Tag Archives: PVSCSI

Nutanix Scalability – Part 3 – Storage Performance for a single Virtual Machine

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Continuing on from Part 1 where we discussed how Nutanix can scale storage capacity separately to compute using storage only nodes, we will now cover how Nutanix can scale the storage performance of a Virtual server including beyond the capabilities of a single (scaled up) node.

Virtual machines, just like traditional physical servers benefit from having multiple storage controllers (e.g.: RAID controllers) and multiple drives regardless of their type (e.g.: HDD/SSD etc).

The same is true for VMs on Nutanix ADSF, more storage controllers and more virtual disks increase the storage performance.

For traditional hypervisors such as ESXi and Hyper-V, ensure you have the maximum four Paravritual SCSI controllers (PVSCSI) assigned to VM’s requiring the highest performance and low latency. Having multiple controllers means more queues are available to the virtual disks and therefore the less bottlenecks which can cause latency and inefficiency for the vCPUs assigned to the VM.

Because the benefits of multiple virtual SCSI adapters is so significant, Nutanix decided to ensure this functionality is achieved by default when using Nutanix’ next Generation Hypervisor, AHV with what is known as “Turbo Mode“.

This means virtual machines running on AHV are optimised by default at the virtual storage controller layer, removing the complexity for customers having to understand and configure virtual storage controllers.

Regarding Virtual disks, for optimal performance you’ll need to use at least four assuming you’re using the recommended four Paravirtual SCSI controllers (i.e.: One per SCSI controller) but since we’re talking about scaling performance, let’s talk about more extreme examples.

Let’s say we have an MS Exchange server with 20 databases, the performance requirements for each database is typically in the range of hundred of IOPS, in which case I would recommend one virtual disk (e.g.: VMDK) per database and another for the logs.

In the case of a large MS SQL server which may require tens or hundreds of thousands of IOPS to a single database, I recommend using multiple vDisks per database which involves Splitting SQL datafiles across multiple VMDKs to optimise VM performance.

In both examples, the virtual disks would be spread evenly across the four PVSCSI controllers if using ESXi or Hyper-V whereas AHV customers would just create the vDisks and each vDisk would by default enjoy a dedicated path direct to Nutanix ADSF’s I/O engine called “stargate”.

For more information about configuring virtual storage controllers and multiple virtual disks, see: SQL & Exchange performance in a Virtual Machine which goes through the process step by step.

At this stage we’ve learned to get optimal performance, we need to use multiple virtual disks regardless of hypervisor, and for traditional hypervisors (ESXi & Hyper-V) we need to assign/configure multiple PVSCSI adapters and spread out virtual disks evenly across them.

Let’s say you have a VM running a monster SQL workload, and the nodes/cluster has been sized correctly and the active working set (data) resides 100% in the SSD tier or it’s an all flash cluster.

The VM is also running on AHV enjoying Turbo Mode (or ESXi/Hyper-V with four PVSCSI controllers) and you’ve added 16 vDisks and spanned your database across the vDisks, BUT you still need more performance. What can we do next?

The good news is, Nutanix has lots of way’s to scale performance so let’s look at a few of them:

  • Increase the vCPU of the Nutanix Controller VM (CVM)

This is rarely required, but it’s important to understand that Nutanix is just software running inside a VM, so simply increasing the vCPUs assigned to the CVM gives more available power to drive front end I/O as well as background cluster functionality.

The CVM automatically allows N-2 of the CVMs vCPUs to stargate (the I/O engine) which means if you add more vCPUs to the CVM, you will get more potential front and back end IO.

If your application performance is being impacted due to the local CVM being saturated (firstly, well done as this is very rare), but adding say 2 more vCPUs to the CVM may be enough to alleviate the bottleneck and give you much improved performance. I’ve seen this situation before and for the relatively low “cost” of 2 vCPUs, it can be well wroth it.

It’s important to note you can increase the vCPUs of just a single CVM, multiple CVMs or all CVMs within the cluster depending on your requirements and cluster design. e.g.: In a mixed cluster of nodes with 22c processors and 10c processors, you may move the critical VMs to the nodes with 22c processors and increase the CVM by 2vCPUs while leaving the nodes with the smaller 10c processors at default CVM size. This would deliver increased performance for the entire cluster while the most benefits would be felt on the 22c nodes.

For those interested in the Pros and Cons of the CVM and it’s use of host resources, please review: Cost vs Reward for the Nutanix Controller VM (CVM)

  • Increase the vRAM of the Nutanix Controller VM (CVM)

Increasing the CVMs RAM is another quick and easy way to improve performance. The two main reasons adding RAM can improve performance is because part of the CVMs RAM acts as a read cache so depending on your application and dataset size, the additional read cache can make a huge difference.

