pydc-control 2.93

Description:

pydccontrol 2.93

Python Docker Compose Control
This library can be used to control multiple docker compose-based projects in
a coordinated way, allowing you to start, stop, and developing on separate projects
as necessary.
This project was built from requirements at Adobe that arose when splitting a
monolith project into microservices. It simplifies this transition and makes it easy
to run multiple projects at the same time whether pulling from built/deployed
upstream docker images or developing using local code.
Features

Coordinates the execution of multiple docker compose projects in a
single network/namespace
Start or stop an entire application, no matter the number of projects or
docker-compose files, with a single command
Extremely configurable, add your own commands and projects
Integrates directly with docker compose, as long as you can use docker compose files,
this project can control them
Allows easy development of a single project or multiple projects at the same time
Generate dynamic docker-compose files using Jinja templates for any number of projects
Manages pre-commit config file(s) by copying and
installing in each configured project

Setup
Application filesystem layout
This project assumes a flat directory layout for projects, with the control project
located in the same directory as all other projects. For example:
# Arbitrary directory containing all projects for an application,
# may also contain other directories/applications as well
my-app/
# Control project, uses pydc_control
app-control/
# This is arbitrary, but recommended to be able to add to the path
bin/
# Control script - executable python script that uses pydc_control
appctl
# Configuration file used to build base docker-compose template
config.yml
# Environment variable configuration, symlinked to all projects automatically
docker-compose.env
# Optional pre-commit config file(s)
# This needs to be configured in each project to be copied and installed
my-pre-commit-config.yaml
# Arbitrary projects
project1/
# The only required file for a project is a docker-compose file
docker-compose.yml
project2/
# A jinja-templated file may be used instead (see advanced features below)
docker-compose-template.yml
...

Determine configuration variables
Determine these variables up front:

Docker network name (mynetwork in the examples below)
Docker compose service and container prefix (mynamespace_ in the examples below)
Core service prefix (core_ in the examples below)
Docker compose project name (myproject in the examples below)
Docker registry and tags that will be used for deployed containers

Create a control project
Create a new project for your control project. It can be configured however you'd like,
but it should either have a script available on the path for easy execution or install
it into a local environment for easy execution.
Control script
Add a single python script to the project. Using naming such as <app>ctl is
recommended to make it easy to execute.
#!/usr/bin/env python3

import os
import sys
import pydc_control


if __name__ == '__main__':
# The base path is the location of your control project
base_path = os.path.realpath(os.path.join(os.path.dirname(__file__), '..'))
# Run pydc_control, returns an exit code
sys.exit(pydc_control.run(base_path))

Configuration file
Copy the config.yml.example file (or create your own) and add it to the control
project. Every project in your application should have an entry in the projects
list in this file, with one or more services (containers) attached to it. The syntax
is similar to a docker-compose file, but has a layer of abstraction to handle projects
with multiple services. This file will be used to generate the base docker compose
file during control script runs.
For services that have no associated project, for example, a MySQL or message bus
container, you can create an arbitrarily named project that has null values for the
directory and repository properties.
For projects that have no associated services, for example a project containing
static configuration, you can create a project with the services property set to
an empty list ([]).
Note that several settings near the top of the file are required.
Environment variables file
Copy the docker-compose.env.example file (or create your own) and add it to the
control project. This file should be ignored by your VCS. It may be helpful to
create a docker-compose.env.example file of your own in the project with
placeholders that may be filled in by individual developers when setting up their
own environment.
Any environment variables defined here will be added automatically to any running
container (unless they define env_file: [] in the configuration file).
Setup projects
Each project that pydc control can own should contain at least a docker-compose.yml
file. The docker compose should be quite standard, but typically must contain at least
one service that is prefixed with the service/container prefix determined above and the
network as determined above. The service should also be attached to that network.
For example:
version: '3'

services:
# This prefix MUST match the service prefix determined above
mynamespace_ping:
image: containous/whoami:latest
ports:
- "80"
networks:
- mynetwork

networks:
mynetwork:
external: true

Setup template projects
Projects with docker compose templates can add some variables that are pulled automatically from the config file:
version: '3'

services:
{{ service_prefix }}_ping:
image: containous/whoami:latest
ports:
- "80"
networks:
- {{ network }}

networks:
{{ network }}:
external: true

Add README
Copy the README-example.rst to the README.rst file in your
control project. Follow the instructions at the top of the file to customize it for
your project.
Usage
NOTE: All examples using appctl as the command is just an example. This script
may be named whatever you would like.
To use your control script, simply add it to the PATH and call it from any directory.
The behavior changes based on the directory you are in:

If called from a project directory (listed in config.yml), that project is assumed
to be currently developed, meaning it is automatically added to the -p parameters
in the control script. This will use the development docker-compose file from the
project and all other containers from the generated base docker-compose in the control
project (built from config.yml).
If called from any other project directory (included the control project), it will
only include the generated docker compose from the control project (built from
config.yml).

