What are S3 lifecycle policies for noncurrent versions of objects?

[savas]

S3 lifecycle policies for noncurrent versions of objects are a powerful tool I find myself using often. They allow you to automate the management of your data stored on S3 by letting you specify rules for how long you want to keep noncurrent versions of your objects. You can think of noncurrent versions as the previous versions of an object in a versioned bucket. If you have versioning enabled on your S3 bucket, every time you upload a new version of an object, the old version gets pushed back as a noncurrent version.

To put it into perspective, let’s say you have a bucket where you store logs or backups, and you want to keep track of each update made over time. With versioning on, you maintain all versions of the objects, which is great for ensuring you can roll back to a previous iteration if something goes wrong with your current version. However, over time, the number of noncurrent versions can pile up and consume storage space. That’s where lifecycle policies come into play.

Creating a lifecycle policy for noncurrent version objects allows you to set specific actions based on the age of these objects. For instance, if you decide to keep noncurrent versions for 30 days, you can specify that in a lifecycle rule. After 30 days, any noncurrent version of an object automatically transitions to the storage class you defined, whether that's S3 Glacier or S3 Deep Archive. This action can lead to significant cost savings if you're dealing with a large volume of objects that accumulate over time.

I often set up different rules based on how I want to manage storage costs. For example, if I know that data doesn't need to be accessed frequently after a quarter, I might transition noncurrent versions that are over 90 days old to Glacier. Glacial storage is cheaper, but it does come with a caveat: retrieval times can be longer, so I factor in how likely I am to need those older versions. Additionally, after a specified period – say, after 365 days – I can configure the policy to permanently delete those noncurrent versions. This helps me avoid unnecessary storage costs and keeps my S3 bucket cleaner.

One thing to keep in mind with these policies is that they don’t affect the current version at all. You're working exclusively with noncurrent versions, and the lifecycle policies kick in only when the version achieves the specified state. For example, if you decide to delete noncurrent versions after 180 days, the current version will still remain in the bucket, intact. This distinction ensures that you can always access the latest version without worry.

Suppose you run a project that makes frequent updates to your objects, like a web application that serves user-generated content. You might find that users occasionally need to revert to older versions. In this case, preserving noncurrent versions for a set period can be essential. You can incorporate different lifecycle policies for various data segments based on the frequency of access or importance. For instance, I’ve implemented a policy where critical documents are kept for longer periods compared to ephemeral log files.

Another advanced strategy involves combining lifecycle policies with S3’s intelligent tiering. This way, I ensure that even the noncurrent versions that aren’t frequently accessed can be stored at a lower cost, while still being readily available when needed. You can set your S3 bucket to move noncurrent versions to the intelligent tier automatically after they turn noncurrent. This can happen after just a few days, especially if the data is rarely accessed.

It’s also worth mentioning that monitoring these lifecycle policies can lead to more effective storage management. AWS provides detailed metrics in CloudWatch that can help you keep track of your bucket’s storage usage and costs. I routinely check these metrics to ensure that the policies I’ve set up are working as intended. In some instances, I’ve realized that I could tighten the rules to save even more on costs, including reducing the time before transitioning noncurrent versions.

Setting up these rules does require attention to detail. You need to identify the correct bucket and versioning configuration before applying a lifecycle policy. If you mistakenly apply a policy to the wrong bucket, it can lead to unintended consequences, like the loss of important data. Always make sure to double-check your settings before you implement any changes.

You can set up lifecycle rules using either the AWS Management Console or the AWS CLI. Using the CLI can be much quicker if you need to deploy multiple policies or manage them programmatically. Writing the JSON policy document can guide you in specifying exactly how you want each rule to work. For example, something like this can represent a simple lifecycle rule you might use:


{
"Rules": [
{
"ID": "TransitionNoncurrentToGlacier",
"Status": "Enabled",
"NoncurrentVersionExpiration": {
"NoncurrentDays": 365
},
"NoncurrentVersionTransitions": [
{
"NoncurrentDays": 30,
"StorageClass": "GLACIER"
}
]
}
]
}


In this example, I set up a rule that transitions noncurrent versions to Glacier after 30 days and expires them entirely after 365 days. It’s all about finding the right balance between cost, accessibility, and data retention for your use case.

Remember that once you delete noncurrent versions, they cannot be recovered. That's why I recommend keeping an eye on your policies and ensuring that you have backup strategies in place, especially if you're managing sensitive or critical data. Setting reminders for periodic reviews of your lifecycle policies can be a good practice.

Managing S3 lifecycle policies, particularly for noncurrent versions, isn’t just a set-it-and-forget-it process. I find that I need to be proactive about monitoring and adjusting my policies based on how the data grows and changes over time. You might have to revisit your configurations if your application evolves, or if storage costs become a concern.

Put simply, S3 lifecycle policies offer a straightforward way for you to ensure that older versions of your data don’t keep costing you money indefinitely, while still enabling you to access them when you need to. Each use case will have its own specifications, but the flexibility and automation that these policies afford are invaluable for effective data management.