The second reason is for CVM RAM allows additional medusa (metadata) cache which helps minimise read latency.

If you look at http://CVM_IP:2009/cache_stats (example below) and your “Range Cache Hit %” is 50%, Then you’re getting very good cache hits, whereas if it was just 5% then more RAM may result in significantly better read performance depending on the working set size.

The other critical factor for performance is the medusa cache. We want to see as close to 100% as possible for the “VDisk block map Cache Hit %” & “Extent group id map Cache Hit %”.

StargateCacheStats

The above is an example of a system which has an optimal CVM RAM size for the working set as the Range Cache Hit % of 50% and the “VDisk block map Cache Hit %” & “Extent group id map Cache Hit %” are both sitting consistently at 100%.

The above cache and medusa hit rates are from a test cluster and it was achieving the following performance for a database checksum task (100% read).

ExamplePerformanceWith100%MedusaHitRate

The key here is the very low read latency which peaked at 0.35ms and sustained around 0.18ms over the course of several hours.

Signs of insufficient CVM RAM can be inconsistent read latency so if you’re observing this issue, review http://CVM_IP:2009/cache_stats and contact support for advise on CVM RAM sizing.

Note: There is no “harm” in adding more CVM RAM as long as the CVM is sized within a NUMA node to ensure memory performance remains optimal, the only impact is less available RAM for other Virtual Machines.

Let’s recap where we’re at:

The VM is on AHV enjoying Turbo Mode (or ESXi/Hyper-V with four PVSCSI controllers) with 16 vDisks and spanned your database across the vDisks. We’ve increased the CVM vCPUs and verified we have 100% hit rates for medusa and respectable 50% read cache hit rates, but we still need more performance, what else can we do?

  • Add storage only nodes

The benefits of adding storage only nodes, especially to a busy cluster is not only immediate but obvious when we look at the total IOPS, read and write latency.

If you’ve not read my post titled “Scale out performance testing with Nutanix Storage Only Nodes” I will quickly recap it for you, but I recommend reading the full article.

In short, I ran an MS Exchange Jetstress workload on 4 VMs on an optimally configured 4 node hybrid (SSD+SATA) cluster and achieved the following results.

Jetstress4NodesSummary

Observations from the baseline test:

  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

I then added 4 more nodes to the cluster and without making any changes to the Jetstress, virtual machine/s or the cluster configuration and the IOPS jumped by 2x!!

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

In summary adding the 4 storage only nodes:

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

As we can see from these results, adding storage only nodes can significantly increase the performance without any tuning. Had I tuned the Jetstress configuration, much higher performance and potentially lower read/write latency could have also been achieved.

In short, adding storage only nodes is a quick win for performance with the added advantage of increasing the resiliency and capacity of the cluster.

So we’ve now achieved much higher performance for our workload thanks to a combination of optimally configured VM, CVM and the addition of storage only nodes.

If at this stage you’re still not achieving the performance you require, you’re in the 1% where we may need to utilise Acropolis Block Service (ABS) to further improve performance.

  • Acropolis Block Services (ABS)

ABS was announced in 2016 to address the edge use cases as customers wanted to make Nutanix the standard platform for their datacenters, however they have not been able to realise this vision due to a number of reasons including:

  • The desire/requirement to re-use existing servers
  • Applications which are not virtual (for many reasons, mostly political)
  • Performance / Scalability of externally connected servers
  • Complexity including operational considerations of external iSCSI

For more detailed information about the release please review: What’s .NEXT 2016 – Acropolis Block Services (ABS)

ABS works by using In-guest iSCSI to present vDisks direct to the Guest OS. The vDisks are then automatically load balanced across the entire Nutanix cluster to provide optimal performance.

The below tweet answers the FAQ around how distributed is the workload when using ABS. As we see below, a 4 node cluster uses 4 paths and when the cluster is expanded to 8 nodes ABS automatically (and almost instantly) expands to use 8 paths (or CVMs).

The downside of ABS is the loss of data locality, but if we can’t have data locality, the next best thing is a highly scalable, resilient and dynamic distributed storage fabric.

ABS can scale performance in a linear manner which is only limited by the network bandwidth and number of nodes to drive the IO, so a physical server with say 100GB NICs and a cluster of 32 nodes would produce ridiculous levels of performance in the multi-millions of IOPS range.

The In-guest iSCSI setup is also very simple, just set the iSCSI Target as the Nutanix Cluster IP and the load balancing is dynamically calculated, when the cluster size increases, the vDisks are automatically balanced across the new nodes without user intervention, the same is true for node removals, maintenance, upgrades, failures etc. Everything is managed automatically so ABS is a very simple iSCSI implementation for admins.

