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Spark Cookbook

You're reading from   Spark Cookbook With over 60 recipes on Spark, covering Spark Core, Spark SQL, Spark Streaming, MLlib, and GraphX libraries this is the perfect Spark book to always have by your side

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Product type Paperback
Published in Jul 2015
Publisher
ISBN-13 9781783987061
Length 226 pages
Edition 1st Edition
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Author (1):
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Rishi Yadav Rishi Yadav
Author Profile Icon Rishi Yadav
Rishi Yadav
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Table of Contents (14) Chapters Close

Preface 1. Getting Started with Apache Spark 2. Developing Applications with Spark FREE CHAPTER 3. External Data Sources 4. Spark SQL 5. Spark Streaming 6. Getting Started with Machine Learning Using MLlib 7. Supervised Learning with MLlib – Regression 8. Supervised Learning with MLlib – Classification 9. Unsupervised Learning with MLlib 10. Recommender Systems 11. Graph Processing Using GraphX 12. Optimizations and Performance Tuning Index

Creating HiveContext

SQLContext and its descendant HiveContext are the two entry points into the world of Spark SQL. HiveContext provides a superset of functionality provided by SQLContext. The additional features are:

  • More complete and battle-tested HiveQL parser
  • Access to Hive UDFs
  • Ability to read data from Hive tables

From Spark 1.3 onwards, the Spark shell comes loaded with sqlContext (which is an instance of HiveContext not SQLContext). If you are creating SQLContext in Scala code, it can be created using SparkContext, as follows:

val sc: SparkContext
val sqlContext = new org.apache.spark.sql.SQLContext(sc)

In this recipe, we will cover how to create instance of HiveContext, and then access Hive functionality through Spark SQL.

Getting ready

To enable Hive functionality, make sure that you have Hive enabled (-Phive) assembly JAR is available on all worker nodes; also, copy hive-site.xml into the conf directory of the Spark installation. It is important that Spark has access to hive-site.xml...

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