SATA on Nutanix. Some experimental data.

The question of  why  Nutanix uses SATA drive comes up sometimes, especially from customers who have experienced very poor performance using SATA on traditional arrays.

I can understand this anxiety.  In my time at NetApp we exclusively used SAS or FC-AL drives in performance test work.  At the time there was a huge difference in performance between SCSI and SATA.  Even a few short years ago, FC typically spun at 15K RPM whereas SATA was stuck at about a 5K RPM, so experiencing 3X the rotational delay.

These days SAS and SATA are both available in 7200 RPM configurations, and these are the type we use in standard Nutanix nodes.  In fact the SATA drives that we use are marketed by Seagate as “Nearline SAS”  or NL-SAS.   Mainly to differentiate them from the consumer grade SATA drives that are found in cheap laptops.  There are hundreds of SAS Vs SATA articles on the web, so I won’t go over the theoretical/historical arguments.

SATA in Hybrid/Tiered Storage

In a Nutanix cluster the “heavy lifting” of IO is mainly done by the SSD’s – leaving the SATA drives to service the few remaining IO’s that miss the SSD tier.  Under moderate load, the SATA spindles do pretty well, and since the SATA  $/GB is only 60% of SAS.  SATA seems like a good choice for mostly-cold data.

Let’s Experiment.

From a performance perspective,  I decided to run a few experiments to see just how well SATA performs.  In the test, the  SATA drives are Nutanix standard drives “ST91000640NS” (Seagate, priced around $150).  The comparable SAS drives are the same form-factor (2.5 Inch)  “AL13SEB900” (Toshiba, priced at about $250 USD).  These drives spin at 10K RPM.  Both drives hold around 1TB.

There are three experiments per drive type to reveal the impact of seek-times.  This is achieved using the “filesize” parameter of fio – which determines the LBA range to read.  One thing to note, is that I use a queue-depth of one.  Therefore IOPs can be calculated as simply 1/Response-Time (converted to seconds).



Random Distribution. SATA Vs SAS

Working Set Size7.2K RPM SATA Response Time (ms)10K RPM SAS Response Time (ms)
1 GB5.54
100 GB7.54.5
1000 GB12.57

Zipf Distribution. SATA Only.

Working Set SizeResponse Time (ms)

Somewhat intuitively as the working-set (seek) gets larger, the difference between “Real SAS” and “NL-SAS/SATA” gets wider.  This is intuitive because with a 1GB working-set,  the seek-time is close to zero, and so only the rotational delay (based on RPM) is a factor.  In fact the difference in response time is the same as the difference in rotational speed (1:1.3).

Also  (just for fun) I used the “random_distribution=zipf” function in fio to test the response time when reading across the entire range of the disk – but with a “hotspot” (zipf) rather than a uniform random read – which is pretty unrealistic.

In the “realistic” case – reading across the entire disk on the SATA drives shipped with Nutanix nodes is capable of 8.5 ms response time at 125 IOPS per spindle.


The performance difference between SAS and SATA is often over-stated.  At moderate loads SATA performs well enough for most use-cases.  Even when delivering fully random IO over the entirety of the disk – SATA can deliver 8K in less than 15ms.  Using a more realistic (not 100% random) access pattern the response time is  < 10ms.

For a properly sized Nutanix implementation, the intent is to service most IO from Flash. It’s OK to generate some work on HDD from time-to-time even on SATA.

Impact of Paravirtual SCSI driver VS LSI Emulation with Data.

TL;DR  Comparison of Paravirtual SCSI Vs Emulated SCSI in with measurements.  PVSCSI gives measurably better response times at high load. 

During a performance debugging session, I noticed that the response time on two of the SCSI devices was much higher than the others (Linux host under vmware ESX).  The difference was unexpected since all the devices were part of the same stripe doing a uniform synthetic workload.

iostat output from the system under investigation.


The immediate observation is that queue length is higher, as is wait time.  All these devices reside on the same back-end storage so I am looking for something else.  When I traced back the devices it turned out that the “slow devices” were attached to LSI emulated controllers in ESX.  Whereas the “fast devices” are attached to para-virtual controllers.

I was surprised to see how much difference using para virtual (PV) SCSI drivers made to the guest response time once IOPS started to ramp up.  In these plots the y-axis is iostat “await” time.  The x-axis is time (each point is a 3 second average).

