Validation

Co-Author (s): Kerry Dolan

Introduction

This ESG Technical Validation documents the results of recent Nutanix performance testing focused on real-world performance scalability and sustainability improvements in support of mission- and business-critical application, database, analytics, and end-user computing (EUC) workloads.

Background

For business-critical applications and workloads, traditional infrastructure deployments are complex. Provisioning is often a slow, multi-step process that consumes days or weeks and involves multiple infrastructure teams. Lengthy upgrades require significant downtime, and it is difficult to keep up with patching across application instances. Creating and managing copies for multiple groups—test/dev, QA, and business intelligence—takes time and consumes costly space on storage arrays. Also, restore and recovery operations require hours or days of rolling back snapshots and log files across fragmented resources. Ultimately, the way application infrastructure is deployed can impact productivity, causing delays in time to value for organizations’ business activities. It’s no surprise that three-quarters of respondents (75%) to ESG’s annual technology spending intentions survey reveal that IT is more complex compared with two years ago (see Figure 1).¹

Hyperconverged technologies continue to replace legacy technology solutions, and organizations’ buying criteria have continued to expand. They’re looking past the original promise of simplicity and cost savings; organizations are also prioritizing requirements such as performance, scalability, and reliability—recognizing that technologies like the cloud and software-defined storage will be far less complex and more cost-effective than a traditional siloed approach. In another ESG research study, nearly half (46%) of respondents reported that they were using hyperconverged infrastructure (HCI) solutions, while 69% of respondents said they expect spending on hyperconverged technology to accelerate.² This is not surprising, given the factors driving them to consider HCI. Deployment drivers have historically included improved scalability, total cost of ownership, ease of deployment, and simplified systems management. Organizations need a solution that can deliver both simplicity and consistent, mixed workload performance for critical business workloads without the need to tweak and tune the environment.

Nutanix Cloud Platform

Nutanix is designed to deliver a complete, software-driven IT infrastructure stack with the agility, scalability, and simplicity of the cloud combined with the security, performance, and cost predictability of a traditional on-premises infrastructure. The architecture is a scale-out, fully distributed software platform leveraging web-scale engineering principles innovated by leading cloud companies such as Google, Facebook, and Amazon. The software integrates the compute, virtualization, and storage environments into a single solution. This integration eliminates the complexity of traditional SAN and NAS environments; costly, special-purpose hardware; and the specialized skill sets they require. Nutanix Cloud Platform with new Blockstore and Intel’s SPDK technology—combined with other technologies like Autonomous Extent Store (AES), which was introduced in a prior version of Nutanix Cloud Platform—capitalizes on its optimized architecture to accelerate performance. These innovations optimize for high-throughput and low-latency applications and are designed to fully leverage the benefits of new media such as NVMe and storage-class memory. Support for Intel Optane drives with tiering lets Nutanix Cloud Platform ensure that the hottest data is on the faster media, speeding performance with no application changes.

Nutanix continues to extend its solution to support more data-intensive workloads. A new data sharding architecture enables seamless migration of large scale-up databases to Nutanix Cloud Platform without requiring complex reconfiguration. Support for replication factor 1 (RF1) delivers enhanced TCO and performance for big data analytics applications that manage their own resiliency.

Nutanix delivers a comprehensive, unified storage platform for block, files, and objects to support any organization’s diverse data management requirements. Object storage has gained wider adoption as one of the best ways to rapidly store and retrieve continuously growing data sets that are often machine-generated and scale to petabytes. Nutanix Objects is an S3-compatible, software-defined, scale-out object storage solution. From an infrastructure perspective, this frees organizations to focus on highly performant tiers of compute and storage independently, decoupling cold or archived data. When data needs to be processed, it can be brought closer to compute resources on demand, with no impact to the user experience. SmartStore uses standard S3 APIs in Nutanix Objects to connect to the remote storage tier.

Nutanix also offers software-defined, hyperconverged infrastructure for databases that provides simplicity, agility, high availability, and efficiency. A key feature that makes Nutanix a simple and effective platform for databases is the software tool called Era, which helps customers with complete database lifecycle management of opensource RDBMSs, NoSQL, and In-Memory databases at the click of a button. Era enables you to deploy databases in minutes, configured with high-availability disaster recovery. It also provides simple, space-efficient copy creation; easy patching and upgrades; automatic refresh of copies; and simple rollback to any point in time with Nutanix Era Time Machine.

