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Object Lifecycle Management

MinIO Object Lifecycle Management allows creating rules for time or date based automatic transition or expiry of objects. For object transition, MinIO automatically moves the object to a configured remote storage tier. For object expiry, MinIO automatically deletes the object.

MinIO lifecycle management is built for behavior and syntax compatibility with AWS S3 Lifecycle Management. For example, you can export S3 lifecycle management rules and import them into MinIO or vice-versa. MinIO uses JSON to describe lifecycle management rules, and conversion to or from XML may be required.

Object Transition (“Tiering”)

MinIO supports creating object transition lifecycle management rules, where MinIO can automatically move an object to a remote storage “tier”. MinIO supports any S3-compatible service as a remote tier in addition to the following public cloud storage services:

MinIO object transition supports use cases like moving aged data from MinIO clusters in private or public cloud infrastructure to low-cost public cloud storage solutions.

MinIO manages retrieving tiered objects on-the-fly without any additional application-side logic. MinIO retains the minimum object metadata required to support retrieving objects transitioned to a remote tier. MinIO therefore requires exclusive access to the data on the remote storage tier. Object retrieval assumes no external mutation, migration, or deletion of stored objects. Object transition is not a replacement for backup/recovery strategies such as Bucket Replication.

Use the mc admin tier command to create a remote target for tiering data to a supported Cloud Service Provider object storage You can then use the mc ilm add command with one of the following commandline options to create new transition rules on a bucket:

Versioned Buckets

MinIO adopts S3 behavior for transition rules on versioned buckets. Specifically, MinIO by default applies the transition operation to the current object version.

To transition noncurrent object versions, specify the --noncurrentversion-transition-days option when creating the transition rule.

Object Expiration

MinIO lifecycle management supports expiring objects on a bucket. Object “expiration” involves performing a DELETE operation on the object. For example, you can create a lifecycle management rule to expire any object older than 365 days.

Use the mc ilm add command with one of the following commandline options to create new expiration rules on a bucket:

For buckets with replication configured, MinIO does not replicate objects deleted by a lifecycle management expiration rule. See Replication of Delete Operations for more information.

Versioned Buckets

MinIO adopts S3 behavior for expiration rules on versioned buckets. MinIO has two specific default behaviors for versioned buckets:

  • MinIO applies the expiration option to only the current object version by creating a DeleteMarker as is normal with versioned delete.

    To expire noncurrent object versions, specify the --noncurrentversion-expiration-days option when creating the expiration rule.

  • MinIO does not expire DeleteMarkers even if no other versions of that object exist.

    To expire delete markers when there are no remaining versions for that object, specify the --expired-object-delete-marker option when creating the expiration rule.

Lifecycle Management Object Scanner

MinIO uses a built-in scanner to actively check objects against all configured lifecycle management rules. The scanner is a low-priority process that yields to high IO workloads to prevent performance spikes triggered by rule timing. The scanner may therefore not detect an object as eligible for a configured transition or expiration lifecycle rule until after the lifecycle rule period has passed.

Delayed application of lifecycle management rules is typically associated to limited node resources and cluster size. Scanner speed tends to slow as clusters grow as more time is required to visit all buckets and objects. This can be exacerbated if the cluster hardware is undersized for regular workloads, as the scanner will yield to high cluster load to avoid performance loss. Consider regularly checking cluster metrics, capacity, and resource usage to ensure the cluster hardware is scaling alongside cluster and workload growth.