Summary:

Nutanix provides excellent scalability for Virtual Machines and provides ABS for niche workloads which may require more performance than a single node can offer.

Up next, Part 4 where we cover the latest and most exciting development for scaling storage Performance for Monster VMs.

Back to the Scalability, Resiliency and Performance Index.

vSphere | PVSCSI Adapters & striped/spanned NTFS volumes

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A little while ago I wrote a post titled “Splitting SQL datafiles across multiple VMDKs for optimal VM performance” where I talked about how SQL databases can be split with minimal/no interruption to production to give better performance by spreading the IO load across multiple PVSCSI adapters and virtual machine disks (VMDKs).

In a follow up post titled “SQL & Exchange performance in a Virtual Machine” I mentioned the above article and concluded:

If the DBA is not confident doing this, you can also just add multiple virtual disks (connected via multiple PVSCSI controllers) and create a stripe in guest (via Disk Manager) and this will also give you the benefit of multiple vdisks.

Both posts have been very popular and one of the comments I got via twitter was that creating striped or spanned NTFS volumes in guest was not supported by VMware when using PVSCSI.

This is stated in VMware KB “Configuring disks to use VMware Paravirtual SCSI (PVSCSI) adapters (1010398)” as shown below:

kbspanned

Prior to writing both posts I was aware of this KB, but after comprehensively testing this numerous times on different platforms over the years, and more recently on Nutanix, I concluded after liaising with many VMware experts (including several VCDXs) that this was either a legacy recommendation which needed to be updated, or simply a mistake by the author of the KB (which can happen as we’re all human).

As such, I followed up with VMware by raising a SR on August 14th 2016.

After following up several times I had given up waiting for an answer but I am pleased to say today (2nd November 2016) I finally got a reply.

vmwaregsspvscsi

In summary, spanned (and stripped volumes which was not mentioned in the KB) are supported and to quote VMware GSS “will have no issues”.

One strong recommendation I have is DO NOT use VMDKs hosted in different failure domains (e.g.: LUNs, SAN/NASs) in the one spanned/striped volume as this increases the size of the failure domain and your chances of the volume going offline.

So there you have it, if you need to increase the performance for an application and you are not confident to split databases at the application level, you can (typically) get increased IO performance by using striped volumes in guest which are quick and easy to setup. The only downside is you will need to take your DB offline to copy it to the new volume before bringing it back online.

Hope this puts peoples mind at ease about striped volumes with PVSCSI.

Splitting SQL datafiles across multiple VMDKs for optimal VM performance

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After recently helping multiple customers resolve performance issues with vBCA workloads by configuring multiple PVSCSI adapters and spreading workloads across multiple VMDKs, I wrote: SQL and Exchange performance in a virtual machine.

The post talked about how you should use multiple PVSCSI adapters with multiple VMDKs spread evenly across the adapters to achieve optimal performance and reduce overheads.

But what about if you only have a single SQL database. Can we split it across multiple VMDKs and importantly, can we do this without downtime?

The answer to both, thankfully is Yes!

The below is an example of a worst case scenario for a SQL server database. A single VMDK (using a single SCSI controller) hosting the Operating System, Database and Logs, especially when it’s a business critical application.

In the above scenario the single virtual SCSI controller and/or the single VMDK could both result in lower than expected performance.

We have learned earlier that using multiple PVSCSI adapters and VMDKs is the best way to deploy a high performance solution. The below is an example deployment where the OS , Pagefile and SQL binaries are using one virtual controller and VMDK, then four VMDKs for database files are hosted by a further two PVSCSI controllers and the logs are hosted by a fourth PVSCSI controller and VMDK.

In the above diagram the C:\ is using a LSI Logic controller which in most cases does not constraint performance, however since it’s very easy to change to a PVSCSI controller and there are no significant downsides, I recommend standardizing on PVSCSI.

Now if we look at our current database, we can see it has one database file and one log file as shown below.

The first step is the update the Virtual machines disk layout as describe in the aforementioned article which should end up looking like the below:

Next we go into Disk manager to rescan for the new storage devices, mark the drives are online, then format them with a 64k Allocation size which is optimal for databases. Once this is done you should check My Computer and see something similar to the below:

Next I recommend creating a directory for the database and log files rather than using the root directory so each drive should have a new folder as per the example below.

Next step is to create the new database files on each of new drives as shown below.

