Understanding QEMU devices

Not sure where I came across this, but it is an excellent description of QEMU (and virtualization in general).  I am very much a fan of this style of technical communication as exemplified in this final summary paragraph (the full article is longer):

In summary, even though QEMU was first written as a way of emulating hardware memory maps in order to virtualize a guest OS, it turns out that the fastest virtualization also depends on virtual hardware: a memory map of registers with particular documented side effects that has no bare-metal counterpart. And at the end of the day, all virtualization really means is running a particular set of assembly instructions (the guest OS) to manipulate locations within a giant memory map for causing a particular set of side effects, where QEMU is just a user-space application providing a memory map and mimicking the same side effects you would get when executing those guest instructions on the appropriate bare metal hardware.


A Nutanix / Prometheus exporter in bash


For a fun afternoon project, how about a retro prometheus exporter using Apache/nginx, cgi-bin and bash!?

About prometheus format

A Prometheus exporter simply has to return a page with metric names and metric values in a particular format like below.

ntnx_bash{metric="cluster_read_iops"} 0
ntnx_bash{metric="cluster_write_iops"} 1

When you configure prometheus via prometheus.yml you’re telling prometheus to visit a particular IP:Port over HTTP and ask for a page called metrics – so if the “page” called metrics is a script – the script just has to return (print) out data in the expected format – and prometheus will accept that as a basic “exporter”. The idea here is to write a very simple exporter in bash that connects to a Nutanix cluster – hits the stats API and returns IOPS data for a given container in the correct format.

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Linux memory monitoring (allocations Vs usage)

How to use some of Linux’s standard tools and how different types of memory usage shows up.

Examples of using malloc and writing to memory with three use-cases for a simple process

In each case we run the example with a 64MB allocation so that we can see the usage from standard linux tools.

We do something like this

gary@linux:~/git/unixfun$ ./malloc_and_write 65536
Allocating 65536 KB
Allocating 67108864 bytes
The address of your memory is 0x7fa2829ff010
Hit <return> to exit
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Using iperf multi-stream may not work as expected

Running iperf with parallel threads

TL;DR – When running iperf with parallel threads/workers the -P option must be specified after the -c <target-IP> option. This is mentioned in the manpage but some options (-t for instance) work in any order, while others (specifically the -P for parallel threads) definitely does not, which is a bit confusing.

For example – these two invocations of iperf give very different results

  • iperf -P 5 -c (The -P before -c) -Yields 20.4 Gbits/sec
  • iperf -c -P 5 (The -P after the -c)- Yields 78.3 Gbits/sec
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mpstat has an option to show utilization per NUMA node

Not sure how long this has been a thing, but I recently discovered that mpstat takes a -N option for “NUMA Node” that works in the same way as -P for “Processor”. e.g. $ mpstat -N 0,1 1 will show stats for NUMA nodes 0 and 1 every 1 second. Just like mpstat -P ALL shows all processors mpstat -N ALL shows all NUMA nodes (and is easier to type).

The output looks like this

05:09:17 PM NODE    %usr   %nice    %sys %iowait    %irq   %soft  %steal  %guest  %gnice   %idle
05:09:18 PM 0 1.13 0.00 9.30 0.00 0.28 0.15 0.00 31.78 0.00 57.21
05:09:18 PM 1 0.40 0.00 8.03 0.00 0.28 1.05 0.00 31.34 0.00 58.78

Average: NODE %usr %nice %sys %iowait %irq %soft %steal %guest %gnice %idle
Average: 0 0.80 0.00 8.56 0.00 0.27 0.11 0.00 36.49 0.00 53.78
Average: 1 0.49 0.00 10.02 0.00 0.32 1.01 0.00 25.13 0.00 63.03

Using mpstat -N is is quite easy to check to see how the workload is distributed among the NUMA nodes of a multi-socket machine.

Running the ML-Perf Storage benchmark on Nutanix files.

Some technical notes on our submission to the benchmark committee.


For the past few months engineers from Nutanix have been participating in the MLPerftm Storage benchmark which is designed to measure the storage performance required for ML training workloads.  

We are pleased with how well our general-purpose file-server has delivered against the demands of this high throughput workload.  

Benchmark throughput and dataset
  • 125,000 files
  • 16.7 TB of data around 30% of the usable capacity (no “short stroking”)
  • Filesize 57-213MB per file
  • NFS v4 over Ethernet
  • 5GB/s delivered per compute node from single NFSv4 mountpoint
  • 25GB/s delivered across 5 compute nodes from single NFSv4 mountpoint

The dataset was 125,000 files consuming 16.7 TB, the file sizes ranged from 57 MB to 213 MB. ┬áThere is no temporal or spatial hotspot (meaning that the entire dataset is read) so there is no opportunity to cache the data in DRAM – the data is being accessed from NVMe flash media. For our benchmark submission we used standard NFSv4, standard Ethernet using the same Nutanix file-serving software that already powers everything from VDI users home-directories, medical images and more. No infiniband or special purpose parallel filesystems were harmed in this benchmark submission.

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Viewing Nutanix cluster metrics in prometheus/grafana

Using Nutanix API with prometheus push-gateway.

Many customers would like to view their cluster metrics alongside existing performance data using Prometheus/Grafana

Currently Nutanix does not provide a native exporter for Prometheus to use as a datasource. However we can use the prometheus push-gateway and a simple script which pulls from the native APIs to get data into prometheus. From there we can use Grafana or anything that can connect to Prometheus.

