Introduction to Hypercorn

Hypercorn is a high-performance, Scalable Asynchronous Server Gateway Interface (ASGI) server for Python web applications. It is an alternative to popular ASGI servers like Uvicorn and Daphne, offering robust features and high configurability. Hypercorn is designed to provide the best performance for your async web applications.

Getting Started with Hypercorn

To install Hypercorn, you can use pip:

  pip install hypercorn

Running an ASGI Application

To run an ASGI application, use the command:

  hypercorn your_app:app

Configuration Options

Hypercorn offers various configuration options that can be set via CLI arguments, configuration files, or environment variables. Here are some common configurations:

• --bind: Bind to an address (default: 127.0.0.1:8000)
• --workers: Number of worker processes (default: 1)
• --worker-class: The type of worker to use (default: async)
• --reload: Enable auto-reloading of the application on code changes

Example Application

Let’s create an example ASGI application using Starlette and run it with Hypercorn. First, install the necessary libraries:

  pip install starlette

Next, create a simple application:

  
    from starlette.applications import Starlette
    from starlette.responses import JSONResponse
    from starlette.routing import Route

    async def homepage(request):
        return JSONResponse({'hello': 'world'})

    app = Starlette(debug=True, routes=[
        Route('/', homepage),
    ])
  

Finally, run the application using Hypercorn:

  hypercorn app:app --reload

API Examples

Here are several API examples for Hypercorn:

Worker Concurrency

You can specify the concurrency level for each worker:

  hypercorn app:app --worker-class uvloop --workers 4

Logging

Configure logging level:

  hypercorn app:app --log-level debug

SSL/HTTPS Configuration

Enable SSL for secure connections:

  hypercorn app:app --certfile=/path/to/cert.pem --keyfile=/path/to/key.pem

Custom Config File

Create a custom configuration file (e.g., config.toml):

  
    [hypercorn]
    bind = "0.0.0.0:8000"
    workers = 2
    loglevel = "info"
  

Run the application with the custom configuration file:

  hypercorn app:app --config config.toml

By leveraging these configurations and examples, you can optimize Hypercorn for various use cases to achieve high performance and scalability in your Python web applications.

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