Monthly Archives: July 2016

The All-Flash Array (AFA) is Obsolete!

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Over the last few years, I’ve had numerous customers ask about how Nutanix can support bare metal workloads. Up until recently, I haven’t had an answer the customers have wanted to hear.

As a result, some customers have been stuck using their exisiting SAN or worse still being forced to go out and buy a new SAN.

As a result many customers who have wanted to use or have already deployed hyperconverged infrastructure (HCI) for all other workloads are stuck managing an all flash array silo to service some bare metal workloads.

In June at .NEXT 2016, Nutanix announced Acropolis Block Services (ABS) which now allows bare metal workloads to be serviced by new or existing Nutanix clusters.

ABSoverview

As Nutanix has both hybrid (SSD+SATA) and all-flash nodes, customers can chose the right node type/s for their workloads and present the storage externally for bare metal workloads while also supporting Virtual Machines and Acropolis File Services (AFS) and containers.

So why would anyone buy an all-flash array? Let’s discuss a few scenarios.

Scenario 1: Bare metal workloads

Firstly, what applications even need bare metal these days? This is an important question to ask yourself. Challenge the requirement for bare metal and see if the justifications are still valid and if so, has anything changed which would allow virtualization of the applications. But this is a topic for another post.

If a customer only needs new infrastructure for bare metal workloads, deploying Nutanix and ABS means they can start small and scale as required. This avoids one of the major pitfalls of having to size a monolithic centralised, dual controller storage array.

While some AFA vendors can/do allow for non-disruptive controller upgrades, it’s still not a very attractive proposition, nor is it quick or easy. and reduces resiliency during the process as one of two controllers are offline. Nutanix on the other hand performs one click rolling upgrades which mean the largest the cluster, the lower the impact of an upgrade as it is performed one node at a time without disruption and can also be done without risk of a subsequent failure taking storage offline.

If the environment will only ever be used for bare metal workloads, no problem. Acropolis Block Services offers all the advantages of an All Flash Array, with far superior flexibility, scalability and simplicity.

Advantages:

  1. Start small and scale granularly as required allowing customers to take advantage of newer CPU/RAM/Flash technologies more frequently
  2. Scale performance and capacity by adding node/s
  3. Scale capacity only with storage-only nodes (which come in all flash)
  4. Automatically scale multi-pathing as the cluster expands
  5. Solution can support future workloads including multiple hypervisors / VMs / file services & containers without creating a silo
  6. You can use Hybrid nodes to save cost while delivering All Flash performance for workloads which require it by using VM flash pinning which ensures all data is stored in flash and can be specified on a per disk basis.
  7. The same ability as an all flash array to only add compute nodes.

Disadvantages:

  1. Your all-flash array vendor reps will hound you.

Scenario 2: Mixed workloads inc VMs and bare metal

As with scenario 1, deploying Nutanix and ABS means customers can start small and scale as required. This again avoids the major pitfall of having to size a monolithic centralised, dual controller storage array and eliminates the need for separate environments.

Virtual machines can run on compute+storage nodes while bare metal workloads can have storage presented by all nodes within the cluster, including storage-only nodes. For those who are concerned about (potential but unlikely) noisy neighbour situations, specific nodes can also be specified while maintaining all the advantages of Nutanix one-click, non-disruptive upgrades.

Advantages:

  1. Start small and scale granularly as required allowing customers to take advantage of newer CPU/RAM/Flash technologies more frequently
  2. Scale performance and capacity by adding node/s
  3. Scale capacity only with storage-only nodes (which also come in all flash)
  4. Automatically scale multi-pathing for bare metal workloads as the cluster expands
  5. Solution can support future workloads including multiple Hypervisors / VMs / file services & containers without creating a silo.

Disadvantages:

  1. Your All-Flash array vendor reps will hound you.

What are the remaining advantages of using an all flash array?

In all seriousness, I can’t think of any but for fun let’s cover a few areas you can expect all-flash array vendors to argue.

Performance

Ah the age old appendage measuring contest. I have written about this topic many times, including in one of my most popular posts “Peak performances vs Real world performance“.

