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Apache Hive Essentials

You're reading from   Apache Hive Essentials Immerse yourself on a fantastic journey to discover the attributes of big data by using Hive

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Product type Paperback
Published in Feb 2015
Publisher Packt
ISBN-13 9781783558575
Length 208 pages
Edition 1st 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 Selection and Scope 5. Data Manipulation 6. Data Aggregation and Sampling 7. Performance Considerations 8. Extensibility Considerations 9. Security Considerations 10. Working with Other Tools Index

The SELECT statement


The most common use case of using Hive is to query the data in Hadoop. To achieve this, we need to write and execute the SELECT statement in Hive. The typical work done by the SELECT statement is to project the rows meeting query conditions specified in the WHERE clause after the target table and return the result set. The SELECT statement is quite often used with FROM, DISTINCT, WHERE, and LIMIT keywords. We will introduce them through examples as follows.

The SELECT * statement here means all the columns in the table are selected. By default, all rows are returned including duplicated rows. If the DISTINCT keyword is used, only unique rows from the table are selected and returned. The LIMIT keyword is used to limit the number of rows returned randomly. In addition, SELECT * scans the whole table/file without triggering MapReduce jobs, so it runs faster than SELECT <column_name>. Since Hive 0.10.0, the simple SELECT statements, such as SELECT <column_name&gt...

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