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  • Cloudera Cloudera
  • This three-day 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.

    What You Will Learn

    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

    Book the course

    How would you like to train?

    What to Expect

    This course is designed for software developers, engineers, and data scientists who have experience developing Spark applications and want to learn how to improve the performance of their code. This is not an introduction to Spark.

    Spark examples and hands-on exercises are presented in Python and the ability to program in this language is required. Basic familiarity with the Linux command line is assumed. Basic knowledge of SQL is helpful.

    Course Topics

    Spark Architecture

    • RDDs
    • DataFrames and Datasets
    • Lazy Evaluation
    • Pipelining

    Data Sources and Formats

    • Available Formats Overview
    • Impact on Performance
    • The Small Files Problem

    Inferring Schemas

    • The Cost of Inference
    • Mitigating Tactics

    Dealing With Skewed Data

    • Recognizing Skew
    • Mitigating Tactics

    Catalyst and Tungsten Overview

    • Catalyst Overview
    • Tungsten Overview

    Mitigating Spark Shuffles

    • Denormalization
    • Broadcast Joins
    • Map-Side Operations
    • Sort Merge Joins

    Partitioned and Bucketed Tables

    • Partitioned Tables
    • Bucketed Tables
    • Impact on Performance

    Improving Join Performance

    • Skewed Joins
    • Bucketed Joins
    • Incremental Joins

    Pyspark Overhead and UDFs

    • Pyspark Overhead
    • Scalar UDFs
    • Vector UDFs using Apache Arrow
    • Scala UDFs

    Caching Data for Reuse

    • Caching Options
    • Impact on Performance
    • Caching Pitfalls

    Workload XM (WXM) Introduction

    • WXM Overview
    • WXM for Spark Developers

    What's New in Spark 3.0?

    • Adaptive Number of Shuffle Partitions
    • Skew Joins
    • Convert Sort Merge Joins to Broadcast Joins
    • Dynamic Partition Pruning
    • Dynamic Coalesce Shuffle Partitions

    Appendix A: Partition Processing

    Appendix B: Broadcasting

    Appendix C: Scheduling

    Learn more

    CCA Spark and Hadoop Developer Certification

    This course is excellent preparation for the CCA Spark and Hadoop Developer exam. Although we recommend further training and hands-on experience before attempting the exam, this course covers many of the subjects tested. 

    Certification is a great differentiator. It helps establish you as a leader in the field, providing employers and customers with tangible evidence of your skills and expertise.

    Advance your career

    Big data developers are among the world's most in-demand and highly-compensated technical roles. Check out some of the job opportunities currently listed that match the professional profile, many of which seek CCA qualifications.

    Private training

    We also provide private training at your site, at your pace, and tailored to your needs.

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