The fact is, every storage product has limits, even all-flash arrays and Nutanix. The major difference is that Nutanix limits are per cluster rather than per Dual Controller Pair, and Nutanix can continue to scale the number of nodes in a cluster and continue to increase performance. So if ultimate performance is actually required, Nutanix can continue to scale to meet any performance/capacity requirements.

In fact, with ABS the limit for performance is not even at the cluster layer as multiple clusters can provide storage to the same bare metal server/s while maintaining single pane of glass management through PRISM Central.

I recently completed some testing with where I demonstrated the performance advantage of storage only nodes for virtual machines as well as how storage-only nodes improve performance for bare metal servers using Acropolis Block Services which I will be publishing results for in the near future.

Data Reduction

Nutanix has had support for deduplication, compression for a long time and introduced Erasure Coding (EC-X) mid 2015. Each of these technologies are supported when using Acropolis Block Services (ABS).

As a result, when comparing data reduction with all-flash array vendors, while the implementation of these data reduction technologies varies between vendors, they all achieves similar data reduction ratios when applied to the same dataset.

Beware of some vendors who include things like backups in their deduplication or data reduction ratios, this is very misleading and most vendors have the same capabilities. For more information on this see: Deduplication ratios – What should be included in the reported ratio?

Cost

Here we should think about what are the age old problems are with centralized shared storage (like AFAs)? Things like choosing the right controllers and the fact when you add more capacity to the storage, you’re not (or at least rarely) scaling the controller/s at the same time come to mind immediately.

With Nutanix and Acropolis Block Services you can start your All Flash solution with three nodes which means a low capital expenditure (CAPEX) and then scale either linearly (with the same node types) or non-linearly (with mixed types or storage only nodes) as you need to without having to rip and replace (e.g.: SAN controller head swaps).

Starting small and scaling as required also allows you to take advantage of newer technologies such as newer Intel chipsets and NVMe/3D XPoint to get better value for your money.

Starting small and scaling as required also minimizes – if not eliminates – the risk of oversizing and avoids unnecessary operational expenses (OPEX) such as rack space, power, cooling. This also reduces supporting infrastructure requirements such as networking.

Summary:

As shown below, the Nutanix Acropolis Distributed Storage Fabric (ADSF) can support almost any workload from VDI to mixed server workloads, file, block , big data, business critical applications such as SAP / Oracle / Exchange / SQL and bare metal workloads without creating silos with point solutions.

NutanixSingleFabricAllWorkloads

In addition to supporting all these workloads, Nutanix ADSF scalability both from a capacity/performance and resiliency perspective ensures customers can start small and scale when required to meet their exact business needs without the guesswork.

With these capabilities, the All-Flash array is obsolete.

I encourage everyone to share (constructively) your thoughts in the comments section.

Note: You must sign in to comment using WordPress, Facebook, LinkedIn or Twitter as Anonymous comments will not be approved,

Related Articles:

  1. Things to consider when choosing infrastructure.

  2. Scale out performance testing with Nutanix Storage Only Nodes

  3. What’s .NEXT 2016 – Acropolis Block Services (ABS)

  4. Scale out performance testing of bare metal workloads on Acropolis Block Services (Coming soon)

  5. What’s .NEXT 2016 – Any node can be storage only

  6. What’s .NEXT 2016 – All Flash Everywhere!

Scale out performance testing with Nutanix Storage Only Nodes

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At Nutanix inaugural user conference in 2015, Storage Only nodes were announced which allowed customers for the first time to scale capacity without having to add compute nodes. This allows customers more flexibility and eliminates the need to license the storage nodes for vSphere as storage only nodes run Acropolis Hypervisor (AHV) and are managed entirely through PRISM.

A common question from prospective and existing Nutanix customers is what if my VMs storage exceeds the capacity of a Nutanix node? The answer is detailed in this blog post but in short, as the Acropolis Distributed Storage Fabric (ADSF) distributes data throughout the cluster at a 1MB granularity, a VMs storage can exceed the local node and performance even improves including reads from the capacity (SAS/SATA) tier.

Storage only nodes were previously limited to the NX-6035C (and Dell XC/Lenovo HX equivalents) but at Nutanix .NEXT conference in Las Vegas 2016, it was announced that any node (including all-flash) can be a storage only node.

