Effectively leveraging fast networking and storage hardware (e.g., RDMA, NVMe, etc.) in Apache Spark remains challenging. Current ways to integrate the hardware at the operating system level fall short, as the hardware performance advantages are shadowed by higher layer software overheads. This session will show how to integrate RDMA and NVMe hardware in Spark in a way that allows applications to bypass both the operating system and the Java virtual machine during I/O operations. With such an approach, the hardware performance advantages become visible at the application level, and eventually translate into workload runtime improvements. Stuedi will demonstrate how to run various Spark workloads (e.g, SQL, Graph, etc.) effectively on 100Gbit/s networks and NVMe flash. Session hashtag: #SFr7
I'm a member of the research staff at IBM research Zurich. My research interests are in distributed systems, networking and operating systems. I graduated with a PhD from ETH Zurich in 2008 and spent two years (2008-2010) as a Postdoc at Microsoft Research Silicon Valley. The general theme of my work is to explore how modern networking and storage hardware can be exploited in distributed systems. Currently I'm working on the Apache Crail, a new Apache project providing fast distributed storage on modern hardware.