With built-in best practices, Era delivers a distinct advantage for databases running on Nutanix Cloud Platform when compared with the traditional setup and tuning that can take days or weeks of administrative effort. The simplicity enables non-DBAs to provision complex, multi-cluster databases with ease. Era fits in well with the promise of hyperconverged infrastructure, which was designed to simplify infrastructure deployments for applications.

ESG Technical Validation

ESG validated both ease of management and increased performance for Nutanix Cloud Platform. ESG validated ease of use with Nutanix Era and validated how Nutanix has improved performance for databases, big data analytics, and VDI for end-user computing, as well as performance and TCO for Nutanix Objects.

Databases with Era: Simplicity

ESG validated ease of use during a remote demonstration of Nutanix Era, with tests focused on relational databases (Oracle and Microsoft SQL). The single-pane dashboard view provides an overview of all database instances, including details of space savings, sources, clones by age, Time Machine snapshots, and alerts.

Provisioning Oracle and Microsoft SQL Databases

We provisioned the databases using four easy-to-navigate screens and several mouse clicks.

• First, we clicked on the menu Dashboard->Databases->Sources->Provision and selected the specific engine; we had the choice of selecting a single- or multi-instance database cluster.
• Next, we chose a Nutanix cluster on which to place the database. This can be local or at a remote location.
• Then, we selected the database version, followed by the compute profile (templated into small, medium, and large in terms of vCPUs, cores, etc.), network profile (vLAN), and public key for access.
• We gave the database a name and entered the database size and the database system password.
• There were fields available to insert pre- and post-commands if desired, such as for data masking and other custom pre- and post-processing of databases.
• Finally, we specified the Time Machine Gold policy, which was configured to save 30 days of continuous transaction logs, plus 30 daily, four weekly, 12 monthly, and four quarterly snapshots.
• The last step was to click Provision, and the task was triggered and completed in a few minutes—just a fraction of the time it would take to perform these steps manually.

Patch Management

Our demo also explored how easy patching was. It involved selecting a clone, clicking the Update Available message, choosing the upgrade from a list, and choosing to upgrade now or at a scheduled time. From the Operations screen, we could see the provisioning and patching task steps being executed, with time stamps. Patching operations is one of the most complex and risky operations DBAs performed on their database systems. Era made this task extremely simple and risk free.

Time Machine

The Time Machine feature provides snapshot restore by rolling back to any point in time, down to the second, by creating a clone from the snapshot. For a CRM database, we viewed the calendar of snapshots, color coded for continuous, daily, weekly, monthly, and quarterly snapshots. Restoring was simply a matter of selecting a date on the calendar, choosing either a daily snapshot or the hour/minute/second from which to restore, choosing the location on which to create the clone, and providing a name and database profile (small, medium, or large). Pre- and post-commands and the API Equivalent button were also available. Database recovery and provisioning copies of production databases are highly complex, frequent, time-consuming, and risky operations involving multiple teams and processes. Nutanix Era eliminated these risks and simplified the tasks down to basic mouse clicks.

RDBMS with Nutanix Cloud Platform: Performance

ESG audited detailed results from performance tests using a four-node Nutanix NX-8170-G7 cluster—populated with eight Intel DC P4510 Series 4TB NVMe devices per node—that examined both synthetic raw performance and realistic database workloads. The testing used the Nutanix tool to demonstrate raw performance capabilities of the platform and industry-standard database workload generation tools that exercised the Nutanix Cloud Platform using live SQL Server and Oracle databases. The workloads used for this report include:

• Raw Performance — This test generated random reads and random writes, with a goal of demonstrating peak burst performance.
• I/O Profile — 8KB random reads and writes, 1MB sequential reads and writes.
• SQL Server Performance —
• I/O Profile — Dell’s Benchmark Factory was used to generate an OLTP database workload that emulated users in a typical online brokerage firm as they generated trades, performed account inquiries, and executed market research. The workload was composed of multiple transaction types with a defined ratio of execution—some performed database updates, requiring both read and write operations, while others were read-only. The estimated read/write I/O ratio was 90% reads to 10% writes.
• o HammerDB is an open-source tool that can be used to run benchmarks across a wide range of database engines. The strong community behind it drives regular updates to the tool and its benchmarks. For this validation HammerDB 3.3 was used to drive a TPC-C-like benchmark, emulating a complex OLTP workload of multiple transaction types.
• Oracle I/O Performance —
• The Silly Little Oracle Benchmark (SLOB) was used to efficiently generate realistic system-wide, random, single-block, and application-independent SQL queries. The tool exercised all components of the server and storage subsystems by stressing the physical I/O layer of Oracle through SGA-buffered random I/O, without being limited to a specific load-generating application.

