How do you automatically delete old versions of S3 objects?

[savas]

You’re looking to automatically delete old versions of S3 objects, and there are several ways to achieve this. You might have noticed that S3 provides a versioning feature, which is a double-edged sword. While it allows you to keep track of changes, it can also cause your storage to pile up with old versions, consuming more space and costs. I completely understand why you want to manage this effectively.

First, you’ll want to enable versioning on your S3 bucket if you haven’t already. Enabling versioning is pretty straightforward; just go to your bucket settings and toggle the versioning option. Once it's enabled, each time you upload an object with the same key, a new version is created. This is fantastic for keeping historical data, but the overhead can lead to a lot of unwanted data if you’re not careful.

To automate the deletion of old versions, consider using lifecycle policies. S3 lifecycle policies allow you to define rules that govern the lifecycle of your objects. You can specify how long to keep old versions before they’re automatically deleted. For example, you might set a rule that deletes versions older than 30 days. You would create a lifecycle configuration in JSON format. With AWS CLI, it would look something like this:

aws s3api put-bucket-lifecycle-configuration --bucket your-bucket-name --lifecycle-configuration '{
"Rules": [
{
"ID": "DeleteOldVersions",
"Status": "Enabled",
"Prefix": "",
"NoncurrentVersionExpiration": {
"NoncurrentDays": 30
}
}
]
}'


Once you’ve set this up, you won't have to worry about the old versions anymore. Every time a version exceeds the specified age, it will be deleted automatically. Make sure you configure it according to your needs. If you want to keep versions longer for compliance or backup reasons, feel free to adjust the "NoncurrentDays" value.

If you’re managing a lot of objects and can’t afford too much storage, you might also want to consider using S3 Intelligent-Tiering. This might not directly delete old versions, but it automatically moves objects between two access tiers when access patterns change, which can help reduce costs. Intelligent-Tiering, combined with the lifecycle policies, can work wonders in optimizing storage usage.

Another method I find useful involves writing a Lambda function that triggers based on S3 events or runs at regular intervals via CloudWatch Events. Using Lambda gives you more granular control over the deletion process. You could filter objects based on last modified timestamps and decide on criteria for deletion.

Here’s an example in Python using the "boto3" library to check for old versions and delete them:

import boto3
from datetime import datetime, timedelta

s3 = boto3.client('s3')

def delete_old_versions(bucket_name, days):
threshold_date = datetime.now() - timedelta(days=days)

paginator = s3.get_paginator('list_object_versions')
for page in paginator.paginate(Bucket=bucket_name):
for version in page.get('Versions', []):
version_last_modified = version['LastModified']
if version['IsLatest'] == False and version_last_modified < threshold_date:
print(f'Deleting {version["Key"]} version {version["VersionId"]}')
s3.delete_object(Bucket=bucket_name, Key=version['Key'], VersionId=version['VersionId'])

delete_old_versions('your-bucket-name', 30)


You would set this function to trigger on a schedule, maybe once a day using CloudWatch Events. Just make sure you test this thoroughly in a development environment before throwing it into production. You wouldn’t want to accidentally delete something important.

Another technique involves using the S3 Batch Operations. If you already have lists of objects or labels tagged in a certain way, you can create a job that deletes specific versions across multiple buckets or large sets of objects. You start by creating a manifest file, which includes the objects you want to process and their versions. Your manifest can be in CSV format, listing each object's bucket name, key, and version ID.

Once your manifest is ready, you create the batch job in the console or via AWS CLI. The job can be scheduled, and it works seamlessly in the background, managing the deletions as per your configurations. This method is more heavyweight and incurs charges for the batch operations, so you'll want to be mindful of costs, but it's perfect for large-scale cleanup tasks.

You might also want to consider tagging your objects for easier lifecycle management. Tagging gives you the ability to create more complex rules for your lifecycle policies based on object tags. You can assign a tag at upload, like “archive” or “important”, and then write lifecycle rules specifically for those tags.

One important point to keep in mind is your data retention policy. Understanding your organization’s regulations and compliance requirements is critical. If you're dealing with sensitive data or subject to certain regulations, ensure your deletion process aligns with the legal frameworks. Set rules conservatively if there's a chance you might need to consult older versions for compliance reasons.

If your requirements become complex, another option is to set up a state machine in AWS Step Functions. You could manage the complete lifecycle of your objects, with diverse paths for different versions based on policies you designate.

The beauty of combining these methods is that it not only automates the deletion process but provides flexibility and control. Depending on your specific needs, choose the combination that best fits your operational goals.

Keep in mind, whatever approach you take, monitoring the results is essential. Set up CloudWatch logs to trace what’s happening with your deletions, ensuring that the automation is working as intended. You can add SNS notifications to receive alerts if there’s an error during any of the deletion processes.

Test, monitor, iterate. That’s the cycle you'll follow. You’ll end up with a finely tuned environment, where old versions are handled just as you need them to be, without manually sifting through every layer of objects.

To sum it up, managing old S3 versions can be a seamless experience if you implement lifecycle rules effectively, possibly backed by Lambda functions or Batch Operations, depending on your project's scale and nature. Happy coding, and let me know how it goes for you!