Storage efficiency is a constant battle in the world of virtualization. With Storage Spaces Direct (S2D) clusters offering hyper-converged infrastructure magic, data deduplication can be a powerful tool to maximize storage utilization. But before diving into S2D specifics, let’s explore the broader world of data deduplication.
Understanding Data Deduplication: File vs. Block Level
Data deduplication identifies and eliminates redundant data copies within a storage system. There are two main approaches:
- File Level Deduplication (FLED): Examines entire files for duplicates. Ideal for scenarios with many identical files (think user profile folders).
- Block Level Deduplication (BLD): Breaks down files into smaller blocks, identifying and deduplicating identical blocks across different files. More efficient for storing unique data with recurring patterns (like virtual machine disks).
Choosing the Right Technology for the Workload
The best deduplication approach depends on your workload:
- Virtual Desktops (VDI): VDI deployments often have near-identical user desktops. FLED shines here, significantly reducing storage requirements.
- File Servers: File servers might house a mix of unique and redundant data. BLD can efficiently handle the mix, while FLED can target specific file types for additional savings.
- Databases: Databases often have repetitive data structures. BLD can optimize storage usage, but ensure your database software is compatible with deduplication.
| Deduplication usage type | Intended workload | Configuration settings |
| Default | General purpose file servers | Background optimization Default optimization policy: Minimum file age = 3 days Optimize in-use files = no Optimize partial files = no |
| Hyper-V | VDI | Background optimization Default optimization policy: Minimum file age = 3 days Optimize in-use files = yes Optimize partial files = yes |
| Backup | Virtualized backup apps (for example, DPM) | Priority optimization Default optimization policy: Minimum file age = 0 days Optimize in-use files = yes Optimize partial files = no |
Efficiency with S2D Deduplication
Windows Server 2019 introduced data deduplication support for S2D clusters. This enables storage savings on top of the inherent efficiency of S2D’s software-defined storage. Here’s what makes it special:
- Supported on ReFS and NTFS: Provides flexibility for different storage needs (resiliency vs. performance).
- Works with Mirrored or Parity Spaces: Integrates seamlessly with existing S2D configurations.
- Cluster-Aware Deduplication: Ensures data consistency and redundancy even during failovers.
How Deduplication Works in an S2D Cluster
Here’s a breakdown of the data deduplication process within an S2D cluster:
- Write Request: When a write request arrives at an S2D node, the data is broken down into fixed-size blocks.
- Deduplication Engine: The deduplication engine analyzes each block against a central index, containing previously identified unique blocks.
- Duplicate Detection: If a matching block is found in the index, the S2D cluster only stores a reference to the existing block instead of a full copy. This saves storage space.
- New Block Storage: If the block is unique, the S2D cluster stores it in a dedicated chunk store on one of the cluster nodes.
- Metadata Management: The cluster maintains metadata that tracks the location of original data blocks and references for deduplicated blocks.
Things to Consider:
- Performance Overhead: Deduplication introduces some processing overhead during writes. Evaluate the impact based on your workload.
- Monitoring and Optimization: Regularly monitor deduplication savings and adjust settings for optimal performance.
By understanding data deduplication techniques and how they integrate with S2D clusters, you can unlock significant storage savings and optimize your hyper-converged infrastructure. Remember, choosing the right deduplication approach for your specific workload is key to maximizing efficiency.