First, we tested the cluster’s raw IOPS performance, a common assessment of basic horsepower of the system, and compared results to testing performed in 2017. The system tested in 2017 was an all-flash Nutanix NX-3460-G5, four-node cluster running Nutanix Cloud Platform 5.0 with two Intel Xeon E5-2680v4 processors (14 cores at 2.4 GHz), 256GB RAM, and six 1.92TB SSDs per node. The 2021 Nutanix system under test was a four-node Nutanix NX-8170-G7 cluster running Nutanix Cloud Platform’s latest Acropolis Operating System (AOS) release, with two Intel Xeon 8280 processors (28 cores at 2.7GHz), 768GB RAM, and eight 4TB NVMe devices per node.

As shown in Figure 4, the 2021 Nutanix cluster delivered more than 1.1 million random read IOPS and almost 600K random write IOPS, which constitute a 5.95x performance improvement in random reads and a 5.7x improvement in random writes.

SQL Server Performance

Next, we compared SQL Server OLTP performance between the same two systems. The current tests used the latest software stack: Windows 2019, SQL Server 2019 CU6, and Benchmark Factory 8.3. Four agents were used to generate a total of 80 concurrent users per VM (totaling 320 cluster-wide users), so that all users interacted with the database as quickly as possible (no think time). Test runs were completed for each VM count (one to four) to highlight predictable performance scalability as the demanding OLTP workload exercised more resources in the cluster. It should be noted that IOPS and transactions/sec do not have a 1:1 correspondence. In most cases, a single transaction comprises multiple read and write I/O operations. Another important metric difference is latency. Storage latency is often associated with IOPS, while the transaction response time as reported in this analysis is specific to the OLTP workload, which exercises both compute and storage. As shown in Figure 5, ESG analyzed the average transactions/sec and average transaction response in seconds.

ESG reviewed data showing consistent performance scaling as the concurrent database instances increased from one to four, while average transaction response times remained low. The total number of transactions per second (TPS) averaged 7,370 per database instance, with the lowest-yielding SQL Server VM producing 7,328 TPS and the highest-yielding SQL Server VM producing 7,418 TPS.

This showed a twofold benefit: not only near-linear OLTP performance scalability with a variance of just 6% between all instances as more nodes were added, but also an even workload distribution that predictably consumed resources without impacting the other SQL Server instances. Also impressive was the average transaction response time. The Nutanix solution consistently delivered ultra-fast speeds of .011 seconds per transaction with all four nodes running the workload.

In addition to the SQL Server performance improvements between 2017 and 2021, the Nutanix solution improved between 2020 and 2021, including 10% higher transactions per section with 8% lower average response time, 36% lower average read latency, and 43% lower write latency.

SQL Server Read Performance with Disk Sharding

Nutanix has recently designed optimizations into its AOS, changing certain single-threaded operations to multi-threaded. These changes enable disk sharding that improves the read performance for workloads such as SQL Server that in common practice use a single vDisk. ESG reviewed three scenarios using the same test bed as the SQL OLTP results but substituting HammerDB version 3.3 as the test harness. HammerDB is a load test tool that simulates multiple database users executing transactional and analytic tasks. Tests were run three times, and results show the average in transactions per minute (TPM).

Figure 6 shows the difference in performance with and without Nutanix disk sharding, with workloads configured for a single vDisk for the database hosting either one or eight data files per vDisk and a single vDisk for SQL log files. The results with sharding indicate that optimization was in effect, while the results with no sharding had that enhancement turned off.

• With Nutanix’s improvements, the workload with a single vDisk hosting a single datafile supported 77% more transactions per minute. This indicates that “lift and shift” operations can enjoy a significant performance increase with sharding enabled.
• With sharding enabled, the single vDisk with eight data files supported 109% more TPMs.
• Sharding enables organizations to migrate database workloads from traditional three-tiered architectures without significantly re-architecting how databases consume storage.

Implementing Nutanix Best Practices for MS-SQL workloads with disk sharding delivered 907,844 TPM, which represents an additional 35% improvement in performance.