If the size of the original database is for example 10GB with say 2GB free space and you plan to split the database across 4 drives, then each of the new databases should be sized at no more than 2GB each to begin with. This prepares us to shrink the original DB and helps ensure the data is evenly spread across the new database files.

In the above screenshot, we can see the databases are limited to 2000MB, this is on purpose as we don’t want the database files expanding which can result in an uneven spread of data during the redistribution process I will cover later.

Switch the Recovery mode of Database to SIMPLE

Now go to the database, navigate to Tasks, Shrink and select “Files”

Now select the “Empty File by migrating data to other files in the same filegroup” option and press “Ok”.

Depending on the size of the database and the speed of the storage this may take some time and it will have at least some impact on the performance of the server. As such I recommend performing the process outside of peak hours if possible.

The error below is expected as we do not want to empty out the first *.mdf file completely. This is also an indication of our tasks being complete for empty file operation to the limit we’ve set earlier.

Once the task has completed you should see a roughly even distribution of data across the four database files by using the script below in query window.

USE tpcc
GO
SELECT DB_NAME() AS DbName,
name AS FileName,
size/128.0 AS CurrentSizeMB,
size/128.0 - CAST(FILEPROPERTY(name, 'SpaceUsed') 
AS INT)/128.0 AS FreeSpaceMB
FROM sys.database_files;

C:\Users\Kasim\AppData\Local\Temp\SNAGHTMLd751ece.PNG

Next we want to configure autogrow onto our databases so they can grow during business as usual operations.

The above shows the database are configured to autogrow by 100MB up to a limit of 2048MB each. The amount a database should autogrow will vary based on the rate of growth in your database, as will the file size limit so consider these values carefully.

Once you have set these settings it’s now time to shrink the original final to the same size as the other database files as shown below:

This process cleans up white space (empty space) within the database.

So far we have achieved the following:

  1. Updated the VM with additional PVSCSI controllers and more VMDKs
  2. Initialized the VMDKs and formatted to the Guest OS
  3. Created three new database files
  4. Balanced the database across the four database file (including the original file)

We have achieved all of this without taking the database offline.

At this stage the virtual machine and SQL can be left as is until such time as you can schedule a short maintenance window to perform the following:

  1. Copy the original DB file from C: to the remaining new database VMDK
  2. Copy the original Logs file from C: to the new logs VMDK

This process only takes a few minutes plus the time to copy the database and logs. The duration of the file copy will depend on the size of your database and the performance of the underlying storage. The good news is with the virtual machine having already been partially optimized with more PVSCSI controllers and VMDKs, the read (copy) process will be served by one SCSI controller/VMDK and the paste (write) process served by another which will minimize the downtime required.

Once you have locked in your maintenance window, all you need to do is ensure all users and applications dependent on the database are shutdown, then detach the database and select the “Drop Connections” and “Update Statistics” and press Ok.


The next steps are very simple; we need to copy (or rather move/cut) the database from the original location as shown below:

Now we paste the database file to the new data1 drive.

Then we copy the log file and paste it into the new log drive.

Now we simply reattach the database specifying the new location of the *.mdf file. You will note the message highlighted below which indicates the log files are not found which is expected since we have just relocated them.

C:\Users\Kasim\AppData\Local\Temp\SNAGHTMLd8094b4.PNG

To resolve this simply update the path to the logs file as shown below and press Ok.

And we’re done! Simple as that.

Adjust the maximum growth of the datafile to an appropriate size. If you set to unlimited, please ensure that you monitor the volumes and manage them according to the growth rate of the database.

Lastly, don’t forget to change the database recovery model to Full

Now you have your OS separated from your SQL database and logs and all of the drives are configured across four virtual SCSI controllers.

Summary:

If you have an existing SQL server and storage performance is considered a problem, before buying new storage (Nutanix or otherwise), ensure you optimize the virtual machines storage layout as the constraint may not be the underlying storage.

As this post explains, most of this optimization can be done without taking the database offline so you don’t really have anything lose in following this process. Worst case scenario is performance does not improve and you have eliminated the VM storage as the constraining factor and when you do implement new Nutanix nodes or any underlying storage, you will get the most out of it. Do follow some other best practices like RAM to vCPU balancing, SQL Memory optimization, Trace Flags and database compression, be it row or page.

Acknowledgements:

A huge thank you to Kasim Hansia from the Nutanix Business Critical Applications (vBCA) team for documenting this process and allowing me to publish this post using his screenshots. It’s a pleasure working with such a talented group at Nutanix both in the vBCA team and in the broader organization.

Related Articles:

  1. SQL and Exchange performance in a virtual machine
  2. How to successfully virtualize Microsoft Exchange
  3. MS support for SQL on NFS datastores