Efficient data analytics using SAS 9.4 and HPE GreenLake for File Storage

[StorageExperts]

In the data-driven world today, enterprises deploy artificial intelligence, machine learning, and data analytics applications to gain a competitive edge and make real-time decisions to drive their business. These applications require super-fast data processing capabilities and utilize huge amounts of storage. HPE GreenLake for File Storage offers just that – a highly performant, reliable, and scalable file storage solution. In this blog, I’m focusing on how HPE GreenLake for File Storage runs efficient data analytics using one of the industry-leading applications, SAS 9.4.  – By Anshul Nagori, Senior Worldwide Technical Marketing Engineer, HPE

The latest report from The Forrester Wave marks SAS as a strong leader in the AI decisioning platforms. SAS requires an infrastructure stack that complements its in-memory processing capabilities and offers better performance than ever before. This translates to a hardware and software configuration that delivers data analytics quickly and accurately. In addition to faster compute, it demands reliable and fast storage.  

HPE GreenLake for File Storage standard density is an all-NVMe, disaggregated, scale-out file storage solution. It offers fast, sustained, and predictable performance for data intensive applications like SAS. HPE has built this storage offering in collaboration with VAST Data Software. It uses the HPE Alletra Storage MP modular hardware platform offering a flexible, disaggregated, scale-out, shared-everything architecture. This means you can independently scale performance as well as compute based on your needs. From a management perspective, HPE GreenLake for File Storage benefits from the awesomeness of HPE GreenLake cloud. It enables streamlined deployment, super-easy file share creation, and a simple self-service cloud experience.

We’ve been testing SAS 9.4 Mixed Analytics workload with the HPE GreenLake for File Storage and the results are stellar. Let's take a look at the data reduction achieved during this testing. But first, let me take you through our test environment.

Figure 1: SAS 9.4 on HPE GreenLake for File Storage setup

As shown in Figure 1, we used six HPE ProLiant DL 360 Gen 10 servers with a total of 208 physical cores as test clients. Each client had 512 GB of memory and RedHat Linux Enterprise Linux 9.3 as the operating system. These clients were connected to the HPE GreenLake for File Storage system over multiple 100 GbE ports. The file shares on HPE GreenLake for File Storage were presented and mounted to the SAS clients using NFS over TCP with nConnect set to 8.

Analytics workloads are generally measured in terms of concurrent number of users and/or jobs. This test began with a set of 30 users running 102 concurrent analytics jobs. And we steadily increased the number of users and jobs in each run, from 30 all the way up to 180. Correspondingly, the number of jobs ranged from 102 to 612. Figure 2 shows the combined read + write throughput obtained on each run. The maximum throughput for 180 users we observed was 15 GBps for jobs of up to 50 seconds duration. The throughput reduces as the average job duration increases for the same number of users. This performance is quite decent for SAS customers using high performing shared storage in their GRID environments.

Figure 2: Combined read + write throughput on each run

As you can observe, this throughput doesn’t increase much from 180 concurrent users to 360 users. This means that the HPE GreenLake for File Storage standard density 2x2 configuration performs best for up to 180 concurrent SAS 9.4 users or 612 concurrent jobs.

HPE GreenLake for File Storage – ideal for AI and analytics

HPE GreenLake for File Storage is a modern, disaggregated, scale-out solution offering intuitive cloud experience, simplified management, efficient space optimization for several real-world use cases – especially AI and analytics.

Please check out the technical paper describing this solution and the performance characterization in detail: SAS 9.4 on HPE GreenLake  for File Storage – Run efficient data analytics with SAS 9.4 on a high-performing, disaggregated, scale-out file storage

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Meet HPE Blogger Anshul Nagori, Senior Worldwide Technical Marketing Engineer, HPE

Anshul works for the worldwide storage solutions team at HPE and has more than 13 years of IT experience. He is a regular speaker at SAP and HPE events. His areas of focus include SAP HANA, SAS Analytics, storage, data management, and data protection solutions. Connect with Anshul on LinkedIn.

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