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You're reading from Scala and Spark for Big Data Analytics Explore the concepts of functional programming, data streaming, and machine learning

Product type Paperback

Published in Jul 2017

Publisher Packt

ISBN-13 9781785280849

Length 796 pages

Edition 1st Edition

Languages

Scala

Tools

Apache Spark

Concepts

Big Data

Authors (2):

Karim

Sridhar Alla

View More author details

Table of Contents (25) Chapters

Title Page

Credits

About the Authors

About the Reviewers

www.PacktPub.com

Customer Feedback

Preface

1. Introduction to Scala FREE CHAPTER

2. Object-Oriented Scala

3. Functional Programming Concepts

4. Collection APIs

5. Tackle Big Data – Spark Comes to the Party

6. Start Working with Spark – REPL and RDDs

7. Special RDD Operations

8. Introduce a Little Structure - Spark SQL

9. Stream Me Up, Scotty - Spark Streaming

10. Everything is Connected - GraphX

11. Learning Machine Learning - Spark MLlib and Spark ML

12. My Name is Bayes, Naive Bayes

13. Time to Put Some Order - Cluster Your Data with Spark MLlib

14. Text Analytics Using Spark ML

15. Spark Tuning

16. Time to Go to ClusterLand - Deploying Spark on a Cluster

17. Testing and Debugging Spark

18. PySpark and SparkR

Debugging Spark applications

In this section, we will see how to debug applications that are running locally (on Eclipse or IntelliJ), standalone or mode in YARN or Mesos. However, before diving deeper, it is necessary to know about logging in the Spark application.

Logging with log4j with Spark recap

We have already this topic in Chapter 14, Time to Put Some Order - Cluster Your Data with Spark MLlib. However, let's replay the same contents to make your brain align with the current discussion Debugging Spark applications. As stated earlier, Spark uses log4j for its own logging. If you configured Spark properly, Spark gets logged all the operation to the shell console. A sample snapshot of the file can be seen from the following figure:

Figure 16: A snap of the log4j.properties file

Set the default spark-shell log level to WARN. When running the spark-shell, the log level for this class is used to overwrite the root logger's log level so that the user can have different defaults for the shell...