# Celery-S3
[](https://travis-ci.org/robgolding/celery-s3)
[](https://coveralls.io/github/robgolding/celery-s3?branch=master)
Celery-S3 is a simple S3 result backend for Celery.
If used in conjunction with the SQS broker, it allows for Celery deployments
that use only distributed AWS services -- with no dependency on individual
machines within your infrastructure.
This backend probably isn't suitable for particularly high-traffic Celery
deployments, but it works just fine in general -- and imposes no limits on the
number of workers in the pool.
## Installation
Install via pip:
`pip install celery-s3`
Then configure Celery to use the `S3Backend`:
CELERY_RESULT_BACKEND = 'celery_s3.backends.S3Backend'
CELERY_S3_BACKEND_SETTINGS = {
'aws_access_key_id': '<your_aws_access_key_id>',
'aws_secret_access_key': '<your_aws_secret_access_key>',
'bucket': '<your_bucket_name>',
}
## Configuration
To use a folder within the specified bucket, set the `base_path` in your
`CELERY_S3_BACKEND_SETTINGS`:
CELERY_S3_BACKEND_SETTINGS = {
...
'base_path': '/celery/',
...
}
To use a region other than the default (`us-east-1`), set the `aws_region`
parameter:
CELERY_S3_BACKEND_SETTINGS = {
...
'aws_region': 'us-east-1',
...
}
To use [reduced redundancy storage](https://aws.amazon.com/s3/reduced-redundancy/),
set the `reduced_redundancy` parameter:
CELERY_S3_BACKEND_SETTINGS = {
...
'reduced_redundancy': True,
...
}
To use [server-side encryption](http://docs.aws.amazon.com/AmazonS3/latest/dev/serv-side-encryption.html),
set the `encrypt_key` parameter:
CELERY_S3_BACKEND_SETTINGS = {
...
'encrypt_key': True,
...
}
## Notes
Storing Celery results with this backend will obviously result in API calls
being made to Amazon S3. For each result, at least one `PUT` request will be
made (priced at $0.01 per 1,000 requests at the time of writing). Also, the
data contained within the result object will be stored indefinitely, unless
otherwise specified.
To fetch a result for a task that has already finished, at least two requests
will be made (one `HEAD` and one `GET`). If you use Celery's `result.get()` to
wait for a task to finish, S3 will be polled continuously until the task has
finished.
By default, the poll interval is set to 0.5 seconds, which could result in
a lot of requests (two `HEAD` requests per second until the task has finished,
then one `GET` request to fetch the result). If you need to use
`result.get()`, consider increasing the interval and using a timeout to prevent
polling forever: `result.get(interval=5, timeout=600)`.
Also, for tasks whose result you don't need, be sure to use `ignore_result`:
@celery.task(ignore_result=True)
def process_data(obj):
obj.do_processing()
Once task results have been used and are no longer needed, be sure to call
`result.forget()` to delete the corresponding S3 key. Otherwise, old results
will remain forever and contribute to storage costs (storage is priced at
$0.095 per GB per month at the time of writing).
Also, the S3 lifecycle can be used to archive or delete old keys after
a certain period of time.
