Advanced Spark Application Performance Tuning
Course delivers the key concepts and expertise developers need to improve the performance of their Apache Spark applications.
Course Overview
Course delivers the key concepts and expertise developers need to improve the performance of their Apache Spark applications.
This hands-on training course delivers the key concepts and expertise developers need to improve the performance of their Apache Spark applications. During the course, participants will learn how to identify common sources of poor performance in Spark applications, techniques for avoiding or solving them, and best practices for Spark application monitoring.
Apache Spark Application Performance Tuning presents the architecture and concepts behind Apache Spark and underlying data platform, then builds on this foundational understanding by teaching students how to tune Spark application code. The course format emphasizes instructor-led demonstrations illustrate both performance issues and the techniques that address them, followed by hands-on exercises that give students an opportunity to practice what they’ve learned through an interactive notebook environment. The course applies to Spark 2.4, but also introduces the Spark 3.0 Adaptive Query Execution framework.
Course Objectives
Students who successfully complete this course will be able to:
Understand Apache Spark’s architecture, job execution, and how techniques such as lazy execution and pipelining can improve runtime performance
Evaluate the performance characteristics of core data structures such as RDD and DataFrames
Select the file formats that will provide the best performance for your application
Identify and resolve performance problems caused by data skew
Use partitioning, bucketing, and join optimizations to improve SparkSQL performance
Understand the performance overhead of Python-based RDDs, DataFrames, and user-defined functions
Take advantage of caching for better application performance
Understand how the Catalyst and Tungsten optimizers work
Understand how Workload XM can help troubleshoot and proactively monitor Spark applications performance
Learn about the new features in Spark 3.0 and specifically how the Adaptive Query Execution engine improves performance
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