Nutanix X-Ray Benchmarking tool – Snapshot Impact Scenario

In the first part of this series, I introduced Nutanix X-Ray benchmarking tool which has been designed very differently to traditional benchmarking tools as the performance of the app is the control and the variable is the platform,not the other way around.

This is done by generating realistic IO patterns (e.g.: Not 100% 4k read) and then performing functions against the platform to see how the control (the VM application performance) is impacted by the underlying platforms functionality.

A great example of this is performing snapshots as the first step in a space efficient backup solution.

X-Ray has a built in test which generates an OLTP workload which is ran for 8 hours which for an all flash platform generates 6000 IOPS across the database and 400 IOPS for the logs. The scenario is detailed in the X-Ray report shown below.


The Snapshot impact scenario is then ran against multiple platforms and using the Analysis functionality within X-ray. we can generate a report which overlays the results from multiple platforms.

The below example is GA Acropolis Hypervisor (AHV) on AOS 5.1.1 verses a leading hypervisor and SDS platform showing the snapshot impact scenario.


Each of the red lines indicate a snapshot and what we observe is the performance of both platforms remains consistent until the 10th snapshot (shown below) where the Nutanix platform continues without impact and the leading hypervisor and SDS platform starts degrading significantly.


In the real world, customers use the intelligent features of storage, SDS or hyper-converged platforms but rarely test how this functionality works prior to purchasing. This is because it’s difficult and time consuming to do so.

Nutanix X-Ray tool makes the process of validating a platforms performance under real world scenarios a quick and easy process and provides automatically generated reports where accurate comparisons can be made.

What this example shows is that while both platforms could achieve the required performance without snapshots, only Nutanix AHV & AOS could maintain the performance while utilising snapshots to achieve the type of recovery point objective (RPO) that is expected in production environments, especially with business critical workloads.

As part of the Nutanix Solutions and Performance engineering organisation, I can tell you that the focus for Nutanix is real world performance, using data reduction, leveraging snapshots, mixing workloads and testing a large scale.

In upcoming posts I will show more examples of X-Ray test scenarios as well as comparisons between GA Acropolis Hypervisor (AHV) & AOS 5.1.1 verses a leading hypervisor and SDS platform.

Related Articles:

Nutanix X-Ray Benchmarking tool Part 1 – Introduction

Nutanix X-Ray Benchmarking tool Part 3 – Extended Node Failure Scenario

It’s 2017, let’s review Thick vs Thin Provisioning

For a long time, it has been widely considered that thick provisioning is required to achieve maximum storage performance and for many years this was a good rule of thumb.

Before we get into details, what are Thick and Thin provisioning?

Thick provisioning is where storage allocated to a LUN, NFS mount or Virtual Disk (such as a VMDK in ESXi, VHDX in Hyper-V or vDisk in AHV) is zeroed out and/or fully reserved regardless of how much capacity is actually used.

Thick provisioning avoids a storage subsystem from having to zero out a block before writing new data which is one of the reasons higher performance could be achieved on many storage platforms.

Thin provisioning on the other hand is where storage allocated to a LUN or Virtual Disk is zeroed as data is written and allows physical capacity to be overcommitted.

The advantages of Thick provisioning included easier capacity management, or simply put a “What you see is what you get” as well as maximum performance on most platforms. But by maximum performance, even on older storage platforms the advantage was rarely significant as people would claim.

VMware conducted a Performance Study of VMware vStorage Thin Provisioning back in the ESXi 4.0 days (~2009) which I will briefly summarise.

On page 6 of the performance study the following graph shows the different in performance between Thin and Thick VMDKs during zeroing and post-zeroing.

As you can see the performance is almost identical.

The disadvantages though were and remain significant to this day which include an inability to overcommit storage, meaning physical free space has to be maintained at multiple layers such as RAID group, LUN, Virtual Disk layers, leading to inefficiency.

The advantages of Thin provisioning include the ability to overcommit storage which results in more flexibility when sizing LUNs & Virtual Disks and less wasted space. The only real downsides were potentially increased capacity management complexity and lower performance.

