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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
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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

Spark REPL also known as CLI


In Chapter 1, Introduction to Spark, we learnt that one of the advantages of Apache Spark over the MapReduce framework is interactive processing. Apache Spark achieves the same using Spark REPL.

Spark REPL or Spark shell, also known as Spark CLI, is a very useful tool for exploring the Spark programming. REPL is an acronym for Read-Evaluate-Print Loop. It is an interactive shell used by programmers to interact with a framework. Apache Spark also comes with REPL that beginners can use to understand the Spark programming model.

To launch the Spark REPL, we will execute the command that we executed in the previous section:

$SPARK_HOME/bin/spark-shell

Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel).
16/11/01 16:38:43 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
16/11...
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