What is the performance impact & overheads of Inline Compression on Nutanix?

I’m frequently getting asked about Nutanix data reduction capabilities such as Deduplication, Erasure Coding and Compression and one of the most common questions (especially in a competitive situation) is:

“What is the performance impact and the overhead of Inline Compression on Nutanix?”

The short answer is, the pros outweigh the cons and this has been true for as long as I can remember with the Nutanix platform.

I have been testing of various applications, node types, cluster sizes and configurations and thought I would share some data on the overheads and performance impact of in-line compression which is what Nutanix (and I) recommend for most deployments including for business critical applications such as Oracle, MS SQL and MS Exchange.

In this case I was testing storage performance for MS Exchange using Jetstress.

Now without going into the exact configuration of the environment (to avoid competitors FUD), the test was simple. I created a Windows 2012 VM and configured Jetstress. I then performed 3 x 15min runs each of which completed a database checksum at the completion.

Following the 3 runs, I enabled In-line compression and repeated the same 3 tests.

The below chart is a screenshot from the Nutanix PRISM HTML 5 UI showing the Cluster wide IOPS, latency and throughput along with the Controller VM CPU utilisation.

PerformanceSummary

As we can see, the 6 performance runs are very similar across all metrics including the CVM CPU utilisation. The below table shows each run including database read latency and log write latency which are the two key performance metrics for MS Exchange Jetstress testing.

JetstressPerfwandwocompression

Note: The performance numbers above are not the peak or best performance Nutanix can deliver, they are just one of the many test scenarios I ran.

We can see the delta between the No Compression and Inline compression is almost zero. This test shows that while we all know inline data reduction has overheads on the I/O path, that does not necessarily translate into slower performance for the application.

In this case, Nutanix in-line compression is so efficient, that customers can enjoy excellent data efficiencies for applications like MS Exchange, with virtually no impact on performance or additional CPU overheads on the CVM.

Oh and all of this performance on Acropolis Hypervisor (AHV)!

Sizing infrastructure based on vendor Data Reduction assumptions – Part 2

In part 1, we discussed how data reduction ratios can, and do, vary significantly between customers and datasets and that making assumptions on data reduction ratios, even when vendors provide guarantees, does not protect you from potentially serious problems if the data reduction ratios are not achieved.

In Part 2 we will go through an example of how misleading data reduction guarantees can be.

One HCI manufacturer provides a guarantee promising 10:1 which sounds too good to be true, and that’s because it, quite frankly, isn’t true. The guarantee includes a significant caveat for the 10:1 data reduction:

The savings/efficiency are based on the assumption that you configure a backup policy to take at least one <redacted> backup per day of every virtual machine on every<redacted> system in a given VMware Datacenter with those backups retained for 30 days.

I have a number of issues with this limitation including:

  1. The use of the word “backup” referring directly/indirectly to data reduction (savings)
  2. The use of the word “backup” when referring to metadata copies within the same system
  3. No actual deduplication or compression is required to achieve the 10:1 data reduction because metadata copies (or what the vendor incorrectly calls “backups”) are counted towards deduplication.

It is important to note, I am not aware of any other vendor who makes the claim that metadata copies ( Snapshots / Point in time copies / Recovery points etc.) are deduplication. They simply are not.

I have previously written about what should be counted in deduplication ratios, and I encourage you to review this post and share your thoughts as it is still a hot topic and one where customers are being oversold/mislead regularly in my experience.

Now let’s do the math on my claim that no actual deduplication or compression is required to achieve the 10:1 ratio.

Let’s use a single 1 TB VM as a simple example. Note: The size doesn’t matter for the calculation.

Take 1 “backup” (even though we all know this is not a backup!!) per day for 30 days and count each copy as if it was a full backup to disk, Data logically stored now equals 31TB  (1 TB + 30 TB).

The actual Size on disk is only a tiny amount of metadata higher than the original 1TB as the metadata copies pointers don’t create any copies of the data which is another reason it’s not a backup.

Then because these metadata copies are counted as deduplication, the vendor reports a data efficiency of 31:1 in its GUI.

Therefore, Effective Capacity Savings = 96.8% (1TB / 31TB = 0.032) which is rigged to be >90% every time.

So the only significant capacity savings which are guaranteed come from “backups” not actual reduction of the customer’s data from capacity saving technologies.

As every modern storage platform I can think of has the capability to create metadata based point in time recovery points, this is not a new or even a unique feature.

So back to our topic, if you’re sizing your infrastructure based on the assumption of the 10:1 data efficiency, you are in for rude shock.

Dig a little deeper into the “guarantee” and we find the following:

It’s the ratio of storage capacity that would have been used on a comparable traditional storage solution to the physical storage that is actually used in the <redacted> hyperconverged infrastructure.  ‘Comparable traditional solutions’ are storage systems that provide VM-level synchronous replication for storage and backup and do not include any deduplication or compression capability.

So if you, for example, had a 5 year old NetApp FAS, and had deduplication and/or compression enabled, the guarantee only applies if you turned those features off, allowed the data to be rehydrated and then compared the results with this vendor’s data reduction ratio.

So to summarize, this “guarantee” lacks integrity because of how misleading it is. It  is worthless to any customer using any form of enterprise storage platform probably in the last 5 –  10 years as the capacity savings from metadata based copies are, and have been, table stakes for many, many years from multiple vendors.

So what guarantee does that vendor provide for actual compression and deduplication of the customers data? The answer is NONE as its all metadata copies or what I like to call “Smoke and Mirrors”.