I have previously written two example architectural decisions regarding using “Thin on Thin“, meaning thin provisioned virtual disks on a thin provisioned LUN or NFS mount as well as “Thin on Thick” meaning thin provisioned virtual disks on a thick provisioned LUN or NFS mount. These two examples cover off many of the traditional pros and cons between thick and think, so I won’t repeat myself here.

I never wrote an example design decision for Thick on Thick, but this was common practice when provisioning storage was time consuming, difficult and involved lengthly delays to engage subject matter experts.

In early 2015, I wrote a two part blog series where I explained it’s not as simple as you might think to calculate usable capacity where I compared SAN/NAS verses Nutanix. In part 1, I highlight that the LUN Provisioning Type is one area which can greatly impact the usable capacity of a traditional storage platform.

But fast forward into the era of hyper-converged platforms like Nutanix and some modern storage arrays and the major downsides of thin provisioning, being complexity of capacity management and reduced performance have not only been reduced, but at least in the case of Nutanix, have been eliminated all together.

Let’s address Capacity management w/ Nutanix:

Storage utilisation only needs to be monitored in ONE place, the storage summary which lives on the home screen of the Nutanix HTML 5 UI.


No matter how many nodes in your cluster, number of containers (which translate to datastores in a VMware environment), virtual machines & virtual disks or physical servers connecting via ABS, this is the only place you need to monitor capacity.

There are no RAID groups, Disk Groups, Aggregates, LUNs etc where capacity needs to be managed. All nodes in a cluster contributed to the capacity of the cluster and even when one or more virtual machines use more capacity than a the node they run on, Nutanix Acropolis Distributed Storage Fabric (ADSF) takes care of it.

So issue #1, Capacity management, is solved. Now it’s onto the issue of performance.

Thin Provisioning Performance w/ Nutanix:

When running ESXi, Nutanix runs NFS datastores and supports thick provisioning via the VAAI-NAS Space reservation primitive as discussed in this post. This allows the creation of thick provisioned (Eager Zero or Lazy Zero Thick) VMDKs when traditionally NFS datastores did not support it.

However this was only required for Oracle RAC and VMware Fault Tolerance and was not a performance requirement.

However from a performance perspective, Thin provisioning actually outperforms thick on intelligent storage such as Nutanix. In the specific case of Nutanix, random write I/O is serviced by the fastest tier available (e.g.: SSD) and via the operations log (OPLOG) which takes the random writes commits them to persistent media, and then coalesces them into sequential IO to then commit to SSD before tiering it off to lower cost storage in the case of hybrid nodes.

This means the write penalty for overwriting or zeroing blocks before writing new I/O is eliminated.

In fact if you configure thick provisioned virtual disks, as the zeros (or whitespace) is being written by the hypervisor, the Nutanix storage fabric acknowledges every I/O and discards the zeros in favour of storing metadata and simply reserving the capacity. In simple terms, this just means Nutanix has to acknowledge a whole bunch of nothing and the thick provisioning is achieve with a simple reservation as opposed to zeroing out many GBs or TBs of storage.

This means thick provisioning is actually lower performance than thin provisioning on Nutanix.

With modern, intelligent storage, there is limited if any benefits to using thick provisioning, the only example I can think of is to artificially inflate the deduplication ratio as thick provisioned virtual disks tend to have a lot of zeros all of which dedupe. I wrote an article titled: “Deduplication ratios – What should be included in the reported ratio?” which covers off this point in detail but in short, don’t create unnessasary data (in this case, zeros) just to inflate your dedupe ratio, it just wastes storage controller resources and achieves no additional benefits.

The following is a comprehensive list of the real world advantages of using thick provisioning on Nutanix.

This space is intentionally left blank


For the best efficiency and performance when deploying virtual machines or storage for physical servers via ABS on Nutanix, use thin provisioning!

What’s .NEXT 2017 – AHV Turbo Mode

Back in 2015 I wrote a series titled “Why Nutanix Acropolis Hypervisor (AHV) is the next generation hypervisor” which covered off many reasons why AHV was and would become a force to be reckoned with.

