Skip to content

Latest commit

 

History

History
108 lines (71 loc) · 5.29 KB

README.md

File metadata and controls

108 lines (71 loc) · 5.29 KB
Raw

Django-Celery-SQS-AWSEB

Deploying Django application with Celery and Redis as broker on AWS Elastic Beanstalk

UPDATE: Elastic Cache Redis instance on AWS can cost you much more than SQS will, so I have switched from Redis to SQS. SQS even has 1 million requests free every month.

CAVEAT: Only thing that SQS Celery broker lacks is the result backend, which is not yet available for SQS, which basically means that you cannot store the results that are returned from your tasks anywhere,

result = some_task.delay() result.get() # gives error -> Result backend 'sqs' not found.

Getting Started

To get started you need to first launch a Elastic Beanstalk environment inside a VPC. I assume you have a VPC already created if not create.

NOTE: AWS RDS won't create a Subnet Group until you have subnets in two availability zones. So for that go into your VPC console > Subnets then create two new subnets with availability zones different from your default subnets, if you have any problems with the ** IPv4 CIDR block** then enter 10.0.2.0/24 and 10.0.3.0/24. If you have another range for example 35.23.2.0/24 then use that but these CIDR Blocks should not overlap with the existing ones.

Now run the following commands for launching EB Environment inside newly created VPC.

  1. eb create test -db.engine postgres -db.user rootuser -db.pass rootpass --vpc --vpc.dbsubnets xxxxxxxxxxx,xxxxxxxxxxx,xxxxxxxxxxxxx,xxxxxxxxxxxx

    --vpc.dbsubnets -> Subnets of the RDS that you just created

  2. You will be prompted to enter some details about the VPC you have just created like the VPC Id which you can get from the VPC Console and you should assign a public IP address and for the EC2 instance subnet enter the private subnet which defaults to 10.0.1.0/24 and for the EB subnet groups enter the public subnet which defaults to 10.0.0.0/24.

  3. Assign a external/public load balancer and for the security group enter the default VPC Security group that is newly created by your VPC in the section VPC Console > Security Groups.

  4. The EB Env should take about 5 to 10 minutes to finish and start.

  5. Now if you haven't set the WSGIPath setting then use eb config and in the window that appears find the WSGIPath and replace that path with your django project wsgi.py file like, WSGIPath: boilerplate/wsgi.py. Save the file and close the window, the env will start updating.

  6. Run eb status and copy paste the CNAME in your ALLOWED_HOSTS settings.

  7. Deploy the application and you run eb open.

SQS

Head to the AWS SQS (Simple Queue Service) console and Create New Queue.

  1. Enter the name of the queue.

  2. Standard Queue should work with most of the use cases.

  3. In Configure Queue you can use the Use SE.

  4. Click create queue

Copy the queue name and paste it in the CELERY_DEFAULT_QUEUE setting in your settings.py file, you can even use the environment variables of your elasticbeanstalk environment to hide the name of your queue like I did in my settings.

With this done, you also need a AWS IAM User with a Role or Group that has permissions to access the Simple Queue Service, you can create it easily with programmatic access and use the credentials generated in your environment.

Define two environment variables namely, AWS_ACCESS_KEY_ID & AWS_SECRET_ACCESS_KEY by EB Console > Configuration > Software.

Create a file named celeryapp.py along side your settings.py file with code same as this repo contains. You may need to change some things like,

os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'boilerplate.settings') -> boilerplate is my project name yours might be different, change it everywhere.

In your settings define some more celery configuration settings like

CELERY_DEFAULT_QUEUE = os.getenv('CELERY_DEFAULT_QUEUE') # name of your sqs queue

AWS_SECRET_ACCESS_KEY = os.getenv('AWS_SECRET_ACCESS_KEY')

CELERY_RESULT_BACKEND = None  # Disabling the results backend since not supported with SQS

BROKER_TRANSPORT_OPTIONS = {
    'polling_interval': 20,
    'region': 'ap-south-1',
}
BROKER_URL = "sqs://"

The BROKER_URL here takes only "sqs://" and the rest of the things are done by celery itself by colleting the necessary credentials for access to this SQS queue using the credentials we have set in your environment variables.

NOTE: I first used celery.py named file that was causing some errors despite of the absolute_import on top of the file so finally I changed the file name to celeryapp.py and that worked.

Also you need the .ebextensions folder my this repo to run the celery worker and don't forget to install and use the requirements.txt file same as mine.

We use pooling in celery worker from the gevent package by providing and extra argument

celery -A boilerplate worke -l Info -P gevent --app=boilerplate.celeryapp:app

--app is to give the location of our celery app since we have changed the file names from celery.py to celeryapp.py.

I have tested everything and my repo works perfectly fine in the elastic beanstalk environment. If you face any problems checkout the repo code and make changes accordingly in case I have missed something in the instructions or open a issue.