PVSCSI = Gey Dots
LSI Emulated SCSI = Red Dots
Lower is better.


Each plot is from a workload which  uses a different offered IO rate.  The offered rates are   8000,9000 and 10,000 the storage is able to meet the rates even though latency increases because there is a lot of outstanding IO.  The workload is mixed read/write with bursts.



After converting sdh and sdi to PV SCSI the response time is again uniform across all devices.


SuperScalin’: How I learned to stop worrying and love SQL Server on Nutanix.

TL;DR  It’s pretty easy to get 1M SQL TPM running a TPC-C like workload on a single Nutanix node.  Use 1 vDisk for Log files, and 6 vDisks for data files.  SQL Server  needs enough CPU and RAM to drive it.  I used 16 vCPU’s  and 64G of RAM.

Running database servers on Nutanix is an increasing trend and DBA’s are naturally skeptical about moving their DB’s to new platforms.  I recently had the chance to run some DB benchmarks on a couple of nodes in our lab.  My goal was to achieve 1M SQL transactions per node, and have that be linearly scalable across multiple nodes.

Screen Shot 2014-11-26 at 5.50.58 PM

It turned out to be ridiculously easy to generate decent numbers using SQL Server.  As a Unix and Oracle old-timer it was a shock to me, just how simple it is to throw up a SQL server instance.  In this experiment, I am using Windows Server 2012 and SQL-Server 2012.

For the test DB I provision 1 Disk for the SQL log files, and 6 disks for the data files.  Temp and the other system DB files are left unchanged.  Nothing is tuned or tweaked on the Nutanix side, everything is setup as per standard best practices – no “benchmark specials”.

SQL Server TPCC Scaling

Load is being generated by HammerDB configured to run the OLTP database workload.  I get a little over 1Million SQL transactions per minute (TPM) on a single Nutanix node.  The scaling is more-or-less linear, yielding 4.2 Million TPM  with 4 Nutanix nodes, which fit in a single 2U chassis . Each node is running both the DB itself, and the shared storage using NDFS.  I stopped at 6 nodes, because that’s all I had access to at the time.

The thing that blew me away in this was just how simple it had been.  Prior to using SQL server, I had been trying to set up Oracle to do the same workload.  It was a huge effort that took me back to the 1990’s, configuring kernel parameters by hand – just to stand up the DB.  I’ll come back to Oracle at a later date.

My SQL Server is configured with 16 vCPU’s and 64GB of RAM, so that the SQL server VM itself has as many resources as possible, so as not to be the bottleneck.

I use the following flags on SQL server.  In SQL terminology these are known as traceflags which are set in the SQL console (I used “DBCC trace status” to display the following.  These are fairly standard and are mentioned in our best practice guide.

Screen Shot 2014-11-30 at 8.38.45 PM

One thing I did change from the norm was to set the target recovery time to 240 seconds, rather than let SQL server determine the recovery time dynamically.  I found that in the benchmarking scenario, SQL server would not do any background flushing at all,  and then suddenly would checkpoint a huge amount of data which caused the TPM to fluctuate wildly.  With the recovery time hard coded to 240 seconds, the background page flusher keeps up with the incoming workload, and does not need to issue huge checkpoints.  My guess is that in real (non benchmark conditions) SQL server waits for the incoming work to drop-off and issues the checkpoint at that time.  Since my benchmark never backs off, SQL server eventually has to issue the checkpoint.

Screen Shot 2014-11-26 at 5.39.07 PM


Lord Kelvin Vs the IO blender

One of the characteristics of a  successful storage system for virtualized environments is that it must handle the IO blender.  Put simply, when lots of regular looking workloads are virtualized and presented to the storage, their regularity is lost, and the resulting IO stream starts to look more and more random.

 This is very similar to the way that synthesisers work – they take multiple regular sine waves of varying frequencies and add them together to get a much more complex sound.

That’s all pretty awesome for making cool space noises, but not so much when presented to the storage OS.  Without the ability to detect regularity, things like caching, pre-fetching and any kind of predictive algorithm break down.

That pre-fetch is never going to happen.

In Nutanix NOS we treat each of these sine waves (workloads) individually, never letting them get mixed together.  NDFS knows about vmdk’s or vhdx disks – and so by keeping the regular workloads separate we can still apply all the usual techniques to keep the bits flowing, even at high loads and disparate workload mixes that cause normal storage systems to fall over in a steaming heap of cache misses and metadata chaos.


