Packt Troubleshooting Apache Spark

Packt – Troubleshooting Apache Spark
English | Size: 1.03 GB
Category: Tutorial
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

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

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