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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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Author (1):
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Dayong Du Dayong Du
Author Profile Icon Dayong Du
Dayong Du
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Table of Contents (12) Chapters Close

Preface 1. Overview of Big Data and Hive 2. Setting Up the Hive Environment FREE CHAPTER 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

Project data with SELECT

The most common use case for Hive is to query data in Hadoop. To achieve this, we need to write and execute a SELECT statement. The typical work done by the SELECT statement is to project the whole row (with SELECT *) or specified columns (with SELECT column1, column2, ...) from a table, with or without conditions.Most simple SELECT statements will not trigger a Yarn job. Instead, a dump task is created just for dumping the data, such as the hdfs dfs -cat command. The SELECT statement is quite often used with the FROM and DISTINCT keywords. A FROM keyword followed by a table is where SELECT projects data. The DISTINCT keyword used after SELECT ensures only unique rows or combination of columns are returned from the table. In addition, SELECT also supports columns combined with user-defined functions, IF(), or a CASE WHEN THEN ELSE END statement, and regular...

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