Celery is an asynchronous task queue based on distributed message passing. It is used to create execution units (i.e. tasks) which are then executed on one or more worker nodes, either synchronously or asynchronously.
Celery is implemented in Python.
Since Celery is based on message passing, it requires some middleware (to handle translation of the message between sender and receiver) called a message broker. RabbitMQ is a message broker often used in conjunction with Celery.
This example will show you how to use Kubernetes to set up a very basic distributed task queue using Celery as the task queue and RabbitMQ as the message broker. It will also show you how to set up a Flower-based front end to monitor the tasks.
At the end of the example, we will have:
- Three pods:
- A Celery task queue
- A RabbitMQ message broker
- A Flower frontend
- A service that provides access to the message broker
- A basic celery task that can be passed to the worker node
You should already have turned up a Kubernetes cluster. To get the most of this example, ensure that Kubernetes will create more than one minion (e.g. by setting your NUM_MINIONS environment variable to 2 or more).
The Celery task queue will need to communicate with the RabbitMQ broker. RabbitMQ will eventually appear on a separate pod, but since pods are ephemeral we need a service that can transparently route requests to RabbitMQ.
Use the file examples/celery-rabbitmq/rabbitmq-service.json:
{
"id": "rabbitmq-service",
"kind": "Service",
"apiVersion": "v1beta1",
"port": 5672,
"containerPort": 5672,
"selector": {
"app": "taskQueue"
},
"labels": {
"name": "rabbitmq-service"
}
}To start the service, run:
$ kubectl create -f examples/celery-rabbitmq/rabbitmq-service.jsonNOTE: If you're running Kubernetes from source, you can use cluster/kubectl.sh instead of kubectl.
This service allows other pods to connect to the rabbitmq. To them, it will be seen as available on port 5672, although the service is routing the traffic to the container (also via port 5672).
A RabbitMQ broker can be turned up using the file examples/celery-rabbitmq/rabbitmq-controller.json:
{
"id": "rabbitmq-controller",
"kind": "ReplicationController",
"apiVersion": "v1beta1",
"desiredState": {
"replicas": 1,
"replicaSelector": {"name": "rabbitmq"},
"podTemplate": {
"desiredState": {
"manifest": {
"version": "v1beta1",
"id": "rabbitmq",
"containers": [{
"name": "rabbitmq",
"image": "dockerfile/rabbitmq",
"cpu": 100,
"ports": [{"containerPort": 5672, "hostPort": 5672}]
}]
}
},
"labels": {
"name": "rabbitmq",
"app": "taskQueue"
}
}
},
"labels": {
"name": "rabbitmq"
}
}Running $ kubectl create -f examples/celery-rabbitmq/rabbitmq-controller.json brings up a replication controller that ensures one pod exists which is running a RabbitMQ instance.
Note that bringing up the pod includes pulling down a docker image, which may take a few moments. This applies to all other pods in this example.
Bringing up the celery worker is done by running $ kubectl create -f examples/celery-rabbitmq/celery-controller.json, which contains this:
{
"id": "celery-controller",
"kind": "ReplicationController",
"apiVersion": "v1beta1",
"desiredState": {
"replicas": 1,
"replicaSelector": {"name": "celery"},
"podTemplate": {
"desiredState": {
"manifest": {
"version": "v1beta1",
"id": "celery",
"containers": [{
"name": "celery",
"image": "endocode/celery-app-add",
"cpu": 100,
"ports": [{"containerPort": 5672, "hostPort": 5672}]
}]
}
},
"labels": {
"name": "celery",
"app": "taskQueue"
}
}
},
"labels": {
"name": "celery"
}
}There are several things to point out here...
