Accelerating Apache Spark Shuffle for Data Analytics on Cloud with Remote Persistent Memory Pools

Explore a 33-minute conference talk on accelerating Apache Spark shuffle operations for cloud-based data analytics using remote persistent memory pools. Dive into the challenges of serving growing data-driven AI and analytics workloads in disaggregated storage and compute environments. Learn about a proposed fully disaggregated shuffle solution leveraging persistent memory and RDMA technologies, including a new pluggable shuffle manager and distributed storage system. Discover how this innovative approach improves Spark's scalability, performance, and reliability, with experimental results showing up to 10x performance speedup over traditional shuffle solutions. Gain insights into the architecture, optimization features, and workflow of this cutting-edge solution presented by Databricks.

Introduction
Agenda
Motivation
Recap
Original Example
Results
New Challenges
Rtmp Architecture
Optimization Features
Workflow
Summary
Performance Evaluation
Examples
Call to Action
Optima Natives