This blog contains experience gained over the years of implementing (and de-implementing) large scale IT applications/software.

Recreating SAP ASE Database I/O Workload using Fio on Azure

After you have deployed SAP databases onto Azure hosted virtual machines, you may find that sometimes you don’t get the performance you were expecting.

 

How can this be? It’s guaranteed isn’t it?
Well, the answer is, as with everything, sometimes it just doesn’t work that way and there are a large number of factors involved.
Even the Microsoft consultants I’ve spoken with have a check point for customers to confirm at the VM level, that they are seeing the IOPS that they are expecting to see.
Especially when deploying high performance applications such as SAP HANA in Azure.
I can’t comment on the reasons why performance may not be as expected, although I do have my own theories.

Let’s look at how we can simply simulate an SAP ASE 16.0 SP03 database I/O operation, so that we can run a reasonably representative and repetitive test, without the need for ASE to even be installed.
Remember, your specific workload could be different due to the design of your database, type and size of transactions and other factors.
What I’m really trying to show here, is how you can use an approximation to provide a simple test that is repetitive and doesn’t need ASE installing.

Microsoft have their own page dedicated to running I/O tests in Azure, and they document the use of the Fio tool for this process.
Read further detail about Fio here: https://docs.microsoft.com/en-gb/azure/virtual-machines/linux/disks-benchmarks

Since you may need to show your I/O results to your local Microsoft representative, I would recommend you use the tool that Microsoft are familiar with, and not some other tool. This should help speed up any fault resolution process.

NOTE: The IOPS will not hit the maximum achievable, because in our test, the page/block size is too high for this. Microsoft’s quoted Azure disk values are achievable only with random read, 8KB page sizes, multiple threads/jobs and a queue depth of 256 (see here: https://docs.microsoft.com/en-gb/azure/virtual-machines/linux/disks-benchmarks).

In SAP ASE 16.0 SP03 (this is the version I had to hand) on a SUSE Linux 12.3 server, imagine we run a SQL operation like “SELECT * FROM MYTABLE WHERE COL2=’X'” which in our example causes an execution path that performs a table scan of the table MYTABLE.
The table scan results in an asynchronous sequential read of the single database data file (data device) on the VM disk which is an LVM logical volume striped over 3 physical disks that make up the one volume group.

We are going to assume that you have saptune installed and configured correctly for SAP ASE, so we will not touch on the Linux configuration.
One thing to note, is that our assumption includes that the Linux file system hosting the database devices is configured to permit direct I/O (avoiding the Linux filesystem cache). This helps with the test configuration setup.

SAP ASE will try and optimise our SQL operation if ASE has been configured correctly, and use a read-ahead algorithm with large I/O pages up-to 128KB. But even with the right ASE configuration, the use of 128KB pages is is not always possible, for example if the table is in some ways fragmented.
As part of our testing we will assume that 128KB pages are not being used. We will instead use 16KB, which is the smallest page size in ASE (worst case scenario).
We will also assume that our SQL statement results in exactly 1GB of data to be read from the disk each time.
This is highly unlikely in a tuned ASE instance, due to the database datacache. However, we will assume this instance is not tuned and under slight load, causing the datacache to have re-used the memory pages between tests.

If we look at the help page for the Fio tool, it’s a fairly hefty read.
Let’s start by translating some of the notations used to something we can appreciate with regards to our test scenario:

Fio Config Item            Our Test Values/Setup
I/O type                    = sequential read
Blocksize                 = 16KB
I/O size                    = 1024m (amount of data)
I/O engine               = asynch I/O – direct (unbuffered)
I/O depth                 = 2048 (disk queue depth)
Target file/device    = /sybase/AS1/sapdata/AS1_data_001.dat
Threads/processes/jobs = 1

We can see that from the list above, the queue depth is the only thing that we are not sure on.
The actual values can be determined by querying the Linux disk devices but in essence what this is doing is asking for a value that represents how much I/O can be queued for a specific disk device.
In checking my setup, I can see that I have 2048 defined on SLES 12 SP3.
More information on queue depth in Azure can be found here: https://docs.microsoft.com/en-us/azure/virtual-machines/windows/premium-storage-performance#queue-depth

On SLES you can check the queue depth using the lsscsi command with the Long, Long, Long format (-lll):

lsscsi -lll

 