Like the RabbitMQ controller, this controller ensures that there is always a pod is running a Celery worker instance. The celery-app-add Docker image is an extension of the standard Celery image. This is the Dockerfile:
FROM dockerfile/celery
ADD celery_conf.py /data/celery_conf.py
ADD run_tasks.py /data/run_tasks.py
ADD run.sh /usr/local/bin/run.sh
ENV C_FORCE_ROOT 1
CMD ["/bin/bash", "/usr/local/bin/run.sh"]
The celery_conf.py contains the defintion of a simple Celery task that adds two numbers. This last line starts the Celery worker.
NOTE: ENV C_FORCE_ROOT 1 forces Celery to be run as the root user, which is not recommended in production!
The celery_conf.py file contains the following:
import os
from celery import Celery
# Get Kubernetes-provided address of the broker service
broker_service_host = os.environ.get('RABBITMQ_SERVICE_SERVICE_HOST')
app = Celery('tasks', broker='amqp://guest@%s//' % broker_service_host, backend='amqp')
@app.task
def add(x, y):
return x + yAssuming you're already familiar with how Celery works, everything here should be familiar, except perhaps the part os.environ.get('RABBITMQ_SERVICE_SERVICE_HOST'). This environment variable contains the IP address of the RabbitMQ service we created in step 1. Kubernetes automatically provides this environment variable to all containers which have the same app label as that defined in the RabbitMQ service (in this case "taskQueue"). In the Python code above, this has the effect of automatically filling in the broker address when the pod is started.
The second python script (run_tasks.py) periodically executes the add() task every 5 seconds with a couple of random numbers.
The question now is, how do you see what's going on?
Flower is a web-based tool for monitoring and administrating Celery clusters. By connecting to the node that contains Celery, you can see the behaviour of all the workers and their tasks in real-time.
To bring up the frontend, run this command $ kubectl create -f examples/celery-rabbitmq/celery-controller.json. This controller is defined as so:
{
"id": "flower-controller",
"kind": "ReplicationController",
"apiVersion": "v1beta1",
"desiredState": {
"replicas": 1,
"replicaSelector": {"name": "flower"},
"podTemplate": {
"desiredState": {
"manifest": {
"version": "v1beta1",
"id": "flower",
"containers": [{
"name": "flower",
"image": "endocode/flower",
"cpu": 100,
"ports": [{"containerPort": 5555, "hostPort": 5555}]
}]
}
},
"labels": {
"name": "flower",
"app": "taskQueue"
}
}
},
"labels": {
"name": "flower"
}
}This will bring up a new pod with Flower installed and port 5555 (Flower's default port) exposed. This image uses the following command to start Flower:
flower --broker=amqp://guest:guest@${RABBITMQ_SERVICE_SERVICE_HOST:localhost}:5672//Again, it uses the Kubernetes-provided environment variable to obtain the address of the RabbitMQ service.
Once all pods are up and running, running kubectl get pods will display something like this:
POD IP CONTAINER(S) IMAGE(S) HOST LABELS STATUS
celery-controller-h3x9k 10.246.1.11 celery endocode/celery-app-add 10.245.1.3/10.245.1.3 app=taskQueue,name=celery Running
flower-controller-cegta 10.246.2.17 flower endocode/flower 10.245.1.4/10.245.1.4 app=taskQueue,name=flower Running
kube-dns-fplln 10.246.1.3 etcd quay.io/coreos/etcd:latest 10.245.1.3/10.245.1.3 k8s-app=kube-dns,kubernetes.io/cluster-service=true Running
kube2sky kubernetes/kube2sky:1.0
skydns kubernetes/skydns:2014-12-23-001
rabbitmq-controller-pjzb3 10.246.2.16 rabbitmq dockerfile/rabbitmq 10.245.1.4/10.245.1.4 app=taskQueue,name=rabbitmq Running
Now you know on which host Flower is running (in this case, 10.245.1.4), you can open your browser and enter the address (e.g. http://10.245.1.4:5555. If you click on the tab called "Tasks", you should see an ever-growing list of tasks called "celery_conf.add" which the run_tasks.py script is dispatching.