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Data Reduction is a feature intended to offer long-term space savings. Data Reduction uses machine learning resulting in efficient use of the available system resources. The use of statistics collected from the incoming data determines what is active and what is idle. Therefore, the activity-based compression function does not apply to net new writes. They are compressed and allocated to a compression pool. This also applies to dedupe as net new writes may not be consuming drive capacity yet. This would be the first entry of a hash ID into the hash table. Continued access to data generates statistics that are used to differentiate the activity level of data.
For example, assume a data reduction reservation of 3:1. When the achieved data reduction ratio is less than 3:1 the physical used percent will be greater than the effective used percent. Likewise, when the data reduction ratio is greater than 3:1 the effective used percent will be greater than the physical used percent. Using the same example of 3:1 as the system reservation, applied to 100TB of usable disk capacity. This indicates the system will manage the resources to achieve 300TB of host data consuming 100TB of usable capacity. When the achieved ratio is less than 3:1 the system is less likely to accommodate 300TB of host data on 100TB of usable capacity. When the achieved ratio is greater than the system reservation the system will accommodate 300TB of host data on 100TB of disk capacity.