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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

SparkContext - metadata


The SparkContext object has a set of metadata that I found useful. The version number, application name, and memory available are useful pieces of information. At the start of a Spark program, I usually display/log the version number.

Value

Use

appName

This value is the application name. If you have established a convention, this field can be useful at runtime.

getConf

This value returns configuration information.

getExecutorMemoryStatus

This value retrieves memory details. It could be useful if you want to check memory details. As Spark is distributed, the values do not mean that you are out of memory.

Master

This value is the name of the master.

Version

I found this value very useful, especially while testing with different versions.

Execute the following command from the shell:

cd ~/Downloads/spark-2.0.0  ( Or to wherever you have your spark installed)
bin/spark-shell

Refer to the following screenshot:

scala> spark.version
res0: String...
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