Oracle Performance Driven by SLOB

Next, ESG compared results of an insert/update/read workload driven by SLOB running on an Oracle database between a modern cluster of four NX-8170 nodes and an all-flash Nutanix NX-9460-G4 cluster tested in 2017. The NX-9460-G4 cluster contained dual Intel Haswell E5-2680v3 processors (12 cores at 2.5GHz), 256 GB of RAM, and six 1.6TB SSDs. Eight total VMs (running Red Hat Enterprise Linux [RHEL] 7.2 with six vCPUs and 32 GB of RAM) were configured with a single instance Oracle database. Each VM was given a 100GB vDisk for the operating system, a 100GB vDisk dedicated for the Oracle Cluster Registry (OCR), and 16 125GB vDisks for Oracle database data files and online redo logs. The NX-8170 four-node cluster ran an updated software stack: Oracle 19.3, Oracle Enterprise Linux 7.7, and SLOB 2.5.2.4.

Performance was recorded using Oracle Automatic Workload Repository (AWR) to provide the performance analysis from Oracle’s point of view, with Oracle’s data.

All Oracle test results show significant improvement in IOPS over the 2017 results, with near-linear scaling for reads and writes, while using only half the number of nodes and maintaining average read latency of less than one millisecond. Where the current configuration really shines is with two VMs per node. The 2021 eight-VM use case on four nodes demonstrated a 35% improvement in total IOPS compared to 2017, when the eight VMs were each on separate nodes, with write latency of only 1.2 ms. When running the benchmark with two VMs per node, the current configuration reduced write latency by 13%.

The 2021 results demonstrate significantly improved scaling. When we expanded from one VM per node to two VMs per node, IOPS increased by 77% while keeping read and write latencies extremely low. These results demonstrate that Nutanix has made significant improvements in its software stack that allow for increased density of database VMs per node.

Big Data Analytics with Nutanix Cloud Platform

The default Nutanix configuration includes a replication factor (RF) of at least two; replicating data to at least two failure domains ensures data resiliency in case of failure. The latest Nutanix AOS update includes an option to provision storage containers with RF1 for those workloads that can benefit from the reduced overhead and do not depend on the storage for resiliency. VMs leveraging RF1 containers must be pinned to a single node. This reduces storage capacity needs and costs, as well as improving performance by reducing the network load. ESG validated testing of both Hadoop/Cloudera and SAS Grid workloads using RF1.

Nutanix also delivers a durable, high performance, cost-effective object store that accelerates time to value for big-data analytics. Nutanix Objects delivers enhanced TCO for large scale big-data deployments such as Splunk SmartStore and also provides an excellent staging platform for data lakes.

Hadoop /Cloudera

The highly sequential nature of many Hadoop-based workloads means that RF1 can produce noticeable throughput performance improvements, including I/O bandwidth and, consequently, job completion times. In the test results shown below, multiple virtualized disks carved from RF1 storage were attached to VMs on a Nutanix Cluster, which consisted of eight NX-8155-G5 nodes with 20 core CPUs and 512GB RAM per node. Each VM consisted of 20vCPU and 384GB RAM. Testing used TeraSort, a commonly used Hadoop storage benchmark that measures the time to sort a 1TB data set of randomly generated data. The chart in Figure 8 shows 3x faster overall TeraSort job completion times when configured with RF1. These improvements continued as the data set sizes were increased up to a maximum of 9TB (3x total cluster memory).

SAS Grid

Nutanix RF1 storage also benefits the performance of the SASWORK file system in a base SAS (sas.com) or SAS Grid deployment. Because data placed in SASWORK is only useful for the duration of the SAS job, data protection is not necessary. The RF1 configuration increases performance for all SAS workloads on Nutanix by reducing network utilization of the cluster. In a test environment, the benefit of RF1 is most apparent when network bandwidth is the limiting factor.

The FIO benchmark with a sequential read and write I/O pattern was used to test the performance impact of RF1 compared to RF2 on Nutanix AOS. FIO was chosen as the benchmark because it has proven to be a match for I/O performance of SAS code running sequential file I/O on Nutanix AOS.

The Nutanix cluster configuration used for this test is a four-node NX-3460-G5 with 2.1Ghz E2695-v4 and 512GB RAM per node.

Figure 9 shows the results of a sequential write test using a Linux VM with an XFS file system. The test system had a single 10 GbE interface in each node with all SSD storage. The test was run in the same VM with two file systems, one configured as RF2 and the other as RF1. Testing demonstrated 2.8x faster performance using RF1.

