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.
How does database performance behave as more DBs are consolidated?
What impact does running the CVM have on available host resources?
tl;dr
The cluster was able to achieve ~90% of the theoretical maximum.
CVM overhead was 5% for this workload.
Experiment setup
The goal was to establish how database performance is affected as additional database workloads are added into the cluster. As a secondary metric, measure the overhead from running the virtual storage controller on the same host as the database servers themselves. We use the Postgres database with pgbench workload and measure the total transactions per second.
Cluster configuration
4 Node Nutanix cluster, with 2x Xeon CPU’s per host with 20 cores per socket.
Database configuration
Each database is identically configured with
Postgres 9.3
Ubuntu Linux
4 vCPU
8GB of memory
pgbench benchmark, running the “simple” query set.
The database is sized so that it fits entirely in memory. This is a test of CPU/Memory not IO.
Experiment steps.
The experiment starts with a single Database on a single host. We add more databases into the cluster until we reach 40 databases in total. At 40 databases with 4 vCPU each and a CPU bound workload we use all 160 CPU cores on the cluster.
The database is configured to fit into the host DRAM memory, and the benchmark runs as fast as it can – the benchmark is CPU bound.
Results
Below are the measured results from running 1-40 databases on the 4 node cluster.
Performance scales almost linearly from 4 to 160 CPU with no obvious bottlenecks before all of the CPU cores are saturated in the host at 40 databases.
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.
Nutanix AOS 5.10 ships with a feature called Autonomous Extent Store (AES). AES effectively provides Metadata Locality to complement the existing data locality that has always existed. For large datasets (e.g. a 10TB database with 20% hot data) we observe a 2X improvement in throughput for random access across the 2TB hot dataset.
In our experiment we deliberately size the active working-set to NOT fit into the metadata cache. We uniformly access 2TB with a 100% random access pattern and record the time to access all 2TB. On the same hardware with AES enabled – the time is cut in half. As can be seen in the chart – the throughput is double, as expected.
It is the localization of metadata from AES that contributes to the 2X improvement. AES keeps most of the metadata local to the node – so there is no need to fetch data across-the-wire. Additionally AES reduces the need to cache metadata in DRAM since local access is so fast. For very large datasets, retrieving metadata can contribute a large proportion of the access time. This is true for all storage, so speeding up metadata resolution can make a dramatic improvement to overall throughput as we demonstrate.
During .Next 2018 in London, Nutanix announced performance improvements in the core-datapath said to give up to 2X performance improvements. Here’s a real-world example of that improvement in practice.
I am using X-Ray to simulate a 1TB data restore into an existing database. Specifically the IO sizes are large, an even split of 64K,128K,256K, 1MB and the pattern is 100% random across the entire 1TB dataset.
bssplit=64k/20:128k/20:256k/20:512k/20:1m/20
Normally storage benchmarks using large IO sizes are performed serially, because it’s easier on the storage back-end. That may be realistic for an initial load, but in this case we want to simulate a restore where the pattern is 100% random.
In this case the time to ingest 1TB drops by half when using Nutanix AOS 5.10 with Autonomous Extent Store (AES) enabled Vs the previous traditional extent store.
This improvement is possible because with AES, inserting directly into the extent store is much faster.
For throughput sensitive, random workloads, AES can detect that it will be faster to skip the oplog. Skipping oplog allows AES to eliminate a network round trip to a remote oplog – and instead only make an RF2 copy for the Extent Store. By contrast, when sustained, large random IO is funneled into oplog, the 10Gbit network can become the bottleneck. Even with faster networks, AES will still be a benefit because the CPU and SSD resource usage is also lower. Unfortunately I only have 10Gbit networking in my lab!
The X-Ray files needed to run this test are on github
In a previous post I showed a chart which plots concurrency [X-axis] against throughput (IOPS) on the Y-Axis. Here is that plot again:
Experienced performance chart ogglers will notice the familiar pattern of Littles Law, whereby throughput (X) rises quickly as concurrency (N) is increased. As we follow the chart to the right, the slope flattens out and we achieve a lower increase in throughput, even as we increase concurrency by the same amount at each stage. The flattening of the curve is best understood as Amdahls Law.
Anyone who follows Dr. Neil Gunther and his Universal Scalability Law, will also recognize this curve.
The USL states that taking the values of concurrency and throughput as inputs, we can in fact calculate the scalability of the system. Specifically we are able to calculate the key factors of contention and crosstalk – which limit absolute linear scalability and eventually result in less throughput as additional load is submitted even as the capacity of the system is saturated.
Using his Excel spreadsheet, I was able to input the numbers from my test and derive values that determine scalability.