[5:0:0:4] disk Msft Virtual Disk 1.0 /dev/sdd
device_blocked=0
iocounterbits=32
iodone_cnt=0x2053eea
ioerr_cnt=0x0
iorequest_cnt=0x2053eea
queue_depth=2048
queue_type=simple
scsi_level=6
state=running
timeout=300
type=0

An alternative way to check is to output the content of the /proc/scsi/sg/devices file and look at the values in the 7th column:

cat /proc/scsi/sg/devices

 

2 0 0 0 0 1 2048 1 1
3 0 1 0 0 1 2048 0 1
5 0 0 0 0 1 2048 0 1
5 0 0 4 0 1 2048 0 1
5 0 0 2 0 1 2048 0 1
5 0 0 1 0 1 2048 0 1
5 0 0 3 0 1 2048 0 1

For the target file (source file in our read test case), we can either use an existing data device file (if ASE is installed and database exists), or we could create a new data file containing zeros, of 1GB in size.

Using “dd” you can quickly create a 1GB file full of zeros:

dd if=/dev/zero of=/sybase/AS1/sapdata/AS1_data_001.dat bs=1024 count=1048576

 

1048576+0 records in
1048576+0 records out
1073741824 bytes (1.1 GB, 1.0 GiB) copied, 6.4592 s, 166 MB/s

We will be using only 1 job/thread in Fio to perform the I/O test.
Generally in ASE 16.0 SP03, the number of “disk tasks” is configured using “sp_configure” and visible in the configuration file.
The configured value is usually 1 in a default installation and vary rarely needs adjusting.

See here: https://help.sap.com/viewer/379424e5820941d0b14683dd3a992d5c/16.0.3.5/en-US/a778c8d8bc2b10149f11a28571f24818.html

Once we’re happy with the above settings, we just need to apply them to the Fio command line as follows:

fio –name=global –readonly –rw=read –direct=1 –bs=16k –size=1024m –iodepth=2048 –filename=/sybase/AS1/sapdata/AS1_data_001.dat –numjobs=1 –name=job1

You will see the output of Fio on the screen as it performs the I/O work.
In testing, the amount of clock time that Fio takes to perform the work is reflective of the performance of the I/O subsystem.
In extremely fast cases, you will need to look at the statistics that have been output to the screen.

The Microsoft documentation and examples show running very lengthy operations on Fio, to ensure that the disk caches are populated properly.
In my experience, I’ve never had the liberty to explain to the customer that they just need to do the same operation for 30 minutes, over and over and it will be much better. I prefer to run this test cold and see what I get as a possible worst-case.

job1: (g=0): rw=read, bs=(R) 16.0KiB-16.0KiB, (W) 16.0KiB-16.0KiB, (T) 16.0KiB-16.0KiB, ioengine=psync, iodepth=2048
fio-3.10
Starting 1 process
Jobs: 1 (f=1): [R(1)][100.0%][r=109MiB/s][r=6950 IOPS][eta 00m:00s]
job1: (groupid=0, jobs=1): err= 0: pid=87654: Tue Jan 14 06:36:01 2020
read: IOPS=6524, BW=102MiB/s (107MB/s)(1024MiB/10044msec)
clat (usec): min=49, max=12223, avg=148.22, stdev=228.29
lat (usec): min=49, max=12223, avg=148.81, stdev=228.39
clat percentiles (usec):
| 1.00th=[ 61], 5.00th=[ 67], 10.00th=[ 70], 20.00th=[ 75],
| 30.00th=[ 81], 40.00th=[ 88], 50.00th=[ 96], 60.00th=[ 108],
| 70.00th=[ 125], 80.00th=[ 159], 90.00th=[ 322], 95.00th=[ 412],
| 99.00th=[ 644], 99.50th=[ 848], 99.90th=[ 3097], 99.95th=[ 5145],
| 99.99th=[ 7963]
bw ( KiB/s): min=64576, max=131712, per=99.98%, avg=104379.00, stdev=21363.19, samples=20
iops : min= 4036, max= 8232, avg=6523.65, stdev=1335.24, samples=20
lat (usec) : 50=0.01%, 100=54.55%, 250=32.72%, 500=10.48%, 750=1.59%
lat (usec) : 1000=0.31%
lat (msec) : 2=0.20%, 4=0.07%, 10=0.07%, 20=0.01%
cpu : usr=6.25%, sys=20.35%, ctx=65541, majf=0, minf=13
IO depths : 1=100.0%, 2=0.0%, 4=0.0%, 8=0.0%, 16=0.0%, 32=0.0%, >=64=0.0%
submit : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
complete : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
issued rwts: total=65536,0,0,0 short=0,0,0,0 dropped=0,0,0,0
latency : target=0, window=0, percentile=100.00%, depth=2048

