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Fast Data Processing with Spark 2

You're reading from   Fast Data Processing with Spark 2 Accelerate your data for rapid insight

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Product type Paperback
Published in Oct 2016
Publisher Packt
ISBN-13 9781785889271
Length 274 pages
Edition 3rd Edition
Languages
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Authors (2):
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Krishna Sankar Krishna Sankar
Author Profile Icon Krishna Sankar
Krishna Sankar
Holden Karau Holden Karau
Author Profile Icon Holden Karau
Holden Karau
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Toc

Table of Contents (13) Chapters Close

Preface 1. Installing Spark and Setting Up Your Cluster 2. Using the Spark Shell FREE CHAPTER 3. Building and Running a Spark Application 4. Creating a SparkSession Object 5. Loading and Saving Data in Spark 6. Manipulating Your RDD 7. Spark 2.0 Concepts 8. Spark SQL 9. Foundations of Datasets/DataFrames – The Proverbial Workhorse for DataScientists 10. Spark with Big Data 11. Machine Learning with Spark ML Pipelines 12. GraphX

Loading data into an RDD


In this chapter, we will examine the different sources you can use for your RDD. If you decide to run it through the examples in the Spark shell, you can call .cache() or .first() on the RDDs you generate to check whether it can be loaded. In Chapter 2, Using the Spark Shell, you learned how to load data text from a file and from S3. In this chapter, we will look at the different formats of data (text file and CSV) and the different sources (filesystem and HDFS) supported.

One of the easiest ways to create an RDD is taking an existing Scala collection and converting it into an RDD. The SparkContext object provides a function called parallelize that takes a Scala collection and converts it into an RDD of the same type as the input collection, as shown here.

As mentioned in the previous chapters, cd to the fdps-v3 directory and run spark-shell or pyspark.

For Scala, refer to the following screenshot:

For Java, refer to the following code:

import java.util.Arrays; 
...
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