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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

Saving your data


While distributed computational jobs are a lot of fun, they are much more useful when the results are stored in a useful place. While the methods for loading an RDD are largely found in the SparkContext class, the methods for saving an RDD are defined on the RDD classes. In Scala, implicit conversions exist so that an RDD, which can be saved as a sequence file, could be converted to the appropriate type; in Java, explicit conversions must be used.

Here are the different ways to save an RDD.

Here's the code for Scala:

rddOfStrings.saveAsTextFile("out.txt") 
keyValueRdd.saveAsObjectFile("sequenceOut") 

Here's the code for Java:

rddOfStrings.saveAsTextFile("out.txt") 
keyValueRdd.saveAsObjectFile("sequenceOut") 

Here's the code for Python:

rddOfStrings.saveAsTextFile("out.txt")

Tip

In addition, users can save the RDD as a compressed text file using the following function: saveAsTextFile(path: String, codec: Class[_ <: CompressionCodec])

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