Docker image with Hypercorn for high-performance web applications in Python 3.6 and above with performance auto-tuning. Optionally in a slim version or based on Alpine Linux.
NOTE: This repository is based on uvicorn-gunicorn-docker.
Python web applications running with Hypercorn (using the "ASGI" specification for Python asynchronous web applications) have shown to have some of the best performances, as measured by third-party benchmarks.
The achievable performance is on par with (and in many cases superior to) Go and Node.js frameworks.
This image has an "auto-tuning" mechanism included, so that you can just add your code and get that same high performance automatically. And without making sacrifices.
Hypercorn is an ASGI web server based on the sans-io hyper, h11, h2, and wsproto libraries and inspired by Gunicorn. Hypercorn supports HTTP/1, HTTP/2, WebSockets (over HTTP/1 and HTTP/2), ASGI/2, and ASGI/3 specifications. Hypercorn can utilise asyncio, uvloop, or trio worker types.
- You don't need to clone the GitHub repo. You can use this image as a base image for other images, using this in your
Dockerfile:
FROM cormorack/hypercorn:python3.7-conda4.9.2
COPY ./app /appIt will expect a file at /app/app/main.py.
Or otherwise a file at /app/main.py.
And will expect it to contain a variable app with your "ASGI" application.
Then you can build your image from the directory that has your Dockerfile, e.g:
docker build -t myimage ./- Run a container based on your image:
docker run -d --name mycontainer -p 80:80 myimageYou should be able to check it in your Docker container's URL, for example: http://192.168.99.100/ or http://127.0.0.1/ (or equivalent, using your Docker host).
Coming soon ...
Coming soon ...
These are the environment variables that you can set in the container to configure it and their default values:
The Python "module" (file) to be imported by Gunicorn, this module would contain the actual application in a variable.
By default:
app.mainif there's a file/app/app/main.pyormainif there's a file/app/main.py
For example, if your main file was at /app/custom_app/custom_main.py, you could set it like:
docker run -d -p 80:80 -e MODULE_NAME="custom_app.custom_main" myimageThe variable inside of the Python module that contains the ASGI application.
By default:
app
For example, if your main Python file has something like:
from fastapi import FastAPI
api = FastAPI()
@api.get("/")
def read_root():
return {"message": "Hello world!"}In this case api would be the variable with the "ASGI application". You could set it like:
docker run -d -p 80:80 -e VARIABLE_NAME="api" myimageThe string with the Python module and the variable name passed to Gunicorn.
By default, set based on the variables MODULE_NAME and VARIABLE_NAME:
app.main:appormain:app
You can set it like:
docker run -d -p 80:80 -e APP_MODULE="custom_app.custom_main:api" myimageThe path to a Gunicorn Python configuration file.
By default:
/app/hypercorn_conf.pyif it exists/app/app/hypercorn_conf.pyif it exists/hypercorn_conf.py(the included default)
You can set it like:
docker run -d -p 80:80 -e HYPERCORN_CONF="/app/custom_hypercorn_conf.py" myimageYou can use the config file from this image as a starting point for yours.
This image will check how many CPU cores are available in the current server running your container.
It will set the number of workers to the number of CPU cores multiplied by this value.
By default:
1
You can set it like:
docker run -d -p 80:80 -e WORKERS_PER_CORE="3" myimageIf you used the value 3 in a server with 2 CPU cores, it would run 6 worker processes.
You can use floating point values too.
So, for example, if you have a big server (let's say, with 8 CPU cores) running several applications, and you have an ASGI application that you know won't need high performance. And you don't want to waste server resources. You could make it use 0.5 workers per CPU core. For example:
docker run -d -p 80:80 -e WORKERS_PER_CORE="0.5" myimageIn a server with 8 CPU cores, this would make it start only 4 worker processes.
Note: By default, if WORKERS_PER_CORE is 1 and the server has only 1 CPU core, instead of starting 1 single worker, it will start 2. This is to avoid bad performance and blocking applications (server application) on small machines (server machine/cloud/etc). This can be overridden using WEB_CONCURRENCY.
Set the maximum number of workers to use.
You can use it to let the image compute the number of workers automatically but making sure it's limited to a maximum.
This can be useful, for example, if each worker uses a database connection and your database has a maximum limit of open connections.
By default it's not set, meaning that it's unlimited.
You can set it like:
docker run -d -p 80:80 -e MAX_WORKERS="24" myimageThis would make the image start at most 24 workers, independent of how many CPU cores are available in the server.
Override the automatic definition of number of workers.
By default:
- Set to the number of CPU cores in the current server multiplied by the environment variable
WORKERS_PER_CORE. So, in a server with 2 cores, by default it will be set to2.
You can set it like:
docker run -d -p 80:80 -e WEB_CONCURRENCY="2" myimageThis would make the image start 2 worker processes, independent of how many CPU cores are available in the server.
