Announcing Changed Block Tracking API support (alpha)

Announcing Changed Block Tracking API support (alpha) What is changed block tracking? Benefits of changed block tracking support in Kubernetes Key components Implementation requirements Storage provider responsibilities Backup solution responsibilities Getting started What’s next? How do I get involved? We're excited to announce the alpha support for a changed block tracking mechanism. This enhances the Kubernetes storage ecosystem by providing an efficient way for CSI storage drivers to identify changed blocks in PersistentVolume snapshots. With a driver that can use the feature, you could benefit from faster and more resource-efficient backup operations. If you're eager to try this feature, you can skip to the Getting Started section. Changed block tracking enables storage systems to identify and track modifications at the block level between snapshots, eliminating the need to scan entire volumes during backup operations. The improvement is a change to the Container Storage Interface (CSI), and also to the storage support in Kubernetes itself. With the alpha feature enabled, your cluster can: Identify allocated blocks within a CSI volume snapshot Determine changed blocks between two snapshots of the same volume Streamline backup operations by focusing only on changed data blocks For Kubernetes users managing large datasets, this API enables significantly more efficient backup processes. Backup applications can now focus only on the blocks that have changed, rather than processing entire volumes. As Kubernetes adoption grows for stateful workloads managing critical data, the need for efficient backup solutions becomes increasingly important. Traditional full backup approaches face challenges with: Long backup windows : Full volume backups can take hours for large datasets, making it difficult to complete within maintenance windows. High resource utilization : Backup operations consume substantial network bandwidth and I/O resources, especially for large data volumes and data-intensive applications. Increased storage costs : Repetitive full backups store redundant data, causing storage requirements to grow linearly even when only a small percentage of data actually changes between backups.