Tag Archives: replication

Dare2Compare Part 6 : Nutanix data efficiency stats can’t be found

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If you’ve not read Parts 1 through 5, we have already proven several claims by HPE Simplivity regarding Nutanix to be false, as well as explored the misleading way in which HPE SVT promote data efficiency.

We continue with Part 6 where we will discuss HPE’s claim that “Nutanix data efficiency stats are stealthier than a ninja”. (below)

While HPE’s claim is an attempt to create Fear, Uncertainty and Doubt (FUD), HPE are partially correct in that we (Nutanix) have done a very poor job of promoting the arguably market leading data efficiency that Nutanix provides.

In fact, several colleagues and I created a feature request to properly report in a clear and detailed way, the ADSF data efficiencies and I am pleased to say these changes were included as part of the recent AOS 5.1 release.

Now what Nutanix users see in PRISM “Storage” view is (as shown below):

  1. A Capacity optimization overview
  2. Data reduction ratio which is made up of deduplication, compression and erasure coding savings*.
  3. Data reduction savings which is a total GB/TB/PB value from data reduction
  4. An Overall Efficiency ratio which is a combination of Data Reduction, Cloning and Thin Provisioning

*Metadata copies/snapshops/pointers etc are not included in the deduplication value as they are not deduplication.

The resulting summary is very clear and easy to understand so customers can see what efficiencies are from data reduction, and which savings (which typically form by far the largest “efficiency”) come from Cloning and thin provisioning.

DataReductionSummary2

One major item which will be included in an upcoming release is zero suppression. Zero suppression is a capability which has been in Nutanix Distributed Storage Fabric since Day 1 and it avoids unnecessarily storing zeros, instead storing metadata which achieves the same outcome but is much higher performance and uses much less capacity.

Nutanix snapshots or pointer based copies (depending on how you refer to them) are also not included in the overall efficiency number, however these will also be included as a seperate line item in a future release as we aim to be very clear regarding what data efficiencies a customer is achieving with Nutanix.

Some vendors recommend Eager Zero Thick (EZT) VMDKs on vSphere, and then deduplicate the zeros which artificially increases the deduplication ratio. Nutanix does not do this as it’s inefficient to create more data to deduplicate when you can simply avoid writing the data in the first place. However we do plan to report the savings from Zero suppression as a seperate line item as it is a value our platform provides.

For a more detailed view, Nutanix customers can dive down into the storage,Diagram view where admins can view of each containers data efficiency breakdown (as shown below).

DetailedContainerView

As we can see, Nutanix is very transparent showing what data reduction features are enabled, what ratio is being achieved, the total, used, reserved and even Thick Provisioned storage with an effective free based on physical multiplied by data reduction ratio and an overall efficiency value.

Now that we’ve covered off how Nutanix measures and reports on data reduction/efficiency, I’d like to highlight a critical factor when discussing data reduction/efficiency and that is that data efficiency is totally dependant on the individual customers data. For the same dataset, the difference between vendors with the same capabilities, e.g.: Deduplication, Compression and Erasure Coding (EC-X) are unlikely to be vastly different (or better put, change a business outcome one way or another) despite what each vendor will say about their implementation of such technologies.

In short: The biggest factor in the achieved data reduction is not the vendor, it’s the customer data.

With that said, if you’re comparing HPE SVT and Nutanix, then there is a pretty major delta between the two products in terms of capabilities and that is because Nutanix supports Erasure Coding (EC-X) and HPE SVT does not.

As a result, Nutanix has a major advantage as Erasure Coding in the Nutanix Acropolis Distributed Storage Fabric (ADSF) is complimentory to both deduplication and compression.

Unlike Compression and Deduplication, Erasure Coding can provide savings (or another way to look at it would be lower data redundancy overheads) regardless of the data type.

So where Deduplication and Compression will get minimal/no savings for data such as Video files, Erasure Coding still provides savings so the delta between Nutanix and HPE SVT will only increase in Nutanix favour the less the customer data will dedupe and/or compress.

HPE SVT on the other hand has a RAID (RAID 6 being N-2 usable or RAID 60 being N-4 usable) overhead and on top of that, use replication (2 copies / 50% usable) for an usable capacity (of raw) of well below 50% depending on the number of drives per node.

