Regardless of the block device type being scanned, it is also important to understand a few principles of common file system space management. When files are deleted from a file system, the space they occupied prior to the deletion becomes free and available to the file system. This happens even though the data on disk was not actually removed, but rather the file system index and pointers were updated to reflect this change. 

When using Data Reduction Estimator to analyze a block device used by a file system, all underlying data in the device is analyzed, regardless of whether this data belongs to files that were already deleted from the file system. 

For example, you can fill a 100 GB file system and make it 100% used, then delete all the files in the file system making it 0% used. When scanning the block device used for storing the file system in this example, Data Reduction Estimator (or any other utility for that matter) will access the data that belongs to the files that are already deleted. 

In order to reduce the impact of block device and file system behavior mentioned above, it is recommended to use Data Reduction Estimator to analyze volumes that contain as much active data as possible rather than volumes that are mostly empty of data. This increases accuracy level and reduces the risk of analyzing old data that is already deleted, but might still have traces on the device. 

The Tool can be downloaded from FixCentral. 

Example 4-1 shows the CLI usage of the Data Reduction Estimator Tool. 

Data-Reduction-Estimator –d <device> [-x Max MBps] [-o result data filename] [-s Update interval] [--command scan|merge|load|partialscan] [--mergefiles Files to merge] [--loglevel Log Level] [--batchfile batch file to process] [-h] 

GLYPH<SM590000> Data Reduction Estimator can be used on the following client operating systems: 

– Windows 2008 Server, Windows 2012 

– Red Hat Enterprise Linux Version 5.x, 6.x, 7.x (64-bit) 

– UBUNTU 12.04 

– ESX 5.0, 5.5, 6.0 

– AIX 6.1, 7.1 

– Solaris 10 

– HP-UX 11.31 

## Is your data a deduplication candidate? 

Deduplication is done by using hash tables in order to identify previously written copies of data. If duplicate data is found, instead of writing the data to disk, the algorithm will reference to the previously found data. 

Data Reduction Pools are designed to support reclamation, compression and deduplication from the start. Deduplication uses 8KB deduplication chunks and an SHA-1 hashing algorithm and 256KB streaming capability. Data Reduction Pools provide deduplicate then compress capability. The scope of deduplication is within a DRP within an I/O Group 

## General assumptions 

Some environments have data with high deduplication savings and are therefore candidates for deduplication. 

Good deduplication savings can be achieved in environments such as virtual desktop and some virtual machine environments. So they might be good candidates for deduplication. 