Multiple devices/jobs in fio

If your underlying filesystem/devices have different response times (e.g. some devices are cached – or are on SSD) and others are on spinning disk, then the behavior of fio can be quite different depending on how the fio config file is specified.  Typically there are two approaches

1) Have a single “job” that has multiple devices

2) Make each device a “job”

With a single job, the iodepth parameter will be the total iodepth for the job (not per device) .  If multiple jobs are used (with one device per job) then the iodepth value is per device.

Option 1 (a single job) results in [roughly] equal IO across disks regardless of response time.  This is like  having a volume manager or RAID device, in that the overall oprate is limited by the slowest device.

For example, notice that even though the wait/response times are quite uneven (ranging from 0.8 ms to 1.5ms) the r/s rate is quite even.  You will notice though the that queue size is very variable so as to achieve similar throughput in the face of uneven response times.

Screen Shot 2014-06-17 at 5.34.55 PM

To get this sort of behavior use the following fio syntax. We allow fio to use up to 128 outstanding IO’s to distribute amongst the 8 “files” or in this case “devices”. In order to maintain the maximum throughput for the overall job, the devices with slower response times have more outstanding IO’s than the devices with faster response times.



The second option, give an uneven throughput because each device is linked to a separate job, and so is completely independent.  The  iodepth parameter is specific to each device, so every device has 16 outstanding IO’s.  The throughput (r/s) is directly tied to the response time of the specific device that it is working on.  So response times that are 10x faster generate throughput that is 10x faster.  For simulating real workloads this is probably not what you want.

For instance when sizing workingset and cache, the disks that have better throughput may dominate the cache.

Screen Shot 2014-06-17 at 5.32.52 PM



Here be Zeroes


Many storage devices/filesystems treat blocks containing nothing but zeros in a special way, often short-circuiting reads from the back-end.  This is normally a good thing but this behavior can cause odd results when benchmarking.  This typically comes up when testing against storage using raw devices that have been thin provisioned.

In this example, I have several disks attached to my linux virtual machine.  Some of these disks contain data, but some of them have never been written to.

When I run an fio test against the disks, we can clearly see that the response time is better for some than for others.  Here’s the fio output…

fio believes that it is issuing the same workload to all disks.
fio believes that it is issuing the same workload to all disks.

and here is the output of iostat -x

Screen Shot 2014-06-12 at 4.29.23 PM

The devices sdf, sdg and sdh are thin provisioned disks that have never been written to. The read response times are be much lower.  Even though the actual storage is identical.

There are a few ways to detect that the data being read is all zero’s.

Firstly use a simple tool like unix “od” or “hd”  to dump out a small section of the disk device and see what it contains.  In the example below I just take the first 1000 bytes and check to see if there is any data.

Screen Shot 2014-06-12 at 4.58.15 PM

Secondly, see if your storage/filesystem has a way to show that it read zeros from the device.  NDFS has a couple of ways of doing that.  The easiest is to look at the 2009:/latency page and look for the stage “FoundZeroes”.


If your storage is returning zeros and so making your benchmarking problematic, you will need to get some data onto the disks!  Normally I just do a large sequential write with whatever benchmarking tool that I am using.  Both IOmeter and fio will write “junk” to disks when writing.


Designing a scaleout storage platform.

I was speaking to one of our developers the other day, and he pointed me to the following paper:  SEDA: An Architecture for Well-Conditioned, Scalable Internet Services as an example of the general philosophy behind the design of the Nutanix Distributed File System (NDFS).

Although the paper uses examples of both a webserver and a gnutella client, the philosophies are relevant to a large scale distributed filesystem.  In the case of NDFS we are serving disk blocks to clients who happen to be virtual machines.  One trade-off that is true in both cases is that scalability is traded for low latency in the single-stream case.  However at load, the response time is generally better than a system that is designed to low-latency, and then attempted to scale-up to achive high throughput.

At Nutanix we often talk about web-scale architectures, and this paper gives a pretty solid idea of what that might mean in concrete terms.

FWIW., according to google scholar, the paper has been cited 937 times, including Cassandra which is how we store filesystem meta-data in a distributed fashion.