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With storage efficiency, DRP thin and compressed volumes provide the best performance numbers. This is due to the new compression implementation, because this provides better load balancing and consistent performance. 

Figure 3-15 shows the types of volumes in the DRP pools. 

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There are four main characteristics that make up the IBM Data Reduction Pool design: 

GLYPH<SM590000> Fine-grained allocation of data blocks 

GLYPH<SM590000> The ability to free back unused (unmapped or overwritten) capacity at a fine grain 

GLYPH<SM590000> Give consistent, predictable performance 

GLYPH<SM590000> Optimize performance for solid state storage, such as Flash 

A data reduction pool, at its core, uses an LSA to allocate capacity. Therefore, the volume that you create from the pool to present to a host application consists of a directory that stores the allocation of blocks within the capacity of the pool. 

All writes for data reduction pools take place at the upper cache layer to the host. Reads have to go through the lower cache layer. The heart of the new DRP functionality is in the new implementation of the Log Structured Array. This includes lower cache, virtualization, Easy Tier, and RAID. LSA understands what works best for each of these components. 

A log structured array allows a “tree-like” directory to be used to define the physical placement of data blocks independent of size and logical location. Each logical block device has a range of logical block addresses (LBAs). Starting from 0 and ending with the block address that fills the capacity. When written, an LSA allows you to allocate data sequentially and provide a directory that provides a lookup to match the logical block address with the physical address within the array. 

Note:  LSA always appends new data to the end of the array. When data is overwritten, the old location and capacity utilized needs to be marked as free . UNMAP functions can also request that you free  no longer needed capacity. Compression overwrites can result in a different capacity being used. Deduplication might find new duplicates when data is rewritten. 