In short, AHV is the only purpose built hypervisor for hyper-converged infrastructure (HCI) and it has continued to evolve in terms of functionality and maturity while becoming a popular choice for customers.

How popular you ask? Nutanix officially reported 23% adoption as a percentage of nodes sold in our recent third quarter fiscal year 2017 financial highlights.

Over the last couple of years I have personally worked with numerous customers who have adopted AHV especially when it comes to business critical applications such as MS SQL, MS Exchange.

One such example is Shinsegae who is a major retailer running 50,000 MS Exchange mailboxes on Nutanix using AHV as the hypervisor. Shinsegae also runs MS SQL workloads on the same platform which has now become the standard platform for all workloads.

This is just one example of AHV proven in the field and at scale to have the functionality, resiliency and performance to support business critical workloads.

But at Nutanix we’re always striving to deliver more value to our customers, and one area where there is a lot of confusion and misinformation is around the efficiency of the storage I/O path for Nutanix.

The Nutanix Controller VM (CVM) runs on top of multiple hypervisors and delivers excellent performance, but there is always room for improvement. With our extensive experience with in-kernel and virtual machine based storage solutions, we quickly learned that the biggest bottleneck is the hypervisor itself.


With technology such as NVMe becoming mainstream and 3D XPoint not far behind, we looked for a way to give customers the best value from these premium storage technologies.

That’s where AHV Turbo mode comes into play.


AHV Turbo mode is a highly optimised I/O path (shortened and widened) between the User VM (UVM) and Nutanix stargate (I/O engine).

These optimisation have been achieved by moving the I/O path in-kernel.












Just kidding! In-kernel being better for performance is just a myth, Nutanix has achieved major performance improvements by doing the heavy lifting of the I/O data path in User Space, which is the opposite of the much hyped “In-kernel”.

The below diagram show the UVM’s I/O path now goes via Frodo (a.k.a Turbo Mode) which runs in User Space (not In-kernel) and onto stargate within the Controller VM).


Another benefit of AHV and Turbo mode is that it eliminates the requirement for administrators to configure multiple PVSCSI adapters and spread virtual disks across those controllers. When adding virtual disks to an AHV virtual machine, disks automatically benefit from Nutanix SCSI and block multi-queue ensuring enhanced I/O performance for both reads and writes.

The multi-queue I/O flow is handled by multiple frodo threads (Turbo mode) threads and passed onto stargate.


As the above diagram shows, Nutanix with Turbo mode eliminates the bottlenecks associated with legacy hypervisors, one such example is VMFS datastores which required VAAI Atomic Test and Set (ATS) to minimise the impact of locking when the numbers of VMs per datastore increased (e.g. >25). With AHV and Turbo mode, every vdisk has always had it’s own queue (not one per datastore or container) but frodo enhances this by adding a per-vcpu queue at the virtual controller level.

How much performance improvement you ask? Well I ran a quick test which showed amazing performance improvements even on a more than four year old IVB NX3450 which only has 2 x SATA SSDs per node and with the memory read cache disabled (i.e.: No reads from RAM).

A quick summary of the findings were:

  1. 25% lower CPU usage for the similar sequential write performance (2929MBps vs 2964MBps)
  2. 27.5% higher sequential read performance (9512MBps vs 7207MBps)
  3. A 62.52% increase in random read IOPS (510121 vs 261265)
  4. A 33.75% increase in random write IOPS (336326 vs 239193)

So with Turbo Mode, Nutanix is using less CPU and RAM to drive higher IOPS & throughput and doing so in user space.

Intel published “Code Sample: Hello World with Storage Performance Development Kit and NVMe Driver” which states “When comparing the SPDK userspace NVMe driver to an approach using the Linux Kernel, the overhead latency is up to 10x lower”.

This is just one of many examples which shows userspace is clearly not the bottleneck that some people/vendors have tried to claim with the “in-kernel” is faster nonsense I have previously written about.

With Turbo mode, AHV is the highest performance (throughput / IOPS) and lowest latency hypervisor supported by Nutanix!

But wait there’s more! Not only is AHV now the highest performing hypervisor, it’s also used by our largest customer who has more than 1750 nodes running 100% AHV!