Splunk SmartStore with Nutanix Objects

Next, ESG looked at Splunk workloads, comparing the economics of two scenarios. We compared Splunk “classic” on bare metal servers with hot and warm data hosted locally on SSDs in the compute nodes and cold data stored on external storage, with Splunk SmartStore on Nutanix, where hot data is stored on SSDs local to indexer nodes and Nutanix Objects are used for warm and cold data. Both deployments were configured and tested to ensure they conformed to Splunk requirements for latency and performance.³ FIO was executed with a range of block sizes relevant to the Splunk workload—60% 4k, 20% 8k, and 20% 32k. Splunk requires shared storage systems to be able to provide 1,200 IOPS for indexers and 800 IOPS for the search head. It’s important to note that this test was not designed to demonstrate the maximum performance of a Nutanix node or cluster; the purpose was to validate performance at or above required levels to run Splunk.

Figure 10 shows the results of performance testing with Nutanix. Nutanix showed near-linear scalability as the Splunk workloads were run on additional nodes in the cluster. In every case, Nutanix was able to exceed Splunk’s requirements for shared storage IOPS, throughput, and latency. In these tests, latency averaged 4.7 ms for sequential I/O and 5.5 ms for random I/O. Nutanix handily demonstrated suitability for running Splunk index and search workloads on the same host.

To compare the economics of Splunk on bare metal with Splunk SmartStore on Nutanix, we modeled and compared three systems sized to ingest 1TB, 3TB, and 10TB per day. Splunk sizing inputs were as follows: cache/hot and warm data retention – 30 days, SmartStore/cold data retention – 3 years, replication factor – 2, and searchability factor – 2.

The bare metal systems were based on a traditional server vendor’s certified and bundled offering for Splunk. Servers had two Intel Xeon Gold 5120 14-core CPUs, 128GB of RAM, and 8x 1.92TB SSDs each. The NAS system housed 42 TB of usable storage per node. The Splunk SmartStore on Nutanix configurations utilized Nutanix to house indexers and search heads in virtual machines and were configured with AOS PRO three-year licenses with production support, three-year hardware support contracts with production support, and a three-year dedicated Objects license with production support.

Splunk SmartStore on Nutanix demonstrated consistent cost savings over Splunk on bare metal servers, and the savings increased with the size of the environment. This is because the cost savings come mainly from offloading warm and cold data to low-cost remote object storage. With SmartStore, the cold storage capacity appears reduced compared to non-SmartStore since the remote storage takes over responsibility for maintaining high availability. Unlike the bare metal option, the replication factor has no effect on how the remote storage service achieves that goal, so organizations can purchase less storage to store the same volume of data.

By providing both the underlying distributed compute platform and the S3 compliant storage layer via Objects, Nutanix can provide both a high degree of compute and storage elasticity and a cost-efficient means to achieve long-term data retention at large scale. In essence, by decoupling compute and storage, organizations can increase resource utilization, increasing flexibility and reducing costs. They can then focus budgets as required on either the compute tier, investing in new solid-state devices for hot and warm buckets, or separately investing in ultra-dense nodes that can contain cold buckets and at the same time address long-term retention needs. All of this can be driven by the simple GUI workflows or by an API that allows organizations to programmatically create, manage, and query both buckets and objects stores.

Splunk SmartStore on Nutanix can also be deployed with multisite indexer clusters to meet disaster recovery requirements. Object stores in the two sites are hosted in an active-active, near-synchronous replication relationship using Nutanix Objects streaming replication. In case of a site failure, search queries are rerouted to the object store on the active site with help of a load balancer. When the failed object store comes back online, object store replication restarts, with the object stores also replicating any data that was unsent at the time of the failure, as well as data uploaded to the remaining object store while the other object store was down.

Data Lakes on Nutanix Objects

Data lakes are critical for unlocking business value across multiple sources of structured and unstructured data. They remove data silos and enable processing of various data sets to provide key business insights. Nutanix Objects provides a cost-effective, petabyte-scale staging area for data lakes to support Apache Spark and other analytics engines.

New S3 Select functionality in Nutanix Objects enables the use of simple SQL expressions to retrieve subsets of Objects content, dramatically accelerating data query performance. Support for notifications in Objects enables data transformations. Performance optimizations in Nutanix Objects vastly reduce the time taken to copy objects, an operation carried out as part of the indexing process for many big data solutions; this reduces the time taken to perform large batch processes.

End-user Computing

ESG audited performance testing of a Nutanix NX-3060-G7 cluster that simulated a growing Citrix 7 Virtual Apps and Desktops deployment. Tests were designed to show the linear scalability of the Nutanix cluster and storage controller latency during both logon storms and steady-state operation. Testing was conducted using the industry-standard VDI benchmarking tool Login VSI. LoginVSI validates application performance and response times for various predefined VDI workloads to show desktop density potential for a given set of hardware and software components.