This means even for high performance and/or high capacity environments, Nutanix clusters can be scaled without the need to add compute node or purchase additional licensing if you are running vSphere as the hypervisor.

However to date Nutanix are yet to publish any performance data showing the value of storage only nodes, so I decided to run a few tests and demonstrate the value of the Acropolis Distributed Storage Fabric (ADSF) and Storage Only Nodes.

Before we get to the performance data, to avoid competitors inevitable attempts to create FUD about Nutanix performance, I will not be publishing the exact specifications of the node types, drive or Jetstress configurations. I will be publishing the IOPS/latency and database creation, duplication and checksumming durations of the direct comparisons which clearly show the performance advantage of storage only nodes.

Jetstress was not configured to demonstrate maximum performance of the underlying Nutanix solution, it was configured to achieve around 1000 IOPS which is typically higher than even a large Exchange deployment requires per instance. This also allows this test to demonstrate how performance improves when the cluster is performing real world levels of IO (at least in the case of Exchange for this example).

The performance advantage will vary between node types and based on how many storage only nodes are added to the cluster. But the point of this example is to show that ADSF is a truely distributed storage fabric and the storage only nodes and additional Nutanix Controller VMs (CVMs) servicing replication (RF) traffic and remote reads significantly improves performance for VMs residing on the Compute+Storage nodes.

Test Overview:

The first test will be performed using four Jetstress VMs running on a four node cluster. The second test will be performed after an additional four storage only nodes are added to the cluster to form an eight node cluster. Before the second test the cluster will be wiped of all data with the exception of the Windows 2012 R2 template and all Jetstress DBs will be created from scratch so we can compare DB creation as well as performance and DB checksumming durations. Wiping all data also ensures there is no pre-warming of the extent cache (in memory read cache) or metadata cache.

Test Preparation:

I performed a cluster stop / cluster destroy / cluster create to ensure the cluster is totally clean and that we have a fair baseline for the test. The cluster was made up of four nodes.

I then created a base Windows 2012 R2 virtual machine with 4 PVSCSI adapters and 9 vDisks, one for the OS, 4 for the DBs and 4 for the logs. DB drives were formatted with 64k allocation size and log drives with 4k as the different allocation size and seperate virtual disks has shown approx 25% performance improvement in my testing not to mention I recommend In-Line compression and Erasure Coding (EC-X) for Exchange databases and no data reduction for logs.

Jetstress was configured to use 80% of the vDisks capacity which resulted in approx 80% of the Nutanix storage pool capacity being utilised for the test. I will point out these were not low capacity nodes such as NX3060s so the database creation time is significant because there was lots of data to create.

I then cloned the VM 3 times and spread the 4 VMs across 4 Nutanix Nodes running ESXi 5.5 Update 3.

Test 1: Create Databases and run 2hr test

The databases creation phase creates one database, then Jetstress duplicates the database in this case 3 times and immediately after creation the performance test begins.

Note: No data reduction was used for this test as it will result in unrealistic data reduction and performance results as I described in the post Jetstress Testing with Intelligent Tiered Storage Platforms.

I configured Jetstress in this way to ensure the extent cache (in memory read cache) was not pre-warmed and so the results of the test would be fair and repeatable.

Once the performance test completed, I waited for each test to complete before allowing the database checksum validation task to complete. (This is done by using the Multi-host option in Jetstress).

The results for each of the four Jetstress VMs are shown below including the average across the VMs for each of the difference metrics.

Jetstress4NodesSummary

Observations from Test 1:

  1. We achieved the desired >1000 IOPS per VM
  2. Performance was consistent across all Jetstress instances
  3. Log writes were in the 1ms range as they were serviced by the ADSF Oplog (persistent write buffer)
  4. Database reads were on average just under 10ms which is well below the Microsoft recommended 20ms
  5. The Database creation time averaged 2hrs 24mins
  6. The duplication of 3 databases averaged 4hrs 17mins
  7. The database checksum took on average around 38mins

Test 2: Delete all data, Add four nodes to the cluster & repeat test 1

All Jetstress VMs were deleted and a full curator scan manually initiated to ensure all data was fully removed from disk prior to beginning the next test which ensured a fair baseline.

