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.
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.Continue reading →
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.
A Nutanix cluster can persist a replicated write across two nodes in around 250 uSec which is critical for single-threaded DB write workloads. The performance compares very well with hosted cloud database instances using the same class of processor (db.r5.4xlarge in the figure below). The metrics below are for SQL insert transactions not the underlying IO.
Single threaded commit heavy insert rates. Latency as seen from SQL insert statement.
Often in my lab I want to shutdown a large number of VMs quickly. In the example below I submit the power-off command for a maximum of 50 VMs in parallel. Be aware that we’re using the command line, and in line with true Unix philosophy the OS will assume we know what we are doing and obey us completely and immediately. In other words pasting the below commands to your CVM will immediately shutdown all powered on VMs.
for i in $(acli vm.list power_state=on | awk '{ print $(NF) }' |tail -50); do acli vm.off $i & done
AOS 6.1 greatly improved database performance on Nutanix especially when the guest VM uses just a single disk for all the database files. The underlying change is known as vdisk sharding. Basically it allows the Nutanix CVM to scale up the number of threads used to service a single virtual disk under heavy load.
In this example we use the KVM cloud image from the Canonical Ubuntu image repository. More information on Ubuntu cloud images is on the canonical cloud image page. More detail on the cloud image boot process and cloud-init here: Ubuntu UEC/Imanges.
We can use the Ubuntu cloud image catalog, and specifically use one that has been built to run on KVM. Since AHV is based on KVM/QEMU Nutanix can use that image format directly without any further conversion.
Using a cloud image can be a quicker way to stand up a particular version of Linux without having to go through the Linux installation process (choosing usernames, keyboard types, timezones etc.). However, you will need to pass in a public key so that you can login to the instance once it has booted.
Full disclosure. I have worked for Nutanix in the performance engineering group since 2013. My opinions are likely biased, but that also gives me a decent amount of context when it comes to the performance of Nutanix storage over time. We already have a lot of customers running database workloads on Nutanix. But what about those high-performance databases still running on traditional storage?
I dug out a chart that I presented at .Next in 2017 and added to it the performance of a modern platform (AOS 6.0 and an NVME+SSD platform). For this random read microbenchmark performance has more than doubled. If you took a look at a HCI system even a few years back and decided that performance wasn’t where you needed it – there’s a good chance that the HW+SW systems shipping today could meet your needs.
With help from the Nutanix X-Ray team I have created an IO “benchmark” which simulates a “General Server Virtualization” workload. I call it the “Mixed Workload Simulator“
Many storage performance testers are familiar with vdbench, and wish to use it to test Hyper-Converged (HCI) performance. To accurately performance test HCI you need to deploy workloads on all HCI nodes. However, deploying multiple VMs and coordinating vdbench can be tricky, so with X-ray we provide an easy way to run vdbench at scale. Here’s how to do it.
Following on from the previous [1] [2] experiments with Postgres & pgbench. A quick look at how the workload is seen from the Nutanix CVM.
The Linux VM running postgres has two virtual disks:
One is taking transaction log writes.
The other is doing reads and writes from the main datafiles.
Since the database size is small (50% the size of the Linux RAM) – the data is mostly cached inside the guest, and so most reads do not hit storage. As a result we only see writes going to the DB files.
Additionally, we see that database datafile writes the arrive in a bursty fashion, and that these write bursts are more intense (~10x) than the log file writes.
Charts from Prometheus/Grafana showing IO rates seen from the perspective of the Linux guest VM
Despite the database flushes ocurring in bursts with a decent amount of concurrency the Nutanix CVM provides an average of 1.5ms write response time.
From the Nutanix CVM port 2009 handler, we can access the individual vdisk statistics. In this particular case vDisk 45269 is the data file disk, and 40043 is the database transaction log disk.
Datafile writes completed in 1.5millisecond average – despite deep queues during burst
The vdisk categorizer correctly identifies the database datafile write pattern as highly random.
Writes to the datbase datafiles are almost entirely random
As a result, the writes are passed into the replicated oplog
The burst of writes hits the oplog as expected
Meanwhile the log writes are categorized as mostly sequential, which is expected for a database log file workload.
Meanwhile, log file writes are mostly categorized as sequential.
Even though the log writes are sequential, they are low-concurrency and small size (looks like mostly 16K-32K). This write pattern is also a good candidate for oplog.
Once connected to the appliance we can use postgres and pgbench to generate simplistic database workload.
[1] Do this on a Linux box somewhere. For some reason the conversion failed using my qemu utilities installed via brew. Importing OVAs direct into AHV should be available in the future.
