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Apache Spark 2.x for Java Developers

You're reading from   Apache Spark 2.x for Java Developers Explore big data at scale using Apache Spark 2.x Java APIs

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781787126497
Length 350 pages
Edition 1st Edition
Languages
Concepts
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Authors (2):
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Sourav Gulati Sourav Gulati
Author Profile Icon Sourav Gulati
Sourav Gulati
Sumit Kumar Sumit Kumar
Author Profile Icon Sumit Kumar
Sumit Kumar
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Toc

Table of Contents (12) Chapters Close

Preface 1. Introduction to Spark FREE CHAPTER 2. Revisiting Java 3. Let Us Spark 4. Understanding the Spark Programming Model 5. Working with Data and Storage 6. Spark on Cluster 7. Spark Programming Model - Advanced 8. Working with Spark SQL 9. Near Real-Time Processing with Spark Streaming 10. Machine Learning Analytics with Spark MLlib 11. Learning Spark GraphX

Some basic exercises using Spark shell


Note that Spark shell is available only in the Scala language. However, we have kept examples easy to understand by Java developers.

Checking Spark version

Execute the following command to check the Spark version using spark-shell:

scala>sc.version
res0: String = 2.1.1

It is shown in the following screenshot:

Creating and filtering RDD

Let's start by creating an RDD of strings:

scala>val stringRdd=sc.parallelize(Array("Java","Scala","Python","Ruby","JavaScript","Java"))
stringRdd: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[0] at parallelize at <console>:24

Now, we will filter this RDD to keep only those strings that start with the letter J:

scala>valfilteredRdd = stringRdd.filter(s =>s.startsWith("J"))
filteredRdd: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[2] at filter at <console>:26

In the first chapter, we learnt that if an operation on RDD returns an RDD then it is a transformation, or else it is an action.

The...

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