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Scala and Spark for Big Data Analytics

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

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781785280849
Length 796 pages
Edition 1st Edition
Languages
Concepts
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Authors (2):
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Sridhar Alla Sridhar Alla
Author Profile Icon Sridhar Alla
Sridhar Alla
Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
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Toc

Table of Contents (19) Chapters Close

Preface 1. Introduction to Scala 2. Object-Oriented Scala FREE CHAPTER 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

Submitting Spark job for cluster analysis

The examples shown in this chapter can be made scalable for the even larger dataset to serve different purposes. You can package all three clustering algorithms with all the required dependencies and submit them as a Spark job in the cluster. Now use the following lines of code to submit your Spark job of K-means clustering, for example (use similar syntax for other classes), for the Saratoga NY Homes dataset:

# Run application as standalone mode on 8 cores 
SPARK_HOME/bin/spark-submit \
--class org.apache.spark.examples.KMeansDemo \
--master local[8] \
KMeansDemo-0.1-SNAPSHOT-jar-with-dependencies.jar \
Saratoga_NY_Homes.txt

# Run on a YARN cluster
export HADOOP_CONF_DIR=XXX
SPARK_HOME/bin/spark-submit \
--class org.apache.spark.examples.KMeansDemo \
--master yarn \
--deploy-mode cluster \ # can be client for client mode...
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