Four Jetstress VMs were then deployed from the same template, powered on and the saved Jetstress configuration was applied before beginning the test.

Note: The Jetstress thread count was not changed and remains the same as for Test 1.

As with Test 1 the databases creation phase created one database, then Jetstress duplicates the database 3 times and immediately after creation the performance test begins and ran for the same 2hr duration.

The results for each of the four Jetstress VMs are shown below including the average across the VMs for each of the difference metrics.

Jetstress8NodesSummary

Observations from Test 2:

  1. Achieved IOPS jumped by almost 2x
  2. Log writes average latency was lower by 13%
  3. Database write latency dropped by >20%
  4. Database read latency dropped by almost 2x
  5. The Database creation time was just under 15 mins faster
  6. The duplication of 3 databases improved by almost 35 mins
  7. The database checksum was 40 seconds faster.

Without changing the Jetstress thread count, due to the improved performance of the cluster the achieved IOPS jumped by 2x!!

Summary:

These tests is a clear demonstration of the scalability advantage of the Acropolis Distributed Storage Fabric (ADSF) and storage only nodes for customers wanting to increase performance and/or capacity in their HCI environment.

The ability of ADSF to distribute write IO across all nodes within a cluster means write performance improves significantly with the addition of nodes (including storage only) to the cluster while reducing read and write latency due to the decreased workload on the compute + storage nodes servicing the VMs.

But data locality is lost with storage only nodes, right?

Wrong! Storage only nodes actually improve (yes, improve!) data locality by maximising the amount of available space on the compute+storage nodes. This is as a direct result of storage only nodes accepting replication data for write IO and storing the 2nd or 3rd copies (in the case of RF3) on the storage only nodes. This is also demonstrated by the lower read latency observed during this test.

Storage only nodes not only improve the performance and capacity for Virtual machines, but also for physical servers using Acropolis Block Services (ABS) and users of Acropolis File Services (AFS) both of which had enhancements announced at .NEXT 2016 this year.

Storage Performance : ReFS vs NTFS

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I am regularly asked by customers if they should use NTFS or the newer ReFS when formatting drives for applications like Microsoft Exchange and SQL.

Most customers are asking in the context of performance, so I thought I would share some recent testing results using MS Exchange Jetstress.

Firstly, what is ReFS and when/would you use ReFS?

What is ReFS?

Resilient File System (ReFS) is a new local file system. It maximizes data availability, despite errors that would historically cause data loss or downtime. Data integrity ensures that business critical data is protected from errors and available when needed. Its architecture is designed to provide scalability and performance in an era of constantly growing data set sizes and dynamic workloads.

The key features of ReFS are:

  • Integrity: ReFS stores data so that it is protected from many of the common errors that can cause data loss. File system metadata is always protected. Optionally, user data can be protected on a per-volume, per-directory, or per-file basis. If corruption occurs, ReFS can detect and, when configured with Storage Spaces, automatically correct the corruption. In the event of a system error, ReFS is designed to recover from that error rapidly, with no loss of user data.
  • Availability: ReFS is designed to prioritize the availability of data. With ReFS, if corruption occurs, and it cannot be repaired automatically, the online salvage process is localized to the area of corruption, requiring no volume down-time. In short, if corruption occurs, ReFS will stay online.
  • Scalability: ReFS is designed for the data set sizes of today and the data set sizes of tomorrow; it’s optimized for high scalability.
  • App Compatibility: To maximize AppCompat, ReFS supports a subset of NTFS features plus Win32 APIs that are widely adopted.
  • Proactive Error Identification: The integrity capabilities of ReFS are leveraged by a data integrity scanner (a “scrubber”) that periodically scans the volume, attempts to identify latent corruption, and then proactively triggers a repair of that corrupt data.

Source: Microsoft Technet – Resilient file system

From my perspective, ReFS makes sense when using physical servers with unintelligent storage such as JBOD or any storage which does not perform things such as checksums on both read and write IO and enforce Force Unit Access (FUA). However if you’re deploying MS Exchange / MS SQL etc on intelligent storage such as Nutanix Acropolis Distributed Storage Fabric (ADSF) then ReFS is not required as data integrity is already ensured by the storage layer. For example, in the event of silent data corruption, ADSF will detect the corruption on read and simply retrieve the data from the second copy which resides on a different physical drive on a different node within the cluster. This is also transparent to the Virtual Machine, OS and application and therefore compatible with any OS and application.

