Packt | Troubleshooting Apache Spark


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Oct 21, 2018

By: Tomasz Lelek
Released: November 28, 2018
Caption (CC): Included
Torrent Contains: 49 Files, 1 Folders
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Quick, simple solutions to common development issues and Debugging techniques with Apache Spark.

Video Details

ISBN 9781789805253
Course Length 1 hour 43 minutes

Table of Contents

• Common Problems and Troubleshooting the Spark Distributed Engine
• Distributed DataFrames Optimization Pitfalls
• Distributed Joins in Cluster
• Solving Problems with Non-Efficient Transformations
• Troubleshooting Real-Time Processing Jobs in Spark Streaming


• Solve long-running computation problems by leveraging lazy evaluation in Spark
• Avoid memory leaks by understanding the internal memory management of Apache Spark
• Rework problems due to not-scaling out pipelines by using partitions
• Debug and create user-defined functions that enrich the Spark API
• Choose a proper join strategy depending on the characteristics of your input data
• Troubleshoot APIs for joins - DataFrames or DataSets
• Write code that minimizes object creation using the proper API
• Troubleshoot real-time pipelines written in Spark Streaming


Apache Spark has been around quite some time, but do you really know how to solve the development issues and problems you face with it? This course will give you new possibilities and you'll cover many aspects of Apache Spark; some you may know and some you probably never knew existed. If you take a lot of time learning and performing tasks on Spark, you are unable to leverage Apache Spark's full capabilities and features, and face a roadblock in your development journey. You'll face issues and will be unable to optimize your development process due to common problems and bugs; you'll be looking for techniques which can save you from falling into any pitfalls and common errors during development. With this course you'll learn to implement some practical and proven techniques to improve particular aspects of Apache Spark with proper research

You need to understand the common problems and issues Spark developers face, collate them, and build simple solutions for these problems. One way to understand common issues is to look out for Stack Overflow queries. This course is a high-quality troubleshooting course, highlighting issues faced by developers in different stages of their application development and providing them with simple and practical solutions to these issues. It supplies solutions to some problems and challenges faced by developers; however, this course also focuses on discovering new possibilities with Apache Spark. By the end of this course, you will have solved your Spark problems without any hassle.

All the code and supporting files for this course are available on Github at

Style and Approach

This course takes a question-and-answer approach, identifying key problems faced by Apache Spark developers and providing straightforward solutions.


• Optimize resources and costs by utilizing Spark's speed
• Troubleshoot the Spark execution DAG by exploring Spark logical and physical query plans to perform the same logic on fewer executors and machines
• Solve the problem of slow-running jobs by speeding up feedback loops by creating efficient transformations and joins using Spark APIs


Tomasz Lelek

Tomasz Lelek is a software engineer, programming mostly in Java and Scala. He has been working with the Spark and ML APIs for the past 6 years, with production experience in processing petabytes of data. He is passionate about nearly everything associated with software development and believes that we should always try to consider different solutions and approaches before attempting to solve a problem. Recently he was a speaker at conferences in Poland—Confitura and JDD (Java Developers Day)—and at Krakow Scala User Group. He has also conducted a live coding session at the Geecon Conference. Contacted on 10/09/2019 for Typescript book - author said he is not an expert on Typescript, his area of expertise is in back-end technologies like Java, Spring and Big Data - Spark.