 

Run status group 0 (all jobs):
READ: bw=102MiB/s (107MB/s), 102MiB/s-102MiB/s (107MB/s-107MB/s), io=1024MiB (1074MB), run=10044-10044msec

Disk stats (read/write):
dm-8: ios=64233/2, merge=0/0, ticks=7416/8, in_queue=7436, util=74.54%, aggrios=21845/0, aggrmerge=0/0, aggrticks=2580/2, aggrin_queue=2581, aggrutil=25.78%
sdg: ios=21844/0, merge=0/0, ticks=2616/0, in_queue=2616, util=25.78%
sdh: ios=21844/1, merge=0/0, ticks=2600/4, in_queue=2600, util=25.63%
sdi: ios=21848/1, merge=0/0, ticks=2524/4, in_queue=2528, util=24.92%

The lines of significance to you, will be:

– Line: IOPS.

Shows the min, max and average IOPS that were obtained during the execution. This should roughly correspond to the IOPS expected for the type of Azure disk on which your source data file is located. Remember that if you have striped file system with RAID under a logical volume manager, then you should expect to see more IOPS because you have more disks.

NOTE: The IOPS will not hit the maximum achievable, because our page/block size is too high for this. The Azure disk values are achievable only with random read, 8KB page sizes, multiple threads/jobs and a queue depth of 256 (https://docs.microsoft.com/en-gb/azure/virtual-machines/linux/disks-benchmarks).

– Lines: “lat (usec)” and “lat (msec)”.

These are the proportions of latency in micro and milliseconds respectively.
If you have high percentages in the millisecond ranges, then you may have an issue. You would not expect this for the type of disks you would want to be running an SAP ASE database on.

In my example above, I am using 3x P40 Premium Storage SSD disks.
You can tell it is a striped logical volume setup, because the very last 3 lines of output shows my 3 Linux disk device names (sdg, sdh and sdi) which sit under my volume group.

You can use the useful links here to determine what you should be seeing on your setup:

NOTE: If you are running SAP on the ASE database, then you will more than likely be using Premium Storage (it’s the only option supported by SAP) and it will be Azure Managed (not un-managed).

Let’s look at the same Fio output using a 128KB page size (like ASE would if it was using large I/O).
We use the same command line but just change the “-bs” parameter to 128KB:

fio –name=global –readonly –rw=read –direct=1 –bs=128k –size=1024m –iodepth=2048 –filename=/sybase/AS1/sapdata/AS1_data_001.dat –numjobs=1 –name=job1

 

job1: (g=0): rw=read, bs=(R) 128KiB-128KiB, (W) 128KiB-128KiB, (T) 128KiB-128KiB, ioengine=psync, iodepth=2048
fio-3.10
Starting 1 process
Jobs: 1 (f=1): [R(1)][100.0%][r=128MiB/s][r=1021 IOPS][eta 00m:00s]
job1: (groupid=0, jobs=1): err= 0: pid=93539: Tue Jan 14 06:54:48 2020
read: IOPS=1025, BW=128MiB/s (134MB/s)(1024MiB/7987msec)
clat (usec): min=90, max=46843, avg=971.48, stdev=5784.85
lat (usec): min=91, max=46844, avg=972.04, stdev=5784.84
clat percentiles (usec):
| 1.00th=[ 101], 5.00th=[ 109], 10.00th=[ 113], 20.00th=[ 119],
| 30.00th=[ 124], 40.00th=[ 130], 50.00th=[ 137], 60.00th=[ 145],
| 70.00th=[ 157], 80.00th=[ 176], 90.00th=[ 210], 95.00th=[ 273],
| 99.00th=[42206], 99.50th=[42730], 99.90th=[43254], 99.95th=[43254],
| 99.99th=[46924]
bw ( KiB/s): min=130299, max=143616, per=100.00%, avg=131413.00, stdev=3376.53, samples=15
iops : min= 1017, max= 1122, avg=1026.60, stdev=26.40, samples=15
lat (usec) : 100=0.87%, 250=93.13%, 500=3.26%, 750=0.43%, 1000=0.13%
lat (msec) : 2=0.18%, 4=0.01%, 10=0.04%, 50=1.95%
cpu : usr=0.55%, sys=4.12%, ctx=8194, majf=0, minf=41
IO depths : 1=100.0%, 2=0.0%, 4=0.0%, 8=0.0%, 16=0.0%, 32=0.0%, >=64=0.0%
submit : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
complete : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
issued rwts: total=8192,0,0,0 short=0,0,0,0 dropped=0,0,0,0
latency : target=0, window=0, percentile=100.00%, depth=2048

