We’ve all heard the marketing claims from some storage vendors about how efficient their storage products are. Data efficiency ratios of 40:1 , 60:1 even 100:1 continue to be thrown around as if they are amazing, somehow unique or achieved as a result of proprietary hardware.
Let’s talk about how vendors may try to justify these crazy ratios:
- Calling Snapshots or Metadata based (pointer) copies, “Full copies” (or even worse “backups” which I discussed in Copies of data on the same Primary Storage is not a backup solution.)
For many years, Storage vendors have been able to take space efficient copies of LUNs, Datastores, Virtual Machines etc which rely on snapshots or metadata. These are not full copies and reporting this as data efficiency is quite mis-leading in my opinion as this is and has been for many years Table stakes.
Be wary of vendors encouraging (or requiring) you configure more frequent “backups” (which are after all just Snapshots or metadata copies) to achieve the advertised data efficiencies.
- Reporting VAAI/VCAI clones as full copies
If I have a VMware Horizon View environment, It makes sense to use VAAI/VCAI space efficient clones as they provide numerous benefits including faster provisioning, recompose and use less space which leads to them being served from cache (making performance better).
So if I have an environment with just 100 desktops deployed via VCAI, You have a 100:1 data reduction ratio, 1000 desktops and you have 1000:1. But this is again Table stakes… well sort of because some vendors don’t support VAAI/VCAI and others only have partial support as I discuss in Not all VAAI-NAS storage solutions are created equal.
Funnily enough, one vendor even offloads what VAAI/VCAI can do (with almost no overhead I might add) to proprietary hardware. Either way, while VAAI/VCAI clones are fantastic and can add lots of value, claiming high data efficiency ratios as a result is again mis-leading especially if done so in the context of being a unique capability.
- Compression of Highly compressible data
Some data, such as Logs or text files are highly compressible, so ratios of >10:1 for this type of data are not uncommon or unrealistic. However consider than if logs only use a few GB of storage, then 10:1 isn’t really saving you that much space (or money).
For example a 100:1 data reduction ratio of 100MB of logs is only saving you ~10GB which is good, but not exactly something to make a purchasing decision on.
Also compression of databases which lots of white space also compress very well, so the larger the Initial size of the DB, the more it will compress.
The compression technology used by storage vendors is not vastly different, which means for the same data, they will all achieve a similar reduction ratio. As much as I’d love to tell you Nutanix has much better ratios than Vendors X,Y and Z, its just not true, so I’m not going to lie to you and say otherwise.
- Deduplication of Data which is deliberately duplicated
An example of this would be MS Exchange Database Availability Groups (DAGs). Exchange creates multiple copies of data across multiple physical or virtual servers to provide application and storage level availability.
Deduplication of this is not difficult, and can be achieved (if indeed you want to dedupe it) by any number of vendors.
In a distributed environment such as HCI, you wouldn’t want to deduplicate this data as it would force VMs across the cluster to remotely access more data over the network which is not what HCI is all about.
In a centralised SAN/NAS solution, deduplication makes more sense than for HCI, but still, when an application is creating the duplicate data deliberately, it may be a good idea to exclude it from being deduplicated.
As with compression, for the same data, most vendors will achieve a similar ratio so again this is table stakes no matter how each vendor tries to differentiate. Some vendors dedupe at more granular levels than others, but this provides diminishing returns and increased overheads, so more granular isn’t always going to deliver a better business outcome.
- Claiming Thin Provisioning as data efficiency
If you have a Thin Provisioned 1TB virtual disk and you only write 50GB to the disk, you would have a data efficiency ratio of 20:1. So the larger you create your virtual disk and the less data you write to it, the better the ratio will be. Pretty silly in my opinion as Thin Provisioning is nothing new and this is just another deceptive way to artificially improve data efficiency ratios.
- Claiming removal of zeros as data reduction
For example, if you create an Eager Zero Thick VMDK, then use only a fraction, as with the Thin Provisioning example (above), removal of zeros will obviously give a really high data reduction ratio.
However Intelegent storage doesn’t need Eager Zero Thick (EZT) VMDKs to give optimal performance nor will they write zeros to begin with. Intelligent storage will simply store metadata instead of a ton of worthless zeros. So a data reduction ratio from a more intelligent storage solution would be much lower than a vendor who has less intelligence and has to remove zeros. This is yet another reason why data efficiency (marketing) numbers have minimal value.
Two of the limited use cases for EZT VMDKs is Fault Tolerance (who uses that anyway) and Oracle RAC, so removal of zeros with intelligent storage is essentially moot.
Summary:
Data reduction technologies have value, but they have been around for a number of years so if you compare two modern storage products, you are unlikely to see any significant difference between vendor A and B (or C,D,E,F and G).
The major advantage of data reduction is apparent when comparing new products with 5+ year old technology. If you are in this situation where you have very old tech, most newer products will give you a vast improvement, it’s not unique to just one vendor.
At the end of the day, there are numerous factors which influence what data efficiency ratio can be achieved by a storage product. When comparing between vendors, if done in a fair manner, the differences are unlikely to be significant enough to sway a purchasing decision as most modern storage platforms have more than adequate data reduction capabilities.
Beware: Dishonest and mis-leading marketing about data reduction is common so don’t get caught up in a long winded conversations about data efficiency or be tricked into thinking one vendor is amazing and unique in this area, it just isn’t the case.
Data reduction is table stakes and really shouldn’t be the focus of a storage or HCI purchasing decision.
My recommendation is focus on areas which deliver operational simplicity, removes complexity/dependancies within the datacenter and achieve real business outcomes.
Related Posts:
1. Sizing infrastructure based on vendor Data Reduction assumptions – Part 1
2. Sizing infrastructure based on vendor Data Reduction assumptions – Part 2
3.Deduplication ratios – What should be included in the reported ratio?