Nutanix, using RF2 and EC-X provides between 50% (minimum) and 80% (maximum) usable capacity of RAW and with RF3 (N+2) between 33% (minimum) and 66% (maximum) usable excluding the benefits of compression and deduplication.

The next major factor in data efficiency ratios is how they are measured!

In Part 1 I have already covered how misleading HPE SVT’s 10:1 efficiency guarantee is, and this is a great example of why it can be difficult to compare apples/apples between vendors. Nutanix on the other hand does not measure data efficiency in the same misleading manner.

In Summary:

  1. Nutanix AOS 5.1 has comprehensive data reduction/efficiency reporting within the PRISM HTML GUI
  2. Nutanix data reduction capabilities exceed that of HPE SVT as both products have Dedupe and Compression, but Erasure Coding (EC-X) is only supported on Nutanix
  3. All data reduction capabilities on Nutanix are complimentory, so Dedupe , Compression and Erasure Coding can all work together to maximise savings.
  4. Erasure Coding provides data reduction even for data which is not compressible or dedupeable
  5. Nutanix data efficiency stats are easily visible in the PRISM GUI and are much more detailed than HPE SVT

Return to the Dare2Compare Index:

But wait, there’s more!

As far as data reduction results are concerned, they are all over twitter and a simple search comes up with many examples. The first one being my favorite. Not because of the data reduction ratio itself but because it shows one of the major values of a 100% software solution where a simple software upgrade (which is one-click rolling, non-disruptive) provided the customer a significantly higher data reduction ratio. So basically, the customer got more capacity for free!

Note: None of the below show the latest data efficiency reporting capabilities from AOS 5.1.

Here are a few other examples which I found using this Twitter search:

Nutanix Data Protection Capabilities

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There is a lot of misinformation being spread in the HCI space about Nutanix data protection capabilities. One such example (below) was published recently on InfoStore.

Evaluating Data Protection for Hyperconverged Infrastructure

When I see articles like this, It really makes me wonder about the accuracy of content on these type of website as it seems articles are published without so much as a brief fact check from InfoStore.

None the less, I am writing this post to confirm what Data Protection Capabilities Nutanix provides.

  • Native In-Built Data protection

Prior to my joining Nutanix in mid-2013, Nutanix already provided a Hypervisor agnostic Integrated backup and disaster recovery solution with centralised consumer- grade management through our PRISM GUI which is HTML 5 based.

The built in capabilties are flexible and VM-centric policies to protect virtualized applications with different RPOs and RTOs with or without application consistency.

The solution also supports Local, remote, and cloud-based backups, and synchronous and asynchronous replication-based disaster recovery solutions.

Currently supported cloud targets include AWS and Azure as shown below.

CloudBackup

The below video which shows in real time how to create Application consistent snapshots from the Nutanix PRISM GUI.

Nutanix can also perform One to One, One to Many and Many to One replication of application consistent snapshots to onsite or offsite Nutanix clusters as well as Cloud providers (AWS/Azure), ensuring choice and flexibility for customers.

Nutanix native data protection can also replicate between and recover VMs to clusters of different hypervisors.

  • CommVault Intellisnap Integration

Nutanix also provides integration with Commvault Intellisnap which allows existing Commvault customers to continue leveraging their investment in the market leading data protection product and to take advantage of other features where required.

The below shows how agentless backups of Virtual Machines is supported with Acropolis Hypervisor (AHV). Note: Commvault is also fully supported with Hyper-V and ESXi.

By Commvault directly calling the Nutanix Distributed Storage Fabric (NDSF) it ensures snapshots are taken quickly and efficiently without the dependancy on a hypervisor.

  • Hypervisor specific support such as VMware API Data Protection (VADP)

Nutanix also supports solutions which leverage VADP, allowing customers with existing investment in products such as Veeam & Netbackup to continue with their existing strategy until such time as they want to migrate to Nutanix native data protection or solutions such as Commvault.

  • In-Guest Agents

Nutanix supports the use of In-Guest agents which are typically very inefficient with centralised SAN/NAS storage but due to data locality and NDSF being a truly distributed platform, In-Guest Incremental forever backups perform extremely well on Nutanix as the traditional choke points such as Network, Storage Controllers & RAID packs have been eliminated.

Summary:

As one size does not fit all in the world of I.T, Nutanix provides customers choice to meet a wide range of market segments and requirements with strong native data protection capabilities as well as 3rd party integration.

