How to speed up your X-ray benchmark development cycle by re-using/re-cycling benchmark VMs and more importantly data-sets.
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End to End Creation of a Nutanix Cluster on AWS and Running X-Ray
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A series of videos showing how to install, run, modify and analyze HCI clusters with the Nutanix X-ray tool
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How can database density be measured? 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…
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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….
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Do database workloads benefit from data locality?
Read More →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…
Read More →One of the nice things about using public cloud is the ability to use pre-canned application virtual appliances created by companies like Bitnami. We can use these same appliance images on Nutanix AHV to easily do a Postgres database benchmark Step 1. Get the bitnami image wget https://bitnami.com/redirect/to/587231/bitnami-postgresql-11.3-0-r56-linux-debian-9-x86_64.zip Step 2. Unzip the file and convert…
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DB Compression
Read More →How to improve large DB read performance by 2X 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…
Read More →How to reduce database restore time by 50% 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…
Read More →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…
Read More →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…
Read More →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…
Read More →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…
Read More →What happens when power is lost to all nodes of a HCI Cluster? Ever wondered what happens when all power is simultaneously lost on a HCI cluster? One of the core principles of cloud design is that components are expected to fail, but the cluster as a whole should stay “up”. We wanted to…
Read More →Creating a HCI benchmark to simulate multi-tennent workloads HCI deployments are typically multi-tennant and often different nodes will support different types of workloads. It is very common to have large resource-hungry databases separated across nodes using anti-affinity rules. As with traditional storage, applications are writing to a shared storage environment which is necessary to…
Read More → A simple benchmark for Random Reads, Random Writes, Sequential Reads, Sequential Writes.
Read More → How to create a customized performance test using X-ray.
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