Two Nutanix NX-3060-G7 blocks with a total of eight nodes formed the test bed cluster. To characterize VDI performance, the LoginVSI Knowledge Worker workload was used. This workload simulates user behavior, using up to seven simultaneous well-known desktop applications like Microsoft Office, Internet Explorer, and Adobe Acrobat Reader, plus video.

Testing began with Login VSI on one node of a four-node cluster, with eight server VMs, to determine the VSImax score, which is the maximum number of users that can be hosted with response times under the threshold for an acceptable user experience.

VSImax was determined to be 175 users for one node. We then ran tests to determine average response time and latency, as both the number of user sessions doubled—to 350, 700, and 1400—and the number of VMs doubled (16, 32, and 64) at two, four, and eight nodes. Figure 12 shows the VSImax average response time in microseconds⁴ as the number of nodes increased.

The results clearly show that the number of user sessions can linearly scale as the number of nodes increases, while maintaining predictable performance in terms of average response time. In other words, increasing the number of virtual desktops as the number of Nutanix nodes increases will not result in a degradation of application response times.

ESG then examined the storage controller latency during a logon storm of 1,400 virtual desktops on the eight-node Nutanix cluster. We examined the latency for both the logon period and steady-state operation (Figure 13). During the 48-minute logon period, user latency reached a maximum of approximately four milliseconds. The average storage latency during this period was calculated to be 2.67 milliseconds. Once all 1,400 virtual desktops were logged on, the average steady-state latency was calculated to be 4.6 milliseconds.

The Bigger Truth

In a world where organizations are leveraging digital transformation, DevOps, and agile development to drive greater efficiency and productivity, organizations need the simplicity and scalability of the cloud that HCI provides to minimize complexity with predictable costs for business- and mission-critical applications and databases with high performance SLAs. High levels of reliable and scalable enterprise-class performance are no longer optional. Nutanix Cloud Platform delivers these features with its unified storage for block, file, and object workloads.

To address these not-always-aligned challenges, Nutanix has:

• Made it simple to deploy and manage. Nutanix provides all the tools and dashboards to manage the environment from a single pane of glass, automatable via APIs.
• Streamlined the I/O stack while leveraging technologies like NVMe, Intel Optane drives, up to 100GbE top of rack (ToR) switching support, and RDMA support to maximize performance.
• Provided for complete, simplified information lifecycle management for databases, advanced analytics, and end-user computing workloads.
• Made improvements to AOS that support vDisk sharding for virtualized workloads, improving database performance.
• Enabled configuration of RF1 storage containers for workloads that don’t rely on storage for resiliency, resulting in higher performance and lower storage costs.
• Enabled Nutanix Objects bidirectional replication for multisite Splunk SmartStore clusters to enhance disaster recovery with an RPO of seconds.
• Enabled Nutanix Objects to be used as a data lake for analytics with S3 Select and faster object copy functionality.

ESG validated that Nutanix has addressed these issues with its latest generation Nutanix Cloud Platform. Testing confirmed that Nutanix meets the demanding performance requirements of dynamic, mission-critical applications like databases, data analytics platforms, and end-user computing. The Nutanix Cloud Platform delivered significant IOPS and latency improvements in all our tests. Synthetic and real-world testing exercised both compute and storage resources to meet the high-transaction and low-latency demands of scalable OLTP database deployments in both Microsoft SQL Server and Oracle OLTP database environments. Testing of big data workloads demonstrated significant performance improvements using RF1 storage provisioning. Nutanix also delivered predictably scalable performance for analytics workloads using Splunk SmartStore with Nutanix Objects and for a Citrix Virtual Apps and Desktops deployment, easily handling the impact of I/O bursts commonly associated with VDI logon storms, while providing impressively low latency. Significant economic advantages were validated using Splunk SmartStore on Nutanix as compared to Splunk on bare metal servers.

The results presented in this document are based on testing in a controlled environment. Due to the many variables in each production data center, it is important to perform planning and testing in your own environment to validate the viability and efficacy of any solution.

As hyperconverged technologies mature, Nutanix continues to expand the boundaries of what is possible by not only adopting and developing cutting-edge technology but also providing software that simplifies life for IT admins and DBAs alike. If your organization needs to modernize your IT infrastructure, ESG recommends a serious look at Nutanix Cloud Platform to provide your critical enterprise applications the benefits of today’s most highly performant compute and storage technology with the simplicity of HCI.

Appendix

The chart below summarizes test results in this report.