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.

Erasure Coding Overheads – Part 1

Erasure Coding has become a hot topic in the Hyperconverged Infrastructure (HCI) world since Nutanix announced its implementation (EC-X) in June 2015 at its inaugural user conference and VMware have followed up recently with support for EC in its 6.2 release for All-Flash deployments.

As this is a new concept to many in the industry there have been a lot of questions about how it works, what are the benefits and of course what are the trade offs.

In short, regardless of vendor Erasure Coding will allow data to be stored with tuneable levels of resiliency such as single parity (similar to RAID 5) and double parity (similar to RAID 6) which provides more usable capacity compared to replication which is more like RAID 1 with ~50% usable capacity of RAW.

Not dissimilar to RAID 5/6, Erasure coding implementations have increased write penalties compared to replication (RF2 for Nutanix or FTT1 VSAN) similar to RAID 1.

For example, the write penalties for RAID are as follows:

  • RAID 1 = 2
  • RAID 5 = 4
  • RAID 6 = 6

Similar write penalties are true for Erasure coding depending on each vendors specific implementation and stripe size (either dynamic/fixed).

I have written a number of posts about Nutanix specific implimentation, for those who are interested see the following deep dive post:

Nutanix – Erasure Coding (EC-X) Deep Dive

VMware has also released a post titled The Use Of Erasure Coding In VMware Virtual SAN 6.2 covering their implementation of Erasure Coding by .

The article is well written and I would like to highlight two quotes from the post which are applicable to any implementation of Erasure coding, including Nutanix EC-X and VSAN.

Quote #1

Erasure Coding does not come for free. It has a substantial overhead in operations per second (IOPS) and networking.

Quote #2

In conclusion, customers must evaluate their options based on their requirements and the use cases at hand. RAID-5/6 may be applicable for some workloads on All-Flash Virtual SAN clusters, especially when capacity efficiency is the top priority. Replication may be the better option, especially when performance is the top priority (IOPS and latency). As always, there is no such thing as one size fits all.

Pros of Erasure Coding:

  • Increased usable capacity of RAW storage compared to replication
  • Potential to increase the amount of data stored in SSD tier
  • Lower cost/GB
  • Nutanix EC-X Implementation places parity on capacity tier to increase the effective SSD tier size

Cons of Erasure Coding:

  • Higher write overheads
  • Higher impact (read) in the event of drive/node failure
  • Performance will suffer significantly for I/O patterns with high percentage of overwrites
  • Increased computational overheads

Recommended Workloads to use Erasure Coding:

  • Write Once Read Many (WORM) workloads are the ideal candidate for Erasure Coding
  • Backups
  • Archives
  • File Servers
  • Log Servers
  • Email (depending on usage)

As many of the strong use cases for Erasure coding are workloads not requiring high IO, using Erasure Coding across both performance and capacity tiers can provide significant advantages.

Workloads not ideal for Erasure Coding:

  • Anything Write / Overwrite Intensive
  • VDI

This is due to VDI typically being very write intensive which would increase the overheads on the software defined storage. VDI is also typically not capacity intensive thanks to intelligent cloning so EC advantages would be minimal.

Summary:

Regardless of vendor, all Erasure Coding implementations have higher overheads than traditional replication such as Nutanix RF2/RF3 and VSANs FTT1/2.

The overheads will vary depending on:

  • The configured parity level
  • The stripe size (which may vary between vendors)
  • The I/O profile, the more write intensive the higher the overheads
  • If the striping is performed in-line on all data or post process on write cold data
  • If the stripe is degraded or not from a drive/node failure

The usable capacity also varies depending on:

  • The number of nodes in a cluster which can limit the stripe size (see the next point)
  • The stripe size (dependant on number of nodes in the cluster)
    • E.g.: A 3+1 will give usable capacity up to 75% and a 4+1 will give up to 80% usable capacity.

It is importaint to understand as the stripe size increases, the resulting usable capacity increases diminish. As the stripe size increases, so do the overheads on the storage controllers and network. The impact during a failure is also increased as is the risk of a drive or node failure impacting the stripe.

In Part 2, I am planning on publishing testing examples to show the performance delta between typical replication and erasure coding for a write intensive workload.

Related Articles:

  1. Large scale clusters and increased resiliency with RF3 + EC-X
  2. What I/O will Nutanix Erasure coding (EC-X) take effect on?
  3. Sizing assumptions for solutions with Erasure Coding (EC-X)