Supported tags and respective Dockerfile links

• python3.7-alpine-gcp-secret-manager, latest (Dockerfile) contains grpcio, grpcio-tools and google-cloud-secret-manager installed; the latest version of Alpine
• python3.7-alpine-grpcio, (Dockerfile) contains grpcio, grpcio-tools installed; the latest version of Alpine; google-cloud-secret-manager can be installed via pip without dependecies problems
• python3.7-alpine (Dockerfile) the latest version of Alpine

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Note: There are tags for each build date. If you need to "pin" the Docker image version you use, you can select one of those tags. E.g. alexiskandre/meinheld-gunicorn-gcp:python3.7-alpine-gcp-secret-manager.

Dockerfiles with other versions python (without preinstalled grpcio libs) can be found in the original git repository.

meinheld-gunicorn

Docker image with Meinheld managed by Gunicorn for high-performance web applications in Python, with performance auto-tuning. Built in Alpine Linux.

Meinheld is a high-performance WSGI-compliant web server.

Meinheld is served through Gunicorn via --worker-class="egg:meinheld#gunicorn_worker in Docker Start Gunicorn command according to Meinheld official.

Python web applications running with Meinheld controlled by Gunicorn have some of the best performances achievable by (older) Python frameworks based on WSGI (synchronous code, instead of ASGI, which is asynchronous) (*).

This applies to frameworks like Flask and Django.

System

The latest version of Alpine Linux.

grpcio, grpcio-tools, google-cloud-secret-manager libraries preinstalled

If you're using Google Cloud Secret Manager, you might face a problem that it is not possible to install python's gcp-secret-manager library in Alpine without grpcio installed. However it takes a long while for the installation (20+ mins). Also you will need some C++ dependecies to be able to run it after installtion.

This Docker image has already everything pre-setup in Alpine Linux to be able to start working with google.cloud libraries right away.

Working on cluster machines

If you have a cluster of machines with Kubernetes, Google Clour Run, Docker Swarm Mode, Nomad or other similar complex system to manage distributed containers on multiple machines, then you're likely to handle replication at the cluster level instead of using a process manager in each container that starts multiple worker processes (this is what this Docker image does).

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For this reason, by default the number of workers is set to 1 in the predefined Gunicorn config file

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However you can always modify it by either

• creating your ownr Gunicorn config file placing it to /app/ directory
• setting is_web_concurrency = True with WORKERS_PER_CORE = 2 (or higher) in your env variables

Sources

GitHub repo: https://github.com/iskandre/meinheld-gunicorn-docker/

Docker Hub image: https://hub.docker.com/r/alexiskandre/meinheld-gunicorn-gcp/

How to use

You don't have to clone this repo.

You can use this image as a base image for other images.

Assuming you have a file requirements.txt, you could have a Dockerfile like this:

FROM alexiskandre/meinheld-gunicorn-gcp:python3.7-alpine-gcp-secret-manager

COPY ./requirements.txt /app/requirements.txt

RUN pip install --no-cache-dir --upgrade -r /app/requirements.txt

COPY ./app /app

It 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 "WSGI" application.

Then you can build your image from the directory that has your Dockerfile, e.g:

docker build -t myimage ./

Advanced usage

Environment variables

These are the environment variables that you can set in the container to configure it and their default values:

MODULE_NAME

The Python "module" (file) to be imported by Gunicorn, this module would contain the actual application in a variable.

By default:

• app.main if there's a file /app/app/main.py or
• main if 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" myimage

VARIABLE_NAME

The variable inside of the Python module that contains the WSGI application.

By default:

• app

For example, if your main Python file has something like:

from flask import Flask
api = Flask(__name__)

@api.route("/")
def hello():
    return "Hello World from Flask"

In this case api would be the variable with the "WSGI application". You could set it like:

docker run -d -p 80:80 -e VARIABLE_NAME="api" myimage

APP_MODULE

The 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:app or
• main:app

You can set it like:

docker run -d -p 80:80 -e APP_MODULE="custom_app.custom_main:api" myimage

GUNICORN_CONF

The path to a Gunicorn Python configuration file.

By default:

• /app/gunicorn_conf.py if it exists
• /app/app/gunicorn_conf.py if it exists
• /gunicorn_conf.py (the included default)

You can set it like:

docker run -d -p 80:80 -e GUNICORN_CONF="/app/custom_gunicorn_conf.py" myimage

WORKERS_PER_CORE

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:

• 2

You can set it like:

docker run -d -p 80:80 -e WORKERS_PER_CORE="3" myimage

If 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" myimage

In a server with 8 CPU cores, this would make it start only 4 worker processes.

WEB_CONCURRENCY

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 to 4.

You can set it like:

docker run -d -p 80:80 -e WEB_CONCURRENCY="2" myimage

This would make the image start 2 worker processes, independent of how many CPU cores are available in the server.

HOST

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

PORT

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" myimage

BIND

The 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" myimage

LOG_LEVEL

The log level for Gunicorn.

One of:

• debug
• info
• warning
• error
• critical

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" myimage

Custom Gunicorn configuration file

The image includes a default Gunicorn Python config file at /gunicorn_conf.py.

It uses the environment variables declared above to set all the configurations.

You can override it by including a file in:

• /app/gunicorn_conf.py
• /app/app/gunicorn_conf.py
• /gunicorn_conf.py

Custom /app/prestart.sh

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 head

and 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.py

Tests

This project is using Poetry. You need a working poetry and python3 environment before setting up a development environment. When you do you can just:

poetry install

To run all the linters and tests run ./scripts/test.sh within poetry:

poetry run ./scripts/tests.sh

tests.sh script reads .env file that can have the following format:

IMAGE_NAME=alexiskandre/meinheld-gunicorn
TAG_NAME=python3.7-alpine-google-secret-manager

Release Notes

Latest Changes after forking

• 🔥 Adding grpcio, grpcio-tools and google-secret-manager libraries in Alpine Linux Docker Images.
• 🔥 Switching the Dockerfiles to use the latest available Alpine version.
• 📝 Modyfing predafult gunicorn config to remove concurrency. In this way it's optimized to be used in concurrent platforms like Google Cloud Run, Kubernetes etc.
• 📝 Wrapping the project in poetry and updating the tests. Updated pyproject.toml dependecies.

Feel free to contact me.

License

This project is licensed under the terms of the MIT license.