Growing up in the 90s, I spent considerable time watching exciting Formula 1 racing. For other kids, it was cartoons, but for me, the excitement of racing became my lifelong passion. It was not just the adrenaline rush from seeing stars such as Michael Schumacher and Rubens Barrichello race to the checkered flag, but the hours clocked in by the engineers with the passion to go few seconds faster and their absolute drive for perfection. Wanna see something cool? Check out the moves on this pit stop – if this video doesn’t blow your spark plugs, then you probably need a dealer service visit!
It was frequently mere seconds that turned the tide in a race, and as long as Formula 1 has been around, there has been advancement in aerodynamics, material science and data analysis to win. Just like in racing, the inherent desire to best the competition is also what keeps our technology industry thriving, bringing a revolutionary product to our door every few months.
Speed and efficiency are not just important in motorsports but for several applications that can change our way of life, such as security, finance and healthcare. It is the responsibility of hardware and software vendors to cater to these ever-growing needs, where finishing a second faster could mean saving millions of dollars, or even lives. To that end, last week at World of Watson and Spark Summit, IBM, Mellanox, Lenovo and Intel together showcased a solution to address the need for a faster analytics system with the highest efficiency in today’s data-driven world.
The result was industry’s first 100 Terabyte Spark SQL solution that showed up to five times faster query response with three times the efficiency when compared with the current-gen solution. Powered by IBM Open Platform with Apache Hadoop with Spark SQL optimization from IBM Spark Technology Center, and with the building blocks of compute, network and storage that is the epitome of speed and efficiency. With this Hadoop/Spark-based solution, enterprises can now accelerate their existing SQL applications, gaining faster insight from their business data.
The solution consists of 30 Lenovo servers with 28 data nodes (X3650 M5) and two management nodes (x3640 M5). Each data nodes is powered by dual-socket Intel E5-2697 V4 processors (36 cores total) and loaded with 1.5TB of memory and 16TB of Intel NVMe SSDs. With an increasing need to use faster memory and storage for IOPS-intensive big data workloads such as Spark SQL, a faster network is of paramount importance. It takes ~10HDDs per data node to exceed what a 10Gbps can do, but only a single NVMe SSD to drive 25GbE network. Just four of these can fill a 100GbE pipe.
Network latency also becomes crucial for such transactional-centric workload to consistently deliver low- latency queries. The solution uses the industry’s lowest-latency and highest throughput adapter, the Mellanox ConnectX®-4 100GbE NIC. The solution is connected with a 100GbE non-blocking, zero packet loss Spectrum™ SN2700 switch and LinkX® DAC (Direct Attached Copper) cables.
Mellanox ConnectX-4 Ethernet adapters provide the most flexible interconnect solution for Big Data, Cloud and HPC applications at speeds of 10/25 and 40/50/100Gbps. Big Data applications utilizing TCP or UDP over IP transport can achieve the highest efficiency and application density with the hardware-based stateless offloads and flow steering engines. These advanced offloads reduce CPU overhead for packet processing and lower query latency.
Mellanox Spectrum-based SN2700 (32x 100GbE ports) switches provide the highest- performance fabric solution in a 1U form factor while delivering non-blocking throughput for big data workloads. They also feature predictable low-latency, zero-packet loss (ZPL) and microburst absorption that is 9x to 15x times better than the competitors (To learn more read the blog on Can you Afford an Unpredictable Network?). Due to the bursty network traffic in big data workloads, non-blocking switches are crucial in delivering predictable SQL query completion time. In addition, LinkX DAC cables offer reliable connections at speeds from 10 to 100Gb/s with highest quality, featuring error rates up to 100x lower than industry standards.
To demonstrate the performance, this cluster ran a Hadoop-DS (derivative of TPC-DS) benchmark with IBM Spark supporting many of the SQL 2003 features required by the benchmark. Our results showed up to five times faster query times when compared with the current-gen solution, along with three times better energy efficiency in just one-fifth of floor space.