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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
Toc

Table of Contents (19) Chapters close

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

Common mistakes in Spark app development

Common mistakes that happen often are application failure, a slow job that gets stuck due to numerous factors, mistakes in the aggregation, actions or transformations, an exception in the main thread and, of course, Out Of Memory (OOM).

Application failure

Most of the time, application failure happens because one or more stages fail eventually. As discussed earlier in this chapter, Spark jobs comprise several stages. Stages aren't executed independently: for instance, a processing stage can't take place before the relevant input-reading stage. So, suppose that stage 1 executes successfully but stage 2 fails to execute, the whole application fails eventually. This can be shown...

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