Summary:

“No one will question your integrity if your integrity is not questionable.” In this case the guarantee and people promoting it have questionable integrity especially when many customers may not be aware of the difference between metadata copies and actual copies of data, and critically when it comes to backups. Many customers don’t (and shouldn’t have too) know the intricacies of data reduction, they just want an outcome and 10:1 data efficiency (saving) sounds to any reasonable person as they need 10x less than I have now… which is clearly not the case with this vendors guarantee or product.

Apart from a few exceptions which will not be applicable for most customers, 10:1 data reduction is way outside the ballpark of what is realistically achievable without using questionable measurement tactics such as counting metadata copies / snapshots / recovery points etc.

In my opinion the delta in the data reduction ratio between all major vendors in the storage industry for the same dataset, is not a significant factor when making a decision on a platform. This is because there are countless other substantially more critical factors to consider. When the topic of data reduction comes up in meetings I go out of my way to ensure the customer understands this and has covered off the other areas like availability, resiliency, recoverability, manageability, security and so on before I, quite frankly waste their time talking about table stakes capability like data reduction.

I encourage all customers to demand nothing less of vendors than honestly and integrity and in the event a vendor promises you something, hold them accountable to deliver the outcome they promised.

Sizing infrastructure based on vendor Data Reduction assumptions – Part 1

One of the most common mistakes people make when designing solutions is making assumptions. Assumptions in short are things an architect has failed to investigate and/or validate which puts a project at risk of not delivering the desired business outcome/s.

A great example of a really bad assumption to make is what data reduction ratio a storage platform will deliver.

But what if a vendor offers a data reduction guarantee and promises to give you as much equipment required if the ratio is not achieved, you’re protected right? The risk of your assumption being wrong is mitigated with the promise of free storage. Hooray!

Let’s explore this for a minute using an example of one of the more ludicrous guarantees going around the industry at the moment:

A guarantee of 10:1 data reduction!

Let’s say we have 100TB of data, that means we’d only need 10TB right? This might only be say, 4RU of equipment which sounds great!

After deployment, we start migrating and we only get a more realistic 2:1 data reduction, at which point the project stalls due to lack of capacity.

I go back to the vendor and lets say, best case scenario they agree on the spot (HA!) to give you more equipment, its unlikely to be delivered in less than 4 weeks.

So your project is delayed a minimum of 4 weeks until the equipment arrives. You now need to go through your change control process and if you’re doing this properly it would be documented with detailed steps on how to install the equipment, including appropriate back out strategies in the event of issues.

Typically change control takes some time to prepare, go through approvals, documentation etc especially in larger mission critical environments.

When installing any equipment you should also have documented operational verification steps to ensure the equipment has been installed correctly and is highly available, performing as expected etc.

Now that the new equipment is installed, the project continues and all 100TB of your data has been migrated to the new platform. Hooray!

Now let’s talk about the ongoing implications of the assumption of 10:1 data reduction only resulting in a much more realistic 2:1 ratio.

We now have 5x more equipment than we expected, so assuming the original 10TB was 4RU, we would now have 20RU of equipment which is taking up valuable real estate in our datacenter, or which may have required you to lease another rack in your datacenter.

If the product you purchased was a SAN/NAS, you now have lower IOPS/GB as you have just added a bunch more disk shelves to the existing controllers. This is because the controllers have a finite amount of performance, and you’ve just added more drives for it to manage. More drives on a traditional two controller SAN/NAS is only a good thing if the controller is not maxed out, and with flash ever increasing in performance, Controllers will be assuming they are not already the bottleneck.

If the product was HCI, now you require considerably more network interfaces. Depending on the HCI platform, you may require more hypervisor licensing, further increasing CAPEX and OPEX.

Depending on the HCI product, can you even utilise the additional storage without changing the virtual machines configuration? It might sound silly but some products don’t distribute data throughout the cluster, rather having mirrored objects so you may even need to create more virtual disks or distribute the VMs to make use of the new capacity.

Then you need to consider if the HCI product has any scale limitations, as these may require you to redesign your solution.

What about operational expenses? We now have 5x more equipment, so our environmental costs such as power & cooling will increase significantly as will our maintenance windows where we now have to patch 5x more hypervisor nodes in the case of HCI.

Typically customers no longer size for 3-5 years due to the fact HCI is becoming the platform of choice compared to SAN/NAS. This is great but when your data reduction assumption is wrong, (in this example off by 5x) the ongoing impact is enormous.

This means as you scale, you need to scale at 5x the rate you originally designed for. That’s 5x more rack units (RU), 5x more Power, 5x more cooling required, potentially even 5x more hypervisor licensing.

What does all of this mean?

Your Total Cost of Ownership (TCO) and Return on Investment (ROI) goes out the window!

Interestingly, Nutanix recently considered offering a data reduction guarantee and I was one of many who objected and strongly recommended we not drop to the levels of other vendors just because it makes the sales cycle easier.

All of the reasons above and more were put to Nutanix product management and they made the right decision, even though Nutanix data reduction (and avoidance) is very strong, we did not want to put customers in a position where their business outcomes were potentially at risk due to assumptions.

Summary:

While data reduction is a valuable part of a storage platform, the benefits (data reduction ratio) can and do vary significantly between customers and datasets. Making assumptions on data reduction ratios even when vendors provide lots of data showing their averages and providing guarantees, does not protect you from potentially serious problems if the data reduction ratios are not achieved.

In Part 2, I will go through an example of how misleading data reduction guarantees can be.