As a result ReFS (at least in its current version) is not required for deployments of Microsoft OS,Apps on Nutanix or other storage solutions if they have the same functionality.

None the less, this is not supposed to be a post about Nutanix, so let’s now look at the test bed and results of the performance comparison so you can make an informed decision about which to use

Test Bed Setup

The test bed setup is as follows:

Hypervisor: ESXi 5.5 Rel: 3248547

2 Virtual Machines cloned from the same template:
Windows 2012 R2 , 4 vCPUs , 24Gb RAM
4 Paravirtual SCSI adapters
1 vDisk for OS , 4 vDisks for DB, 4 vDisks for Logs

Both VMs are running on the same node, with only one VM running Jetstress at a time. All tests runs were back to back to ensure results would be fair and to check the consistency of the results.

The only difference between the two VMs is as follows:

VM1:

4 vDisks formatted with NTFS and 64k allocation size for Database
4 vDisks formatted with NTFS and 4k allocation size for Logs

VM2:

All 8 vDisks formatted with ReFS (64k)

Tests performed:

Three Jetstress runs per VM one after another, importantly with new databases created before each run to ensure a fair baseline. Doing this ensured the results were skewed by having the Extent Cache (In-Memory Read Cache) or the Medusa Cache (In-Memory Metadata Cache) pre-warmed.

Each run used 16 threads and resulted in the following results.

ReFS Jetstress Instance:

Run One: 6697 IOPS
Run Two: 6896 IOPS
Run Three: 6796 IOPS

Average: 6796 IOPS (approx +-3% between runs)

NTFS Jetstress Instance:

Run One: 7328 IOPS
Run Two: 7240 IOPS
Run Three: 7296 IOPS

Average: 7288 IOPS (approx +-1% between runs)

Result:

The difference being approx 7% higher performance and more consistency when using NTFS.

Additional Tests:

Out of interest I repeated the tests with a lower thread count (8) to see if the results were consistent as we decreased the threads.

8 Threads:

ReFS: 3921 IOPS
NTFS: 4079 IOPS

The result again went in favour of NTFS by approx 4%. This makes sense as the advantage would diminish as the pressure on the storage layer reduces.

Autotune Result:

I then repeated the test with Jetstress set to Autotune with the following results.

ReFS: 16673 IOPS @ 91 threads (Autotuned)
NTFS: 17758 IOPS @ 96 threads (Autotuned)

The autotune results again show that NTFS has an advantage over ReFS of approx 7% which is in line with the results using 16 threads manually configured.

CPU overheads comparisons

ReFS Jetstress Instance:

Run One:Avg 39.293% (Min 23.725 / Max 44.127)
Run Two:Avg 40.28% (Min 37.785 / Max 44.366)
Run Three: Avg 40.175% (Min 36.520 / Max 43.843)

Average: 39.916%

NTFS Jetstress Instance:

Run One: Avg 39.390% (Min 36.746 / Max 42.651)
Run Two: Avg 39.719% (Min 23.613 / Max 45.960)
Run Three:Avg 39.844% (Min 37.347 / Max 42.400)

Average: 39.651%

So NTFS achieved 7% better performance than ReFS using the same thread count even with the Data Integrity features turned off for ReFS volumes without using any more CPU.

Summary:

Overall these tests demonstrate that NTFS consistently outperforms ReFS for MS Exchange type IO patterns. For intelligent storage, ReFS has no advantages and NTFS will provide better performance with roughly the same CPU overheads and without any risk of data integrity issues.

As the recommendation for ReFS is to disable the data integrity features for Exchange, I am yet to hear a good justification as to why ReFS is recommended, but I welcome any comments from those in the know and if the justifications are solid I will update the post to reflect these reasons.

Related Articles:

1. Jetstress Testing with Intelligent Tiered Storage Platforms

2. MS Exchange on Nutanix Acropolis Hypervisor (AHV)

3. How to successfully Virtualize MS Exchange

4. Deduplication and MS Exchange