If you use RabbitMQ for development frequently, sometimes you might have found it uses too much resources (it’s normal while programming to have a lot of queues or tasks being queued and that makes the CPU usage to spike).
Having RabbitMQ installed on the OS seems the perfect approach for production, but
on development I’d rather do something different, in order to isolate
the process. I know I could bound it (for example order it not to start automatically
as a dæmon), by means of
systemd but a few weeks ago I decided to try
docker and see how it results.
It turned out to be just the tool for the work, and so far with a little simple configuration it can run as expected.
There is already a docker image for RabbitMQ, which can be automatically pulled, and then run, for example:
sudo docker pull rabbitmq sudo docker run -d -e RABBITMQ_NODENAME=my-rabbit --cpuset="1" --name docker.rabbitmq -p 5672:5672 rabbitmq:3
-d option indicates the process to start detached, then by passing
-e we pass some environment variables (in this case, the
RABBITMQ_NODENAME is a particular variable for rabbit indicating
how to set the name of the node it is starting). Optionally, we can limit the CPU
usage with the
--cpuset, as in this case which sets the process to use the
second core of the machine (it starts at 0). Then the
--name is a name for the docker being created.
The most important part in this case is the port mapping, made by the
-p option which in this case maps the
port used by RabbitMQ directly (1:1) with the host machine. This makes the docker process to run transparently, as
the other applications that try to communicate with a RabbitMQ won’t notice any difference, making it look like is
executing an actual RabbitMQ service. Finally there is the name of the docker image to run.
What I usually do is to manage the docker image by its instance_id (a number that is displayed after listing
the docker images, by doing
sudo docker ps -a).
Then we can manage it by
sudo docker [start|stop] <instance_id>.
There is another command to see the output being generated by the process
docker logs rabbitmq.docker. Notice in this case the name
designated to the image was used instead of the instance_id. In addition
we can see internal data for the process by running the
command (again we can use the instance_id or the name we assigned).
It’s important to notice that docker is actually not a virtualization platform, but a mechanism that runs processes
in containers, meaning that in this case the entire RabbitMQ is running as a single process within a container, with some
other limitations and bounds constrained by
I found this approach to be very versatile for a development environment, and with RabbitMQ being the first pilot, I think I can migrate more applications to docker instead of having them installed on the system (as long as possible).