The "host" used by Gunicorn, the IP where Gunicorn will listen for requests.
It is the host inside of the container.
So, for example, if you set this variable to 127.0.0.1, it will only be available inside the container, not in the host running it.
It's is provided for completeness, but you probably shouldn't change it.
By default:
0.0.0.0
The port the container should listen on.
If you are running your container in a restrictive environment that forces you to use some specific port (like 8080) you can set it with this variable.
By default:
80
You can set it like:
docker run -d -p 80:8080 -e PORT="8080" myimageThe actual host and port passed to Gunicorn.
By default, set based on the variables HOST and PORT.
So, if you didn't change anything, it will be set by default to:
0.0.0.0:80
You can set it like:
docker run -d -p 80:8080 -e BIND="0.0.0.0:8080" myimageThe log level for Gunicorn.
One of:
debuginfowarningerrorcritical
By default, set to info.
If you need to squeeze more performance sacrificing logging, set it to warning, for example:
You can set it like:
docker run -d -p 80:8080 -e LOG_LEVEL="warning" myimageThe class to be used by Hypercorn for the workers.
Hypercorn can utilise asyncio, uvloop, or trio worker types.
Read more about it in the Hypercorn docs: worker_class.
By default, set to asyncio.
You can set it like:
docker run -d -p 80:8080 -e WORKER_CLASS="uvloop" myimageThe number of seconds to wait for requests on a Keep-Alive connection.
Read more about it in the Hypercorn docs: keep_alive_timeout.
By default, set to 2.
You can set it like:
docker run -d -p 80:8080 -e KEEP_ALIVE="20" myimageTimeout for graceful workers restart.
Read more about it in the Hypercorn docs: graceful_timeout.
By default, set to 120.
You can set it like:
docker run -d -p 80:8080 -e GRACEFUL_TIMEOUT="20" myimageThe access log file to write to.
By default "-", which means stdout (print in the Docker logs).
If you want to disable ACCESS_LOG, set it to an empty value.
For example, you could disable it with:
docker run -d -p 80:8080 -e ACCESS_LOG=myimageThe error log file to write to.
By default "-", which means stderr (print in the Docker logs).
If you want to disable ERROR_LOG, set it to an empty value.
For example, you could disable it with:
docker run -d -p 80:8080 -e ERROR_LOG=myimageThe path where to find the pre-start script.
By default, set to /app/prestart.sh.
You can set it like:
docker run -d -p 80:8080 -e PRE_START_PATH="/custom/script.sh" myimageThe image includes a default Hypercorn Python config file at /hypercorn_conf.py.
It uses the environment variables declared above to set all the configurations.
You can override it by including a file in:
/app/hypercorn_conf.py/app/app/hypercorn_conf.py/hypercorn_conf.py
If you need to run anything before starting the app, you can add a file prestart.sh to the directory /app. The image will automatically detect and run it before starting everything.
For example, if you want to add Alembic SQL migrations (with SQLALchemy), you could create a ./app/prestart.sh file in your code directory (that will be copied by your Dockerfile) with:
#! /usr/bin/env bash
# Let the DB start
sleep 10;
# Run migrations
alembic upgrade headand it would wait 10 seconds to give the database some time to start and then run that alembic command.
If you need to run a Python script before starting the app, you could make the /app/prestart.sh file run your Python script, with something like:
#! /usr/bin/env bash
# Run custom Python script before starting
python /app/my_custom_prestart_script.pyYou can customize the location of the prestart script with the environment variable PRE_START_PATH described above.
The default program that is run is at /start.sh. It does everything described above.
There's also a version for development with live auto-reload at:
/start-reload.shFor development, it's useful to be able to mount the contents of the application code inside of the container as a Docker "host volume", to be able to change the code and test it live, without having to build the image every time.
In that case, it's also useful to run the server with live auto-reload, so that it re-starts automatically at every code change.
The additional script /start-reload.sh runs Hypercorn in a single process.
It is ideal for development.
For example, instead of running:
docker run -d -p 80:80 myimageYou could run:
docker run -d -p 80:80 -v $(pwd):/app myimage /start-reload.sh-v $(pwd):/app: means that the directory$(pwd)should be mounted as a volume inside of the container at/app.$(pwd): runspwd("print working directory") and puts it as part of the string.
/start-reload.sh: adding something (like/start-reload.sh) at the end of the command, replaces the default "command" with this one. In this case, it replaces the default (/start.sh) with the development alternative/start-reload.sh.
As /start-reload.sh doesn't run with Gunicorn, any of the configurations you put in a hypercorn_conf.py file won't apply.
But these environment variables will work the same as described above:
MODULE_NAMEVARIABLE_NAMEAPP_MODULEHOSTPORTLOG_LEVEL
This project is licensed under the terms of the MIT license.