Caching:
    • Flask-Caching
  • Task Queues:
    • Flask-CeleryExt
  • Testing:
    • Flask-Testing

7.2 FastAPI Ecosystem:

FastAPI’s ecosystem is growing rapidly. While it doesn’t have as many dedicated extensions as Flask (partly because many features are built-in), there are numerous libraries and tools that integrate well with FastAPI:

  • Database Integration:
    • SQLAlchemy (with databases or ormar): FastAPI works well with SQLAlchemy, a popular ORM. You can use the databases library for asynchronous database access or ormar for a FastAPI-specific ORM built on top of SQLAlchemy and Pydantic.
    • Tortoise ORM: An asynchronous ORM inspired by Django’s ORM, designed specifically for use with asyncio.
    • Beanie: An asynchronous ODM (Object-Document Mapper) for MongoDB, built on top of Pydantic and Motor.
  • Authentication and Authorization:
    • FastAPI Security (built-in): Provides OAuth2, API keys, and HTTP Basic authentication.
    • Authlib: A comprehensive library for building OAuth and OpenID Connect clients and servers.
  • Task Queues:
    • Celery: A popular distributed task queue that can be used with FastAPI (although you’ll need to use a worker that supports asyncio).
    • RQ (Redis Queue): A simpler task queue that uses Redis as a message broker.
    • Dramatiq: Another option, and supports asyncio.
  • Testing:
    • pytest: The recommended testing framework for FastAPI.
    • httpx: An asynchronous HTTP client that can be used to test FastAPI applications.
    • TestClient (built-in to FastAPI): Provides a convenient way to test your FastAPI application without starting a server.
  • Other Integrations:
    • pydantic-settings: For managing application settings using Pydantic models.
    • fastapi-mail: For sending emails
    • Various libraries for interacting with specific cloud services (e.g., AWS, Google Cloud, Azure).

8. Deployment

Both Flask and FastAPI applications can be deployed to a variety of platforms, including:

  • Traditional Servers:
    • Flask: Use Gunicorn, uWSGI, or Waitress (WSGI servers) with a process manager like systemd or Supervisor.
    • FastAPI: Use Uvicorn, Daphne, or Hypercorn (ASGI servers) with a process manager.
  • Cloud Platforms:
    • Heroku: Supports both Flask and FastAPI.
    • AWS (Amazon Web Services): Use services like Elastic Beanstalk, EC2, or Lambda (with API Gateway).
    • Google Cloud Platform (GCP): Use services like App Engine, Compute Engine, or Cloud Functions (with Cloud Endpoints).
    • Microsoft Azure: Use services like App Service, Virtual Machines, or Azure Functions.
    • DigitalOcean: Supports both Flask and FastAPI.
  • Containers (Docker): Both Flask and FastAPI can be easily containerized using Docker, making them portable and scalable. You can then deploy the containers to platforms like Kubernetes.

9. Key Differences Summarized: A Table

Feature Flask FastAPI
Core Philosophy Minimalist, extensible Modern, performant, type-driven
Asynchronous Primarily synchronous (extensions available) Asynchronous by design (async/await)
Type Hints Optional Required, used for validation & documentation
Data Validation Manual or via extensions (e.g., Flask-WTF) Automatic, via Pydantic models
Serialization Manual (e.g., jsonify) Automatic, via Pydantic models
API Documentation Manual or via extensions (e.g., Flasgger) Automatic (OpenAPI/Swagger UI, ReDoc)
Performance Good, but can be limited by synchronous nature Excellent, especially for I/O-bound tasks
Learning Curve Easier Slightly steeper (due to async & type hints)
Dependency Injection Supported via extensions Built-in
Web Server Interface WSGI ASGI
Maturity Mature, large community Newer, rapidly growing community
Templating Jinja2 (built-in) No built-in templating (use Jinja2 or others)
WebSockets Supported via extensions Supported (via Starlette)
Security Supported via extensions Built-in features (OAuth2, API keys, etc.)

10. Conclusion: Choosing the Right Tool for the Job

In 2024, both Flask and FastAPI remain excellent choices for building web applications and APIs in Python. The best framework for your project depends on your specific requirements, priorities, and team expertise.

Flask is a solid choice for projects where simplicity, flexibility, and a mature ecosystem are paramount. It’s particularly well-suited for smaller projects, websites with server-side rendering, and applications where performance is not the primary concern.

FastAPI is the ideal choice for building high-performance APIs, microservices, and applications that require asynchronous operations, automatic data validation, and interactive documentation. Its modern design and focus on developer productivity make it a compelling option for new projects, especially those leveraging the power of asynchronous programming.

Ultimately, the “best” framework is the one that best fits your needs and allows you to build robust, maintainable, and efficient applications. Consider carefully the factors discussed in this article, and don’t hesitate to experiment with both frameworks to see which one feels more comfortable and productive for your team. The good news is you have access to two incredibly powerful and flexible frameworks for web development in python.

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