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Scala and Spark for Big Data Analytics

You're reading from  Scala and Spark for Big Data Analytics

Product type Book
Published in Jul 2017
Publisher Packt
ISBN-13 9781785280849
Pages 796 pages
Edition 1st Edition
Languages
Concepts
Authors (2):
Md. Rezaul Karim Md. Rezaul Karim
Profile icon Md. Rezaul Karim
Sridhar Alla Sridhar Alla
Profile icon Sridhar Alla
View More author details

Table of Contents (19) Chapters

Preface 1. Introduction to Scala 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 Spark applications that are running locally (on Eclipse or IntelliJ), standalone or cluster 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 discussed 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...

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