PART 1 – Problems with RAID and Object Based Storage for data protection

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I regularly get asked to compare the resiliency of traditional centralized storage with converged as well as newer technologies such as hyper-converged.

So this post will discuss the problems with RAID and newer hyper-converged solutions using Object based storage for data protection.

This post will discuss two examples below, with Part 2 discussing Hyper-converged solutions using Distributed File Systems.

1. Traditional RAID

2. Hyper-converged Object Based Storage

Starting with Traditional shared storage, and the most common RAID level in my experience, RAID 5.

The below diagram shows a 3 x 4TB SATA drives in a RAID 5 with a Hot Spare.
3 Disk R5 w Hot Spare NO BG

Now lets look a drive failure scenario. We now have the Hot Spare activate and start rebuilding as shown below.

3 Disk R5 w Hot Spare REBUILDING NO BG

So this all sounds fine, we’ve had a drive failure, and a spare drive has automatically taken its place and started rebuilding the data.

The problem now is that even in this simplified/small example we have 2 drives (or say 200 IOPS of drives) trying to rebuild onto just a single drive. So the maximum rate at which the RAID 5 can restore resiliency is limited to that of a single drive or 100 IOPS.

If this was a 8 disk RAID 5, we would have 7 drives (or 700 IOPS) trying to rebuild again to only a single drive or 100 IOPS.

There are multiple issues with this architecture.

  1. The restoration of resiliency of the entire RAID is constrained by the destination drive, in this case a SATA drive which can sustain less than 100 IOPS
  2. A single subsequent HDD failure within the RAID will cause data loss.
  3. The RAID rebuild is a high impact activity on the storage controllers which can impact all storage
  4. The RAID rebuild is an especially high impact activity on the virtual machines running on the RAID.
  5. The larger the RAID or the capacity drives in the RAID, the longer the rebuild takes and the higher the performance impact and chance of subsequent failures leading to data loss.

Now I’m sure most of you understand this concept, and have felt the pain of a RAID rebuild taking many hours or even days, but with new hyper converged technology this issue is no longer a problem, right?

Wrong!

It entirely depends on how data is recovered in the event of a drive failure. Lets look at an example of an hyper-converged solution using an object store.The below shows a simplified example of a Hyper-converged Object Based Storage with 4 objects represented by Object A,B,C and D in Black, and the 2nd replicated copy of the object represented Object A,B,C and D in Purple.

Note: Each object in the Object Store can be hundreds of GB in size.HyperconvergedObjectStoreNormal

Let’s take a look what happens in a disk failure scenario.

HyperconvergedObjectStoreFailure

From the above diagram we can see a drive has failed on Node 1, which means Object A and Object D’s replica have been lost. The object store will then replicate a copy of Object A to Node 4, and a replica of Object D to Node 2 to restore resiliency.

There are multiple issues with this architecture.

  1. Object based storage can lack granularity as Objects can be 200Gb+.
  2. The restoration of resiliency of any single object is constrained by the source drive or node.
  3. The restoration of resiliency of any single object is also constrained by the destination drive or node.
  4. The restoration of multiple objects (such as Object A & D in the above example) is constrained by the same drive or node which will result in contention and slow the process of restoring resiliency to both objects.
  5. The impact of the recovery is High on virtual machines running on the source and destination nodes.
  6. The recovery of an Object is constrained by the source and destination node per object.
  7. Object stores generally require a witness, which is stored on another node in the cluster. (Not illustrated above)

It should be pointed out, where SSDs are used for a write cache, this can help reduce the impact and speed up recovery in some cases, but where data needs to be recovered from outside of cache, i.e.: A SAS or SATA drive, the fact writes go to SSD makes no difference as the writes are constrained by the read performance.

Summary:

Traditional RAID used by SAN/NAS and newer Hyper-converged Object based storage both suffer similar issue when recovering from drive or node failures which include:

  1. The restoration of resiliency is constrained by the source drive or node
  2. The restoration of resiliency is constrained by the destination drive or node
  3. The restoration is high impact on the desination
  4. The recovery of one object is constrained by the network connectivity between just two nodes.
  5. The impact of the recovery is High on any data (such as virtual machines) running on the RAID or source/destination node/s
  6. The recovery of RAID or an Object is constrained by a single part of the infrastructure being a RAID controller / drive or a single node.

In Part 2, we will look at the Hyper-converged Distributed File Systems.