Spark Application Performance Management with Pepperdata Application Spotlight

Presented by

Vinod Nair

About this talk

Pepperdata Application Spotlight analyzes all Hadoop and Spark jobs running on the cluster and provides developers with technical insights on how each job performed. Intended for software engineers, developers, and technical leads who develop Spark applications, this webinar demonstrates how Application Spotlight helps developers quickly improve application performance, reduce resource usage, and understand application failures. Participate in this webinar and learn how developers can: –Identify the lines of code and the stages that cause performance issues related to CPU, memory, garbage collection, network, and disk I/O –Easily disambiguate resources used during parallel stages –Understand why run-time variations occur for the same application –Determine whether performance issues are due to the application or other workloads on the cluster –Receive actionable recommendations for tuning jobs –Validate tuning changes made to applications with a before and after comparison –View the highlights worst performing phases of jobs –Improve MapReduce and Spark developer productivity –Improve cluster efficiency based on clear recommendations on how to modify workloads and configurations
 Vinod Nair leads product management at Pepperdata. He brings more than 20 years of experience in engineering and product management to the job, with a special interest in distributed systems and Hadoop. He has worked in software for telecommunications, financial management for small business, and big data. Vinod’s approach to product management is deeply influenced by his success in applying Lean Startup principles and rapid iteration to product design and development.
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Pepperdata Capacity Optimizer delivers 30-47% greater cost savings for data-intensive workloads, eliminating the need for manual tuning by optimizing CPU and memory in real time with no application changes. Pepperdata pays for itself, immediately decreasing instance hours/waste, increasing utilization, and freeing developers from manual tuning to focus on innovation.