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Apache Hive Essentials

You're reading from   Apache Hive Essentials Essential techniques to help you process, and get unique insights from, big data

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788995092
Length 210 pages
Edition 2nd Edition
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Concepts
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Author (1):
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Dayong Du Dayong Du
Author Profile Icon Dayong Du
Dayong Du
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Toc

Table of Contents (12) Chapters Close

Preface 1. Overview of Big Data and Hive FREE CHAPTER 2. Setting Up the Hive Environment 3. Data Definition and Description 4. Data Correlation and Scope 5. Data Manipulation 6. Data Aggregation and Sampling 7. Performance Considerations 8. Extensibility Considerations 9. Security Considerations 10. Working with Other Tools 11. Other Books You May Enjoy

Spark

As a general-purpose data engine, Apache Spark can integrate with Hive closely. Spark SQL has supported a subset of HQL and can leverage the Hive metastore to write or query data in Hive. This approach is also called Spark over Hive. To configure Spark, use Hive the metastore, you only need to copy the hive-site.xml to the ${SPARK_HOME}/conf directory. After that, running the spark-sql command will enter the Spark SQL interactive environment, where you can write SQL to query Hive tables.

On the other hand, Hive over Spark is a similar approach, but lets Hive use Spark as an alternative engine. In this case, users still stay in Hive and write HQL, but run over the Spark engine transparently. Hive over Spark requires the Yarn FairScheduler and set hive.execution.engine=spark. For more details, refer to https://cwiki.apache.org/confluence/display/Hive/Hive+on+Spark%3A+Getting...

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