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

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:

Expanding Capacity on a Nutanix environment – Design Decisions

I recently saw an article about design decisions around expanding capacity for a HCI platform which went through the various considerations and made some recommendations on how to proceed in different situations.

While reading the article, it really made me think how much simpler this process is with Nutanix and how these types of areas are commonly overlooked when choosing a platform.

Let’s start with a few basics:

The Nutanix Acropolis Distributed Storage Fabric (ADSF) is made up of all the drives (SSD/SAS/SATA etc) in all nodes in the cluster. Data is written locally where the VM performing the write resides and replica’s are distributed based on numerous factors throughout the cluster. i.e.: No Pairing, HA pairs, preferred nodes etc.

In the event of a drive failure, regardless of what drive (SSD,SAS,SATA) fails, only that drive is impacted, not a disk group or RAID pack.

This is key as it limited the impact of the failure.

It is importaint to note, ADSF does not store large objects nor does the file system require tuning to stripe data across multiple drives/nodes. ADSF by default distributes the data (at a 1MB granularity) in the most efficient manner throughout the cluster while maintaining the hottest data locally to ensure the lowest overheads and highest performance read I/O.

Let’s go through a few scenarios, which apply to both All Flash and Hybrid environments.

  1. Expanding capacityWhen adding a node or nodes to an existing cluster, without moving any VMs, changing any configuration or making any design decisions, ADSF will proactively send replicas from write I/O to all nodes within the cluster, therefore improving performance while reactively performing disk balancing where a significant imbalance exists within a cluster.

    This might sound odd but with other HCI products new nodes are not used unless you change the stripe configuration or create new objects e.g.: VMDKs which means you can have lots of spare capacity in your cluster, but still experience an out of space condition.

    This is a great example of why ADSF has a major advantage especially when considering environments with large IO and/or capacity requirements.

    The node addition process only requires the administrator to enter the IP addresses and its basically a one click, capacity is available immediately and there is no mass movement of data. There is also no need to move data off and recreate disk groups or similar as these legacy concepts & complexities do not exist in ADSF.

    Nutanix is also the only platform to allow expanding of capacity via Storage Only nodes and supports VMs which have larger capacity requirements than a single node can provide. Both are supported out of the box with zero configuration required.

    Interestingly, adding storage only nodes also increases performance, resiliency for the entire cluster as well as the management stack including PRISM.

  2. Impact & implications to data reduction of adding new nodesWith ADSF, there are no considerations or implications. Data reduction is truely global throughout the cluster and regardless of hypervisor or if you’re adding Compute+Storage or Storage Only nodes, the benefits particularly of deduplication continue to benefit the environment.

    The net effect of adding more nodes is better performance, higher resiliency, faster rebuilds from drive/node failures and again with global deduplication, a higher chance of duplicate data being found and not stored unnecessarily on physical storage resulting in a better deduplication ratio.

    No matter what size node/s are added & no matter what Hypervisor, the benefits from data reduction features such as deduplication and compression work at a global level.

    What about Erasure Coding? Nutanix EC-X creates the most efficient stripe based on the cluster size, so if you start with a small 4 node cluster your stripe would be 2+1 and if you expand the cluster to 5 nodes, the stripe will automatically become 3+1 and if you expand further to 6 nodes or more, the stripe will become 4+1 which is currently the largest stripe supported.

  3. Drive FailuresIn the event of a drive failure (SSD/SAS or SATA) as mentioned earlier, only that drive is impacted. Therefore to restore resiliency, only the data on that drive needs to be repaired as opposed to something like an entire disk group being marked as offline.

    It’s crazy to think a single commodity drive failure in a HCI product could bring down an entire group of drives, causing a significant impact to the environment.

    With Nutanix, a rebuild is performed in a distributed manner throughout all nodes in the cluster, so the larger the cluster, the lower the per node impact and the faster the configured resiliency factor is restored to a fully resilient state.

At this point you’re probably asking, Are there any decisions to make?

When adding any node, compute+storage or storage only, ensure you consider what the impact of a failure of that node will be.

For example, if you add one 15TB storage only node to a cluster of nodes which are only 2TB usable, then you would need to ensure 15TB of available space to allow the cluster to fully self heal from the loss of the 15TB node. As such, I recommend ensuring your N+1 (or N+2) node/s are equal to the size of the largest node in the cluster from both a capacity, performance and CPU/RAM perspective.

So if your biggest node is an NX-8150 with 44c / 512GB RAM and 20TB usable, you should have an N+1 node of the same size to cover the worst case failure scenario of an NX-8150 failing OR have the equivalent available resources available within the cluster.

By following this one, simple rule, your cluster will always be able to fully self heal in the event of a failure and VMs will failover and be able to perform at comparable levels to before the failure.

Simple as that! No RAID, Disk group, deduplication, compression, failure, or rebuild considerations to worry about.

Summary:

The above are just a few examples of the advantages the Nutanix ADSF provides compared to other HCI products. The operational and architectural complexity of other products can lead to additional risk, inefficient use of infrastructure, misconfiguration and ultimately an environment which does not deliver the business outcome it was originally design to.

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)