A Django project template for a RESTful Application using Docker
I used what I learn and some decisions to create a template for new projects. Part of software development is mainly plumbing. Laying bricks together and connecting parts so the important bits of software can be accessing. That’s a pretty important part of the work, but it can be quite tedious and frustrating.
This is somehow a very personal work. I am using my own opinionated ideas for it, but I’ll explain the thought process behind them. Part of the idea is to add to the discussion on how a containerised Django application should work and what is the basic functionality that is expected for a fully production-ready service.
The code is available here. This blog post covers more the why’s, while the README in the code covers more the how’s.
GO CHECK THE CODE IN GITHUB!
- a Django RESTful project template
- sets up a cluster of Docker containers using
- include niceties like logging, system tests and metrics
- code extensively commented
- related blog post explaining the why and opening the discussion to how to work with Docker
Docker is having a lot of attention in the last few years, and it promises to revolutionise everything. Part of it is just hype but nevertheless is a very interesting tool. And as every new tool, we’re still figuring out how it should be used.
The first impression when dealing with Docker is to treat it as a new way of a virtual machine. Certainly, it was my first approach. But I think is better to think of it a process (a service) wrapped in a filesystem.
All the Dockerfile and image building process is to ensure that the filesystem contains the proper code and configuration files to then start the process. This goes against daemonising and other common practices in previous environments, where the same server handled lots of processes and services working in unison. In the Docker world, we replace this with several containers working together, without sharing the same filesystem.
The familiar Linux way of working includes a lot of external services and conveniences that are no longer required. For example, starting multiple services automatically on boot makes no sense if we only care to run one process.
This rule has some exceptions, as we’ll see later, but I think is the best approach. In some cases, a single service requires multiple processes, but simplicity is key.
The way Docker file system work is by adding a layer on top of another layer. That makes the build system to series of steps that execute a command that changes the filesystem and then exits. The next step builds on top of the other.
This has very interesting properties, like the inherent caching system, which is very useful for the build process. The way to create a Dockerfile is to set the parts that are least common to change (e.g. dependencies) on top, and put at
the end of it the ones that are most likely to need to be updated. That way, build times are shorter, as only the latest steps need to be repeated.
Another interesting trick is to change the order of the steps in the Dockerfile while actively developing, and move them to their final place once the initial setup (packages installed, requirements, etc) is stable.
Another property is the fact that each of these layers can only add to a filesystem, but never subtract. Once a build operation has added something, removing it won’t free space of the container. As we want to keep our containers as minimal as possible, care on each of the steps should be taken. In some cases, this means to add something, use it for an operation, and then remove it in a single step. A good example is compilation libraries. Add the libraries, compile, and then remove them as they won’t be used (only the generated binaries).
Given this minimalistic approach, it’s better to start as small as possible. Alpine is a Linux distribution focused on minimalistic containers and security. Their base image is just around 4MB. This is a little misleading, as installing Python will bring it to around 70MB, but it’s still much better than something like an Ubuntu base image, at around 120MB start and very easy to get to 1GB if the image build is done in a traditional way, installing different services and calling
apt-get with abandon.
This template creates an image around 120MB size.
Running a cluster
A single container is not that interesting. After all is not much more than a single service, probably a single process. A critical tool to work with containers is to be able to set several of them to work in unison.
The chosen tool in this case is docker-compose which is great to set up a development cluster. The base of it is the
docker-compose.yaml file, that defines several “services” (containers) and links them all together. The
docker-compose.yaml file contains the names, build instructions, dependencies and describes the cluster.
Note there are two kinds of services. One is the container that runs and ends, producing some result as an operation. For example, run the tests. It starts, runs the tests, and then ends.
The other one is to run a long running service. For example, run a web server. The server starts and it doesn’t stop on its own.
In the docker-compose there are both kind of services.
db are long running services, while
system-test are operations, but most of them are services.
It is possible to differentiate grouping them in different files, but dealing with multiple
docker-compose.yaml files is cumbersome.
The different services defined, and their relationships, are described in this diagram. All the services are described individually later.
As it is obvious from the diagram, the main one is
server. The ones in yellow are operations, while the ones in blue are services.
Note that all services exposes their services in different ports.
All the files that relates to the building of containers of the cluster are in the
./docker subdirectory, with the exception of the main
docker-compose.yaml, that are in the root directory.
./docker directory, there’s a subdir for each service. Note that, because the image is the same, some services like
test inherits the files under
The template Django app
The main app is in the directory
./src and it’s a simple RESTful application that exposes a collection of tweets, elements that have a text, a timestamp and an id. Individual tweets can be retrieved and new ones can be created. A basic CRUD interface.
On top of that, there are unit tests stored in Django common way (inside
./src/tweet/tests.py. To run the tests, it makes usage of pytest and pytest-django. Pytest is a very powerful framework for running tests, and it’s worth to spend some time to learn how to use it for maximum efficiency.
All of this is the core of the application and the part that should be replaced for doing interesting stuff. The rest of it is plumbing to making this code to run and to have it properly monitored. There are also system tests, but I’ll talk about them later.
The application is labelled as
templatesite. Feel free to change the name to whatever makes sense for you.
The main server
server service is the core of the system. Though the same image is used for multiple purposes, the main idea is to set up the Django code and make it run in a web server.
As shown, the nginx process will serve the static files, as generated by Django
collectstatic command, and redirect everything else towards a uWSGI container that runs the Django code. They are connected by a UNIX socket.
Another decision has been to create a single worker on the container. This follows the minimalistic approach. Also, Prometheus (see below) doesn’t like to be round robin behind a load balancer in the same server, as the metrics reported are inconsistent.
