Deduplication – a fancy form of Compression?

One of the things that peeved at the HP D2D Workshop a few days ago was this heading in the HP PowerPoint slides – “Deduplication – a fancy form of Compression”. Somehow it bothered me.

I have always placed both deduplication and compression into a bucket I called “Data Reduction“. Some vendors might call it Storage Economics, spinning it in a cooler manner. Either way, both attempt and succeed to reduce the capacity required to store the amount of data and this translates into benefits in storage management and network. With a smaller data set, lesser processing and capacity are required, likely speeding up the performance of the storage array. At the same time, the primary data backup set (you know, the data that you back up every night?) becomes smaller, making backup and restore faster (not necessarily, but you have to rehydrate the data from its reduced state). Another obvious benefit is the ability to transfer the smaller data set over the network more efficiently, compared to its original state and size, making Disaster Recovery more possible and so on.

I have always known that deduplication works with data objects using a differential method. Whether the data object is a file or a chunk of the file, deduplication attempts to differentiate similarities (duplicates), and store one copy of that object and have others referencing to the single object. The differentiation methods commonly used are hashing and delta differential. In hashing, MD-5 and SHA-1 are the popular hashing algorithms used, while in delta differentials, the data objects are compared (usually in a scrutinizing manner) to find the differences. The duplicates or similarities are discarded.

There are many factors involved in deduplication. It could be the types of data, the processing power required to do the deduplication task, and throughput of processing and so on and resulting in the different deduplication ratio and time required to complete the process. I am not going to delve into that as there are many vendors who will be able to articulate this, such as EMC Data Domain, HP D2D/VLS with its StoreOnce technology, Exagrid, Sepaton, Dell Ocarina Networks, NetApp, EMC Centera, CommVault Simpana, Symantec PureDisk, Symantec NetBackup, EMC Avamar and many more.

Meanwhile, compression (especially most commercial compression technology) are based on dictionary coding, a lossless data reduction algorithm. Note that I am using the term encoding rather than compression because factually, encoding is the right word. You can’t squeeze the data into a smaller size like you do with a real life object.

The technique works like this.

  1. When being encoded, a bit/byte or a set of bytes are compared to a “dictionary” which is a pool of  “words”  in a data structure maintained by the encoding technology
  2. If a match is found, the bit/byte or set of bytes is substituted by an “word”, usually a much shorter (hence smaller size) representation form of the bytes being encoded.
  3. As the encoding process continues, more “dictionary words” are built into the “dictionary” based on the bytes already encoded. This is popularly known as the sliding window implementation.
  4. The end result is the data is highly encoded (heavily replaced) by “dictionary words”  and of a much smaller size.

One of the heavily implemented compression technique is based on the theory and methodology introduced by Lempel-Ziv and further enhanced by the Lempel-Ziv-Welch trio. A very good explanation of LZ method can be found here.

Both deduplication and compression have the same objective – that is to reduce the data size for more efficient storage. But both approach it from a different angle but they are by no means, exclusive. Both can be used to complement each other and further reduce the capacity required to store the data.

Deduplication usually works with larger data objects (chunks, files etc) while compression works harder at the lower level (byte range level). Deduplication is heavily deployed in secondary data sets (or backup) because you can find plenty of duplicates while in primary data sets (the data in production), deduplication and compression are deployed, either in a singular fashion or one after another. Deduplication is usually run as Step 1 and then Compression is run in Step 2.

So far, the only one that has impressed me for the primary data reduction is Ocarina Networks, which uses a 3 step approach in dedupe, compress and using specialized compactors to reduce the data even more. I have seen the ability of Ocarina reducing Schlumberger Geoframe and Petrel seismic data to more than 50%. That was impressive!

Having my bothered state satisfied, I guess having the say of “Deduplication – a fancy form of Compression” is someone else’s cup of tea. I would rather say “Deduplication – a fancy form of Data Reduction Technology” but I am not complaining as much I did before.

About cfheoh

I am a technology blogger with 20+ years of IT experience. I write heavily on technologies related to storage networking and data management because that is my area of interest and expertise. I introduce technologies with the objectives to get readers to *know the facts*, and use that knowledge to cut through the marketing hypes, FUD (fear, uncertainty and doubt) and other fancy stuff. Only then, there will be progress. I am involved in SNIA (Storage Networking Industry Association) and as of October 2013, I have been appointed as SNIA South Asia & SNIA Malaysia non-voting representation to SNIA Technical Council. I was previously the Chairman of SNIA Malaysia until Dec 2012. As of August 2015, I am returning to NetApp to be the Country Manager of Malaysia & Brunei. Given my present position, I am not obligated to write about my employer and its technology, but I am indeed subjected to Social Media Guidelines of the company. Therefore, I would like to make a disclaimer that what I write is my personal opinion, and mine alone. Therefore, I am responsible for what I say and write and this statement indemnify my employer from any damages.
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