Run status group 0 (all jobs):
READ: bw=128MiB/s (134MB/s), 128MiB/s-128MiB/s (134MB/s-134MB/s), io=1024MiB (1074MB), run=7987-7987msec

Disk stats (read/write):
dm-8: ios=8059/0, merge=0/0, ticks=7604/0, in_queue=7640, util=95.82%, aggrios=5461/0, aggrmerge=0/0, aggrticks=5114/0, aggrin_queue=5114, aggrutil=91.44%
sdg: ios=5461/0, merge=0/0, ticks=564/0, in_queue=564, util=6.96%
sdh: ios=5461/0, merge=0/0, ticks=7376/0, in_queue=7376, util=91.08%
sdi: ios=5462/0, merge=0/0, ticks=7404/0, in_queue=7404, util=91.44%

You can see that we actually got a lower IOPS value, but we returned all the data quicker and got a higher throughput.
This is due to the laws of how IOPS and throughput interact. A higher page/block size means we can potentially read more data in each I/O request.

Some of the performance randomness now becomes apparent, with the inconsistency of the “util” for each disk device. However, there is a note on the Fio webpage about how this metric (util) is not necessarily reliable.

You should note that, although we are doing a simulated direct I/O (unbuffered) operation at the Linux level, outside of Linux at the Azure level, there could be caching (data disk caching, which is actually cached on the underlying Azure physical host).

You can check your current setup directly in Azure or at the Linux level, by reading through my previous post on how to do this easily.

https://www.it-implementor.co.uk/2019/12/17/listing-azure-vm-datadisks-and-cache-settings-using-azure-portal-jmespath-bash/

Now for the final test.
Can we get the IOPS that we should be getting for our current setup and disks?

Following the Microsoft documentation to create the fioread.ini and execute (note it needs 120GB of disk space – 4 reader jobs x 30GB):

cat <<EOF > /tmp/fioread.ini
[global]
size=30g
direct=1
iodepth=256
ioengine=libaio
bs=8k

 