Develop projects
To add more development projects besides the current project directory, simply use the
-p flag:
appctl -p project2 config

Run docker compose commands
Most common docker compose commands have shortcuts added to the control script, with an
additional command that may be used to pass arbitrary commands to docker compose.
appctl config
appctl up
appctl up-detach
appctl down
appctl stop
appctl build
appctl pull
appctl docker-compose -- <additional docker compose args>
# Alias for docker-compose command
appctl dc -- <additional docker compose args>
# see appctl --help for all commands available

Container startup order
Services are started up using the following method:


All services (containers) marked with core: true are started detached


All open ports defined on the core services are checked to make sure they are open


Any wait-for-ports (see below) are waited for via requests


All services belonging to projects that are not being developed are started detached

Note: If no projects are being developed (see above), all services are started and
are not detached.



All developed project services are started and logs are displayed for only these
services


The reason for the detached behavior for many of the containers is that they can run in the
background and are often not changed. Additionally, this prevents logs from showing up for them
in the console, which could cause extra noise when developing on only one or two projects.
If the control script process is interrupted (via Ctrl+C for example), the developed
project services are attempted to be stopped (and only these) so that they may be restarted
again. All of these operations and choices are to ensure the smoothest experience with
docker compose logs and interactions so that you can focus only on your developed projects.
Perform VCS checkout, pull, status (git only)
To clone and/or update all projects listed in the config.yml automatically,
use the following command:
appctl checkout
# Alias
appctl co

To check the repository status for every project:
appctl repo-status
# Alias
appctl rs

Advanced features
Dynamically generating image references
While many image references can be hardcoded, it may be desirable to generate image
references based on specific tags or with specific registries based on those tags.
This can be done by using the image_path key in the service definition in
config.yml instead of image. The full image reference is generated from 3 pieces
of information:


The image path defined in the image_path property for the service
in config.yml


The tag defined on the command line of your control script (-t)

The full list of tags is defined in the docker-compose.tags property of the
config.yml file.



The docker-compose.registry property defined in config.yml

The docker-compose.registries-by-tag property may be used to override the registry
based on the tag value.



The full image reference is of the form <registry><path>:<tag>.
Templating docker-compose.yml
In order to use a template instead of a direct docker-compose.yml file in a project,
simply create a file named docker-compose-template.yml and then make sure that
docker-compose.yml file is ignored by your VCS since it will be regenerated on each
run of the control script. The template file is processed via Jinja and has several
variables available to it such as:

dev_project_names - The names of the project currently under development
enabled_services - The services that are marked as enabled and are currently enabled
tag - The docker tag selected
registry - The docker registry from the config file
network - The docker network from the config file
core_prefix - The prefix to use for core containers
service_prefix - The prefix to use for service containers

Making services enabled/disabled
Sometimes it is desirable to not start all services when starting up the application
with the control script, but be able to start these services when needed. For example,
a service that takes a lot of resources to run and is rarely used may not need to
be started every time, but when testing functionality involving the service, it
may be enabled explicitly. Similarly, it may be desirable to be able to disable
specific services.
Simply add the enable: true or disable: true flags to your service definition in
config.yml to make the service disabled by default or enabled by default respectively.
Flags are added to the control script automatically based on the service name to
enable/disable the service.
For example, if a service needs to be disabled by default, in your config.yml:
projects:
project1:
...
service:
- name: service1
enable: true
...

This adds the --enable-service1 flag to the control script automatically and prevents
the service from being included in docker compose otherwise.
If you would like to use the same behavior in development projects, use the enabled_services
dictionary in your docker-compose-template.yml file to tell if a service should be included
or not:
services:
{%- if enabled_services.get('service1') %}
mynamespace_service1:
...
{%- endif %}

Using enable flags from other services
Sometimes it is desired to use a single enable flag to enable multiple services across projects.
This may be done by setting the enable or disable flags to the name of the service to use
for enabling/disabling. This requires that the other service also has the same flag set to true.
Multiple levels of enable/disable redirection are not possible (e.g. s1 is enabled with s2 which
is enabled with s3).
Create your own control script commands
It is easy to create additional commands using pydc_control.
#!/usr/bin/env python3

import argparse
import os
import sys

import pydc_control


def run_db_connect(args: argparse.Namespace):
"""
Connects to the mongo my_db database running on "my_mongo_container"
"""
pydc_control.call_commands(
['docker', 'exec', '-it', 'my_mongo_container', 'mongo', 'my_db']
)
return os.EX_OK


def configure_parsers(parser: argparse.ArgumentParser, commands_parser: argparse._SubParsersAction):
# DB
db_parser = commands_parser.add_parser(
'db',
help='Connects to the mongo database in an interactive shell',
)
db_parser.set_defaults(
func=run_db_connect,
)


if __name__ == '__main__':
base_path = os.path.realpath(os.path.join(os.path.dirname(__file__), '..'))
sys.exit(pydc_control.run(base_path, configure_parsers))

Frequently Asked Questions
Why do we duplicate docker compose configuration between projects and the control config?
While it may seem like duplicated code/configuration, the docker compose files in each
project and the configuration file in the control project serve different purposes and
often look different:

The configuration file in the control project should use a deployed (production or stage-like)
docker image and configuration for the project. The code for each project is not dynamically
modified and is usually promoted/deployed to environments outside of pydc-control.
The docker compose file in the individual (developed) project should use a development
build of the docker image with, ideally, application files and configuration mounted
dynamically into the container so that development is seamless and immediate.
Live-reloading should be used when available to speed development of a project.