How much time will it take for S3 Lifecycle Policy to transfer 30,000objects with 500gb file size from Standard to Glacier

Lifecycle rules are executed internally by S3, and S3 takes care of executing them at arbitrarily massive scale without negatively affecting your workload or those of other customers.

If you're concerned primarily with the cost impact of the storage class transitions, the rule is documented on this document page under the title "Changes in billing": https://docs.aws.amazon.com/AmazonS3/latest/userguide/object-lifecycle-mgmt.html. In short, from a billing standpoint, the object transitions from Standard to any of the Glacier classes take effect at midnight UTC on the day your rule is configured to transition them, regardless of it possibly taking 1-3 days for the changes actually to get completed.

Hi startupagile_dev,

Please go through the below steps and documentation links, I hope it will help to solve your issue.

Transitioning Large Files Using S3 Lifecycle Policies

1. Lifecycle Policy Evaluation and Transition

• Evaluation Frequency: Amazon S3 Lifecycle Policies are evaluated approximately once every 24 hours. When the policy is evaluated, S3 identifies objects that meet the transition criteria and initiates the transition process.

Reference: https://docs.aws.amazon.com/AmazonS3/latest/userguide/object-lifecycle-mgmt.html

• Transition Process: Transitioning an object to Amazon S3 Glacier involves changing the object's storage class metadata. This operation is a metadata update and typically reflects almost instantly from a user's perspective.

Reference: https://docs.aws.amazon.com/AmazonS3/latest/API/API_Transition.html

2. Handling Large Files (500GB Each)

• Metadata Operation: The transition process primarily involves updating the storage class metadata rather than physically moving the data. Therefore, the size of individual files does not impact the transition speed itself.

Reference: https://docs.aws.amazon.com/AmazonS3/latest/userguide/storage-class-intro.html

• Batch Processing: AWS S3 handles transitions efficiently by processing them in batches and leveraging parallelism. This allows for scalable management of large volumes of objects.

Reference: https://docs.aws.amazon.com/AmazonS3/latest/userguide/optimizing-performance-guidelines.html

3. Estimated Timing and Performance Considerations

• Transition Timing: After a lifecycle policy is evaluated, the transition process typically starts within hours. While metadata changes are quick, the full transition to Glacier might be fully reflected within a few hours to a day.

Reference: https://docs.aws.amazon.com/AmazonS3/latest/userguide/lifecycle-transition-general-considerations.html

• AWS Internal Management: AWS’s architecture is designed to handle transitions at scale with efficient parallel processing. The system manages batching to optimize performance and reduce potential bottlenecks.

Reference: https://docs.aws.amazon.com/whitepapers/latest/how-aws-pricing-works/amazon-simple-storage-service-amazon-s3.html

4. Key Factors Affecting Transition Time

• AWS Capacity Management: AWS manages transitions through scalable infrastructure, processing large-scale operations efficiently. The system is designed to handle high volumes of transitions without significant throttling.

Reference: https://docs.aws.amazon.com/AmazonS3/latest/userguide/security-best-practices.html

• Transition Visibility: The transition status and progress can be monitored using S3 management tools and AWS CloudWatch metrics. While the metadata update is fast, full visibility might take some time due to propagation delays.

Reference: https://docs.aws.amazon.com/AmazonS3/latest/userguide/cloudwatch-monitoring.html

5. Recommendations for Large Transitions

• Monitor Progress: Utilize AWS CloudWatch and S3 monitoring tools to track the status of your lifecycle transitions and verify that policies are applied correctly.

Reference: https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/WhatIsCloudWatch.html

• Test Transition Policies: Before applying lifecycle policies to large datasets, test them with smaller subsets to ensure that they work as expected.

• Engage AWS Support: For critical or complex scenarios, consider leveraging AWS Support to ensure proper configuration and resolve any issues.

Reference: https://docs.aws.amazon.com/awssupport/latest/user/aws-support-plans.html