[reader1]
rw=randread
directory=/sybase/AS1/sapdata/

[reader2]
rw=randread
directory=/sybase/AS1/sapdata/

[reader3]
rw=randread
directory=/sybase/AS1/sapdata/

[reader4]
rw=randread
directory=/sybase/AS1/sapdata/
EOF

fio –runtime 30 /tmp/fioread.ini
reader1: (g=0): rw=randread, bs=(R) 8192B-8192B, (W) 8192B-8192B, (T) 8192B-8192B, ioengine=libaio, iodepth=256
reader2: (g=0): rw=randread, bs=(R) 8192B-8192B, (W) 8192B-8192B, (T) 8192B-8192B, ioengine=libaio, iodepth=256
reader3: (g=0): rw=randread, bs=(R) 8192B-8192B, (W) 8192B-8192B, (T) 8192B-8192B, ioengine=libaio, iodepth=256
reader4: (g=0): rw=randread, bs=(R) 8192B-8192B, (W) 8192B-8192B, (T) 8192B-8192B, ioengine=libaio, iodepth=256
fio-3.10
Starting 4 processes
reader1: Laying out IO file (1 file / 30720MiB)
reader2: Laying out IO file (1 file / 30720MiB)
reader3: Laying out IO file (1 file / 30720MiB)
reader4: Laying out IO file (1 file / 30720MiB)
Jobs: 4 (f=4): [r(4)][100.0%][r=128MiB/s][r=16.3k IOPS][eta 00m:00s]
reader1: (groupid=0, jobs=1): err= 0: pid=120284: Tue Jan 14 08:16:38 2020
read: IOPS=4250, BW=33.2MiB/s (34.8MB/s)(998MiB/30067msec)
slat (usec): min=3, max=7518, avg=10.06, stdev=43.39
clat (usec): min=180, max=156683, avg=60208.81, stdev=32909.11
lat (usec): min=196, max=156689, avg=60219.59, stdev=32908.61
clat percentiles (usec):
| 1.00th=[ 1549], 5.00th=[ 3294], 10.00th=[ 4883], 20.00th=[ 45351],
| 30.00th=[ 47973], 40.00th=[ 49021], 50.00th=[ 51643], 60.00th=[ 54789],
| 70.00th=[ 94897], 80.00th=[ 98042], 90.00th=[100140], 95.00th=[101188],
| 99.00th=[143655], 99.50th=[145753], 99.90th=[149947], 99.95th=[149947],
| 99.99th=[149947]
bw ( KiB/s): min=25168, max=46800, per=26.07%, avg=34003.88, stdev=4398.09, samples=60
iops : min= 3146, max= 5850, avg=4250.45, stdev=549.78, samples=60
lat (usec) : 250=0.01%, 500=0.02%, 750=0.12%, 1000=0.28%
lat (msec) : 2=1.35%, 4=5.69%, 10=5.72%, 20=1.15%, 50=30.21%
lat (msec) : 100=45.60%, 250=9.86%
cpu : usr=1.29%, sys=5.58%, ctx=6247, majf=0, minf=523
IO depths : 1=0.1%, 2=0.1%, 4=0.1%, 8=0.1%, 16=0.1%, 32=0.1%, >=64=100.0%
submit : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
complete : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.1%
issued rwts: total=127794,0,0,0 short=0,0,0,0 dropped=0,0,0,0
latency : target=0, window=0, percentile=100.00%, depth=256
reader2: (groupid=0, jobs=1): err= 0: pid=120285: Tue Jan 14 08:16:38 2020
read: IOPS=4183, BW=32.7MiB/s (34.3MB/s)(983MiB/30067msec)
slat (usec): min=3, max=8447, avg= 9.92, stdev=54.73
clat (usec): min=194, max=154937, avg=61163.27, stdev=32365.78
lat (usec): min=217, max=154945, avg=61173.85, stdev=32365.26
clat percentiles (usec):
| 1.00th=[ 1778], 5.00th=[ 3294], 10.00th=[ 5145], 20.00th=[ 46400],
| 30.00th=[ 47973], 40.00th=[ 49546], 50.00th=[ 52167], 60.00th=[ 55313],
| 70.00th=[ 94897], 80.00th=[ 98042], 90.00th=[100140], 95.00th=[101188],
| 99.00th=[111674], 99.50th=[145753], 99.90th=[147850], 99.95th=[149947],
| 99.99th=[149947]
bw ( KiB/s): min=26816, max=43104, per=25.67%, avg=33474.27, stdev=3881.96, samples=60
iops : min= 3352, max= 5388, avg=4184.27, stdev=485.26, samples=60
lat (usec) : 250=0.01%, 500=0.03%, 750=0.08%, 1000=0.15%
lat (msec) : 2=1.02%, 4=6.31%, 10=5.05%, 20=1.12%, 50=27.79%
lat (msec) : 100=49.09%, 250=9.37%
cpu : usr=1.14%, sys=5.53%, ctx=6362, majf=0, minf=522
IO depths : 1=0.1%, 2=0.1%, 4=0.1%, 8=0.1%, 16=0.1%, 32=0.1%, >=64=99.9%
submit : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
complete : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.1%
issued rwts: total=125800,0,0,0 short=0,0,0,0 dropped=0,0,0,0
latency : target=0, window=0, percentile=100.00%, depth=256