The goal is to be able to view cluster metrics alongside other Grafana dashboards. For example show the current Read/Write IOPS that the cluster is delivering on a per container basis. I’m hard-coding IPs and username/passwords in the script which obviously is not production grade, so don’t do that.

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Effects of CPU topology on sqlserver guests with AHV.

VM CPU Topology

The topology (layout) that AHV presents virtual Sockets/CPU to the guest operating system will usually be different than the physical topology. This is expected because we typically present a subset of all cores to the guest VMs.

Usually it is the total number of vCPU given to the VM that matters, not the specific topology, but in the case of SQLserver running an analytical workload (a TPC-H like workload from HammerDB) the topology passed to the VM does make a difference. Between 10% and 20% when measured by the total runtime.

[I think that the reason we see a difference here is that (a) the analytical workloads use hardly any storage bandwidth (I sized the database to fit in memory) and (b) there is probably a lot of cross-talk between the cores/memory as the DB engine issues parallel queries.]

At any rate we see that passing 20 cores as “20 sockets of 1 core” beats the performance of “1 socket with 20 cores” by a wide margin. The physical topology is two sockets of 20 cores on each socket. Thankfully the better performing option is the default.

CPU Topology may make a difference for SQL server running analytical workloads.
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fio versions < 3.3 may show inflated random write performance


If your storage system implements inline compression, performance results with small IO size random writes with time_based and runtime may be inflated with fio versions < 3.3 due to fio generating unexpectedly compressible data when using fio’s default data pattern. Although unintuitive, performance can often be increased by enabling compression especially if the bottleneck is on the storage media, replication or a combination of both.

fio 2.8.1 vs fio 3.33 data patterns

Therefore if you are comparing performance results generated using fio version < 3.3 and fio >=3.3 the random write performance on the same storage platform my appear reduced with more recent fio versions.

fio-3.3 was released in December 2017 but older fio versions are still in use particularly on distributions with long term (LTS) support. For instance Ubuntu 16, which is supported until 2026 ships with fio-2.2.10

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Specifying Drive letters with fio for Windows.

fio on Windows

Download pre-compiled fio binary for Windows

Example fio windows file, single drive

This will create a 1GB file called fiofile on the F:\ Drive in Windows then read the file.  Notice that the specification is “Driveletter” “Backslash” “Colon” “Filename”

In fio terms we are “escaping” the : which fio traditionally uses as a file separator.


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How to monitor SQLServer on Windows with Prometheus


  • Enable SQLServer agent in SSMS
  • Install the Prometheus Windows exporter from github the installer is in the Assets section near the bottom of the page
  • Install Prometheus scraper/database to your monitoring server/laptop via the appropriate installer
  • Point a browser to the prometheus server e.g. :9090
    • Add a new target, which will be the Windows exporter installed in step.
    • It will be something like <SQLSERVERIP>:9182/metrics
    • Ensure the Target shows “Green”
  • Check that we can scrape SQLserver tranactions. In the search/execute box enter something like this
  • Put the SQLserver under load with something like HammerDB
  • Hit Execute on the Prometheus server search box and you should see a transaction rate similar to HammerDB
  • Install Grafana and Point it to the Prometheus server (See multiple examples of how to do this)
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Generate load on Microsoft SQLserver Windows from HammerDB on Linux

HammerDB on Linux driving load to Windows SQL Server

Often it’s nice to be able to drive Windows applications and databases from Linux, especially if you are more comfortable in a Unix environment. This post will show you how to drive a Microsoft SQL Server database running on a Windows server from a remote Linux machine. In this example I am using Ubuntu 22.04, SQLserver 2019, Windows 11 and HammerDB 4.4

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QCOW 3 Ways

How to mount QCOW images as Linux block devices

  • guestmount (requires libguestfs-tools) sudo guestmount -d <vm-name> --ro -i <mountpoint>
  • qemu-nbd (requires the nbd driver)
    • Load the kernel module modprobe nbd max_part=8
    • Bind the device to the image qemu-nbd --connect=/dev/nbd0 <vmdiskimage.qcow>
    • Assuming partition #1 is the target mount /dev/ndb0p1 /a
  • loopback mount. Requires converting qcow to raw
    • Convert qcow to raw qemu-img convert vmdisk.qcow2 -f qcow2 -O raw vmdisk.raw
    • Create a loopback device losetup -f -P vmdisk.raw
    • Locate name of loopback device losetup -l | grep vmdisk.raw
    • Mount (assuming partition #1 on loopback device 99 mount /dev/mapper/loop99p1 /a
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Using cloud-init with AHV command line


  • Using cloud-init with AHV is conceptually identical to using KVM/QEMU- we need to use a few different tools with AHV
  • You will need a Linux image that is configured to use cloud-init. A good source is cloud-images.ubuntu.com
  • We will create a cloud-init textual file and create a mountable version using the cloud-localds tool on a Linux host
  • We will attach the cloud-init enabled ubuntu image and our cloud-init customization file to the VM at boot time
  • At boottime ubuntu will access the cloud-init data mounted as a CDROM and do the customization for us
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Comparing RDS and Nutanix Cluster performance with HammerDB


In a recent experiment using Amazon RDS instance and a VM running in an on-prem Nutanix cluster, both using Skylake class processors with similar clock speeds and vCPU count. The SQLServer database on Nutanix delivered almost 2X the transaction rate as the same workload running on Amazon RDS.

It turns out that migrating an existing SQLServer VM to RDS using the same vCPU count as on-prem may yield only half the expected performance for CPU heavy database workloads. The root cause is how Amazon thinks about vCPU compared to on-prem.

Benchmark Results

HammerDB results from RDS and Nutanix
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