It is also entirely possible to run just uWSGI and create another container that runs nginx and handles the static files. I chose not to because this creates a single HTTP server node. Exposing HTTP with uWSGI is not as good as with nginx, and you’ll need to handle the static files externally. Exposing uWSGI protocol externally is complicated and will require some weird configuration in the nginx frontend. This makes a totally self-contained stateless web container that has the whole functionality.
The database container is mainly a PostgreSQL database, but a couple of details have been added to its
After installing the database, we add our Django code, install it, and then run the migrations and load the configured fixtures. All of this at build time. This makes the base image to contain an empty test database and a pre-generated general database, helping for quick setup of tests. To get a fresh database, just bring down the
db container and restart it. No rebuild is needed unless there are new migrations or new fixtures.
In my experience with Django, as project grow and migrations are added, it slowly takes more and more time to run the tests if the database needs to be regenerated and fixtures to be loaded again. Even if the
--keepdb option is used from tests, sometimes a fresh start is required.
Another important detail is that this database doesn’t store data in a persistent volume, but just inside the container. This is aimed not to work as a persistent database, but to run quickly and to be able to be regenerated into a known state with ease. If you need to change the start data, change the fixtures loaded. Only put inside things you are ok losing.
As part of the setup, notice that the following happens. The database needs to be started and then another process, the Django
manage.py, loads the fixtures. Then the database is turned down and the container exists. This is one of the cases where multiple processes need to run in a container. The turn down is important, as ending the PostgreSQL process abruptly can lead to data corruption. Normally on the next startup of the database, it will be corrected, but it will take a little time. It’s better to end the step cleanly.
Logging events is critical for a successful operation in a production environment and typically is done very late in the development process. I try to introduce logging as I run my unit tests and add different information while developing, which it helps quite a lot in figuring out what’s going on on production servers.
A big part of the pain of logging is to set up properly the routing of the logs. In this case, there’s a dedicated
log container running
syslog where all the INFO and up logs from the server are directed. The collected logs are stored in a file on the container that can be easily checked.
All the requests are also labelled with a unique id, using the Django log request id middleware. The id can also be forwarded through the X-REQUEST-ID HTTP header, and it will be returned in the responses. All the logs from a request will include this ID, making easy to follow what the request has done.
When running the unit tests, the DEBUG logs are also directed to the standard output, so they’ll show as part of the
pytest run. Instead of using print in your unit test while debugging a problem, try to use logging and keep what it makes sense. This will keep a lot of useful information around when a problem arises in production.
There’s also included a Grafana container, called
metrics-graph. Note that these two containers are being pulled from their official images, instead of including a tailored
metrics container has some minimal configuration. This is because the only requirement is to expose the metrics in a Prometheus format, but creating dashboards or making more detailed work on metrics is out of the scope for this.
The good thing about Prometheus is that you can cascade it. It works by fetching the data from our web service itself (through the
/metrics URL), and at the same time it exposes the same URL with all the data it pools. This makes possible to very easily create a hierarchical structure where a container picks information about a few servers and then exposes the information to another one, that groups the data from a few Prometheus containers.
Prometheus query language and metrics aggregation are very powerful, but at the same time is very confusing initially. The included console has a few queries for interesting data in Django.
The codebase includes two subdirectories,
./vendor. The first one is to include your direct dependencies. That mean your own code, that lives in a different repo. This allows you to set a git submodule and use it as a imported module. There’s a README file to show some tips on using git submodules, as they are a little tricky.
The idea behind this is to avoid the usage of git pulling from a private repo inside a requirements file, which requires setup of authentication inside the container (adding ssh keys, git support, etc). I think is better to handle that at the same level as your general repo, and then import all the source code directly.
./vendor is a subdirectory to contain a cache of python modules in wheel format. The service
build-deps builds a container with all the stated dependencies in the requierements.txt file and precompile them (among all sub-dependencies) in convenient wheel files. Then the wheel files can be used to speed up the setup of other containers. This is optional, as the dependencies will be installed in any case, but greatly speeds up rebuilding containers or tweaking requirements.
Testing and interactive development
test runs the unit tests. It doesn’t depend on any external service and can be run in isolation.
dev-server starts the Django development web server. This reloads the code as it changes, as it mounts the local directory. It also logs the
runserver logs into standard output.
system-test independently run tests generating external HTTP requests against the
server. They are under the
./system-test subdir and are run using pytest.
Docker container can define a healthcheck to determine whether a container is properly behaving, and take action if necessary. The application includes a URL root for the heathcheck that currently is checking if the access to the database is correct. More calls to external services can be included in this view.
The healthcheck pings using curl the local nginx URL, so it also tests the routing of the request is correct.
- Though this project aims to have something production-ready, the discussion on how to do it is not trivial. Expect details to require to be changed depending on the system to bring the container to a production environment.
- Docker has some subtleties that are worth paying attention, like the difference between
run. It pays up to try to read with care the documentation and understand the different options and commands.
- Secrets management has been done through environment variables. The only real secrets in the project are the database password and the Django secret key. Secret management may depend on what system you use in production. Using docker native secret support for Docker swarm create files inside the container that can be poured into the environment with the start scripts. Something like adding to
export SECRET=`cat /run/secret/mysecret`
The Django secret key is injected as part of the build as an ARG. Check that’s consistent in all builds at docker-compose.yaml. The database password is stored in an environment variable.
- The ideal usage of containers in CI should be to work with them locally, and when pushed to the repo, trigger a chain of build container, run tests and promote the build until deployment, probably in several stages. One of the best usages of containers is to be able to set them up and not change them all along the process, which was as easy as it sounds with traditional build agents and production servers.
- All ideas welcome. Feel free to comment here or in the GitHub repo.