reader3: (groupid=0, jobs=1): err= 0: pid=120286: Tue Jan 14 08:16:38 2020
read: IOPS=3919, BW=30.6MiB/s (32.1MB/s)(921MiB/30066msec)
slat (usec): min=3, max=12886, avg= 9.40, stdev=56.68
clat (usec): min=276, max=151726, avg=65256.88, stdev=31578.48
lat (usec): min=283, max=151733, avg=65266.86, stdev=31578.73
clat percentiles (usec):
| 1.00th=[ 1958], 5.00th=[ 3884], 10.00th=[ 10421], 20.00th=[ 47449],
| 30.00th=[ 49021], 40.00th=[ 51119], 50.00th=[ 53740], 60.00th=[ 65274],
| 70.00th=[ 96994], 80.00th=[ 99091], 90.00th=[100140], 95.00th=[101188],
| 99.00th=[139461], 99.50th=[145753], 99.90th=[149947], 99.95th=[149947],
| 99.99th=[149947]
bw ( KiB/s): min=21344, max=42960, per=24.04%, avg=31354.32, stdev=5530.77, samples=60
iops : min= 2668, max= 5370, avg=3919.27, stdev=691.34, samples=60
lat (usec) : 500=0.01%, 750=0.05%, 1000=0.12%
lat (msec) : 2=0.92%, 4=4.15%, 10=4.59%, 20=0.59%, 50=25.92%
lat (msec) : 100=53.48%, 250=10.18%
cpu : usr=0.96%, sys=5.22%, ctx=7986, majf=0, minf=521
IO depths : 1=0.1%, 2=0.1%, 4=0.1%, 8=0.1%, 16=0.1%, 32=0.1%, >=64=99.9%
submit : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
complete : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.1%
issued rwts: total=117853,0,0,0 short=0,0,0,0 dropped=0,0,0,0
latency : target=0, window=0, percentile=100.00%, depth=256
reader4: (groupid=0, jobs=1): err= 0: pid=120287: Tue Jan 14 08:16:38 2020
read: IOPS=3955, BW=30.9MiB/s (32.4MB/s)(928MiB/30020msec)
slat (usec): min=3, max=9635, avg= 9.57, stdev=52.03
clat (usec): min=163, max=151463, avg=64699.59, stdev=32233.21
lat (usec): min=176, max=151468, avg=64709.90, stdev=32232.66
clat percentiles (usec):
| 1.00th=[ 1729], 5.00th=[ 3720], 10.00th=[ 7832], 20.00th=[ 46924],
| 30.00th=[ 48497], 40.00th=[ 51119], 50.00th=[ 53740], 60.00th=[ 87557],
| 70.00th=[ 96994], 80.00th=[ 99091], 90.00th=[100140], 95.00th=[102237],
| 99.00th=[109577], 99.50th=[143655], 99.90th=[147850], 99.95th=[147850],
| 99.99th=[147850]
bw ( KiB/s): min=21488, max=46320, per=24.22%, avg=31592.63, stdev=4760.10, samples=60
iops : min= 2686, max= 5790, avg=3949.05, stdev=595.03, samples=60
lat (usec) : 250=0.02%, 500=0.07%, 750=0.07%, 1000=0.09%
lat (msec) : 2=1.31%, 4=4.04%, 10=5.13%, 20=1.28%, 50=24.76%
lat (msec) : 100=52.89%, 250=10.35%
cpu : usr=1.06%, sys=5.21%, ctx=8226, majf=0, minf=522
IO depths : 1=0.1%, 2=0.1%, 4=0.1%, 8=0.1%, 16=0.1%, 32=0.1%, >=64=99.9%
submit : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
complete : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.1%
issued rwts: total=118743,0,0,0 short=0,0,0,0 dropped=0,0,0,0
latency : target=0, window=0, percentile=100.00%, depth=256

Run status group 0 (all jobs):
READ: bw=127MiB/s (134MB/s), 30.6MiB/s-33.2MiB/s (32.1MB/s-34.8MB/s), io=3830MiB (4016MB), run=30020-30067msec

Disk stats (read/write):
dm-8: ios=490190/1, merge=0/0, ticks=30440168/64, in_queue=30570784, util=99.79%, aggrios=163396/0, aggrmerge=0/0, aggrticks=10170760/21, aggrin_queue=10172817, aggrutil=99.60%
sdg: ios=162989/1, merge=0/0, ticks=10134108/64, in_queue=10135484, util=99.59%
sdh: ios=163379/0, merge=0/0, ticks=10175316/0, in_queue=10177440, util=99.60%
sdi: ios=163822/0, merge=0/0, ticks=10202856/0, in_queue=10205528, util=99.59%

throughput = [IOPS] * [block size]
example: 3000 IOPS * 8 (8KB) = 24000KB/s (24MB/s)

From our output, we can see how the IOPS and blocksize affect the throughput calculation:
16,300 (IOPS total) * 8 (8KB) = 130400KB/s (127MB/s)

Simple answer, no, we don’t get what we expect for our P40 disks. Further investigation required. 🙁


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