Taking the largest number (0.074%) the “contention value” (i.e the impact we expect due to Amdahls law) as the limit to linear scaling – we can say that for this particular cluster, running this particular (simplistic/synthetic) workload the Nutanix cluster scales 99.926% linear. Although I did not crank up the concurrency beyond 576, the model shows us that this cluster will start to degrade performance if we try to push concurrency beyond 600 or so. Again, the USL model is for this particular workload – on this particular cluster. Doubling the concurrency of the offered load to 1200 will only net us 500,000 IOPS according to the model.
The high linearity (99.926%) is expected. The workload is 100% read, and with the data-locality feature of Nutanix filesystem – we expect close to 100% scalability.
We will return to these measures of scalability in the future to look at more realistic workloads.
The fio Pareto parameter allows us to create a workload, which references a very large dataset, but specify a hotspot for the access pattern. Here’s an example using the same setup as the ILM experiment, but using a Pareto value of 0:8. My fio file looks like this..
[global]
ioengine=libaio
direct=1
time_based
norandommap
random_distribution=pareto:0.8 The experiment shows that with the access pattern as a Pareto ratio 0:8, meaning 20% of the overall dataset is “hot” the ILM process happens much faster as the hotspot is smaller, and is identified faster than a 100% uniform random access pattern. We would expect a similar shape for any sort of caching mechanism.
At some point potential Hyper-converged infrastructure (HCI) users want to know – “How fast does this thing go?”. The real question is “how do we measure that?”.
The simplest test is to run a single VM, with a single disk and issue a single IO at a time. We see often see this sort of test in bake-offs, and such a test does answer an important question – “what’s the lowest possible response time I can expect from the storage”.
However, this test only gives a single data point. Since nobody purchases a HCI cluster to run a single VM, we also need to know what happens when multiple VMs are run at the same time. This is a much more difficult test to conduct, and many end-users lack access and experience with tools that can give the full picture.
In the example below, the single VM, single vdisk, single IO result is at the very far left of the chart. Since it’s impossible to read I will tell you that the result is about 2,500 IOPS at ~400 microseconds. (in fact we know that if the IOPS are 2,500 the response time MUST be 400 microseconds 1/2,500 == .0004 seconds)
However with a single VM, the cluster is mostly idle, and has capacity to do much more work. In this X-Ray test I add another worker VM doing the exact same workload pattern to every node in the cluster every 5 minutes.
By the time we reach the end of the test, the total IOPS have increased to around 600,000 and the response time only increased by an additional 400 microseconds.
In other words the cluster was able to achieve 240X the amount of work measured by the single VM on a single node with only a 2X increase in response time, which is still less than 1ms.
The overall result is counter-intuitive, because the rate of change in IOPS (240X) is way out of line with the increase in response time (2X). The single VM test is using only a fraction of the cluster capacity.
When comparing HCI clusters to traditional storage arrays – you should expect the traditional array to outperform the cluster at the far left of the chart, but as work scales up the latent capacity of the HCI cluster is able to provide huge amounts of IO with very low response times.
You can run this test yourself by adding this custom workload to X-Ray
Specifically a customer wanted to see how performance changes (and how quickly) as data moves from HDD to SSD automatically as data is accessed. The access pattern is 100% random across the entire disk.
In a hybrid Flash/HDD system – “cold” data (i.e. data that has not been accessed for a long time) is moved from SSD to HDD when the SSD capacity is exhausted. At some point in the future – that same data may become “hot” again, and so we want to make sure that the “newly hot” data is quickly moved back to the SSD tier. The duration of the above chart is around 5 minutes – and we see that by, around the 3 minute mark the entire dataset is resident on the SSD tier.
This X-ray test uses a couple of neat tricks to demonstrate ILM behavior.
Edit container preferences to send sequential data immediately to HDD
Overwriting data with NUL/Zero bytes frees the underlying data on Nutanix filesystem
To demonstrate ILM from HDD to SSD (ad ultimately into the DRAM cache on the CVM) we first have to ensure that we have data on the HDD in the first place. By default Nutanix OS will always try to write new data to SSD. To circumvent that behavior we can edit the container preferences. We use the fact that the “prefill” will be a sequential workload, while the measured workload will be a random workload. To make the change, use “ncli” to change the ” Sequential I/O Pri Order” to be HDD only.
In my case I happened to call my container “xray” since I didn’t want to change the default container. Now, when X-Ray executes the prefill stage, the data will land on HDD. As a second requirement, we want to see what happens when IO with different size blocks are issued so that we can get a chart similar to this: To achieve the desired behavior, we need to make sure that, at the beginning of each test, the data, again resides on HDD. The problem is that the data is up-migrated during the test. To do this we do an initial overwrite of the entire disk with “NULL” bytes using a parameter in fio “zero_buffers”. This causes the data to be freed on the Nutanix filesystem. Then we issue a normal profile with random data. Once the data is freed, then we know that the new initial writes will go to HDD – because we edited the container to do so. The overall test pattern looks like this
Create and clone VMs
Prefill with random data (Data will reside on HDD due to container edit)
Read disk with 16KB block size
Zero out the disks – to remove/free the up-migrated data
