IBM Storwize V5010 Manual page 499

Implementing the ibm storwize v5000 gen2 with ibm spectrum virtualize v8.1

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Random Access Compression Engine

The IBM patented RACE implements an inverted approach when compared to traditional

approaches to compression. RACE uses variable-size chunks for the input, and produces

fixed-size chunks for the output.

This method enables an efficient and consistent way to index the compressed data because it

is stored in fixed-size containers (Figure 9-28).

Figure 9-28 Random Access Compression

Location-based compression

Both compression utilities and traditional storage systems compression compress data by

finding repetitions of bytes within the chunk that is being compressed. The compression ratio

of this chunk depends on how many repetitions can be detected within it. The number of

repetitions is affected by how much the bytes stored in the chunk are related to each other.

The relation between bytes is driven by the format of the object. For example, an office

document might contain textual information and an embedded drawing.

Because the chunking of the file is arbitrary, it has no concept of how the data is laid out

within the document. Therefore, a compressed chunk can be a mixture of the textual

information and part of the drawing. This process yields a lower compression ratio because

the different data types mixed together cause a suboptimal dictionary of repetitions. Which

means that fewer repetitions can be detected, because a repetition of bytes in a text object is

unlikely to be found in a drawing.

location-based compression

This traditional approach to data compression is also called

data repetition detection is based on the location of data within the same chunk.

Chapter 9. Advanced features for storage efficiency

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