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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

A single machine

A single machine is the simplest use case for Spark. It is also a great way to sanity check your build. In spark/bin, there is a shell script called run-example, which can be used to launch a Spark job. The run-example script takes the name of a Spark class and some arguments. Earlier, we used the run-example script from the /bin directory to calculate the value of Pi. There is a collection of the sample Spark jobs in examples/src/main/scala/org/apache/spark/examples/.

All of the sample programs take the parameter, master (the cluster manager), which can be the URL of a distributed cluster or local[N], where N is the number of threads.

Going back to our run-example script, it invokes the more general bin/spark-submit script. For now, let's stick with the run-example script.

To run GroupByTest locally, try running the following command:

bin/run-example GroupByTest

This line will produce an output like this given here:

14/11/15 06:28:40 INFO SparkContext: Job finished: count at  GroupByTest.scala:51, took 0.494519333 s
2000

Note

All the examples in this book can be run on a Spark installation on a local machine. So you can read through the rest of the chapter for additional information after you have gotten some hands-on exposure to Spark running on your local machine.

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