Explain async and await in the Flask application. When do we use Quart

1.	Explain async and await in the Flask application. When do we use Quart instead?
Answer» If Flask is installed with the async extra, routes, error handlers, before request, after request, and teardown methods can all be coroutine functions (PIP install flask[async]). Where ContextVars are used, Python 3.7 or above is required. ContextVar is a variable that can be used. Async def and await can now be used to define views. @app.route("/get-data")[Text Wrapping Break]async def get_data():[Text Wrapping Break] data = await async_db_query(...)[Text Wrapping Break] return jsonify(data) Coroutine-based handlers are also supported by pluggable class-based views. This is TRUE for views that inherit from the flask.views' dispatch_request() method. All HTTP method handlers in views that inherit from flask.views, as well as the View class. MethodView is a class that represents a method. The execution of async functions necessitates the use of an event loop. As a WSGI application, Flask employs a single worker to manage a single request/response cycle. When an async view receives a request, Flask creates an event loop in a thread, runs the view function there, and then returns the result. Even for async views, each request ties up one worker. On the plus side, you may use async code within a view to perform numerous concurrent database QUERIES, HTTP connections to an external API, and so on. However, the maximum number of requests your application can manage at any given time will not change. Async code isn't always faster than sync code. When doing concurrent IO-bound work, async is USEFUL, but it is unlikely to improve CPU-bound operations. Traditional Flask views will continue to be appropriate for the majority of use cases, but Flask's async support allows you to write and use code that wasn't previously possible. When to use Quart instead: Because of the way Flask's async support is designed, it is slower than async-first frameworks. It makes sense to investigate Quart if your codebase is primarily async. Quart is a Flask reimplementation based on the ASGI standard rather than the WSGI standard. This enables it to manage a large number of concurrent requests, long-running requests, and WebSockets without the need for many worker processes or threads. Many of the benefits of async request handling can also be obtained by running Flask with Gevent or Eventlet. To do this, these libraries tweak low-level Python methods, whereas async/ await and ASGI make use of regular, current Python features. Understanding the individual needs is the most important factor in deciding WHETHER to utilize Flask, Quart or something else.

Explain async and await in the Flask application. When do we use Quart instead?

Answer»

If Flask is installed with the async extra, routes, error handlers, before request, after request, and teardown methods can all be coroutine functions (PIP install flask[async]). Where ContextVars are used, Python 3.7 or above is required. ContextVar is a variable that can be used. Async def and await can now be used to define views.

@app.route("/get-data")[Text Wrapping Break]async def get_data():[Text Wrapping Break] data = await async_db_query(...)[Text Wrapping Break] return jsonify(data)

Coroutine-based handlers are also supported by pluggable class-based views. This is TRUE for views that inherit from the flask.views' dispatch_request() method. All HTTP method handlers in views that inherit from flask.views, as well as the View class. MethodView is a class that represents a method.

The execution of async functions necessitates the use of an event loop. As a WSGI application, Flask employs a single worker to manage a single request/response cycle. When an async view receives a request, Flask creates an event loop in a thread, runs the view function there, and then returns the result.

Even for async views, each request ties up one worker. On the plus side, you may use async code within a view to perform numerous concurrent database QUERIES, HTTP connections to an external API, and so on. However, the maximum number of requests your application can manage at any given time will not change.

Async code isn't always faster than sync code. When doing concurrent IO-bound work, async is USEFUL, but it is unlikely to improve CPU-bound operations. Traditional Flask views will continue to be appropriate for the majority of use cases, but Flask's async support allows you to write and use code that wasn't previously possible.

When to use Quart instead: Because of the way Flask's async support is designed, it is slower than async-first frameworks. It makes sense to investigate Quart if your codebase is primarily async. Quart is a Flask reimplementation based on the ASGI standard rather than the WSGI standard. This enables it to manage a large number of concurrent requests, long-running requests, and WebSockets without the need for many worker processes or threads.

Many of the benefits of async request handling can also be obtained by running Flask with Gevent or Eventlet. To do this, these libraries tweak low-level Python methods, whereas async/ await and ASGI make use of regular, current Python features. Understanding the individual needs is the most important factor in deciding WHETHER to utilize Flask, Quart or something else.

Explain async and await in the Flask application. When do we use Quart instead?

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