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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 INNER JOIN statement

Hive JOIN is used to combine rows from two or more tables together. Hive supports common JOIN operations such as what's in the RDBMS, for example, JOIN, LEFT OUTER JOIN, RIGHT OUTER JOIN, FULL OUTER JOIN, and CROSS JOIN. However, Hive only supports equal JOIN instead of unequal JOIN, because unequal JOIN is difficult to be converted to MapReduce jobs.

The INNER JOIN in Hive uses JOIN keywords, which return rows meeting the JOIN conditions from both left and right tables. The INNER JOIN keyword can also be omitted by comma-separated table names since Hive 0.13.0. See the following examples to show various inner JOIN statements in Hive:

  • Prepare another table to join and load data:
    jdbc:hive2://> CREATE TABLE IF NOT EXISTS employee_hr
    . . . . . . .> (
    . . . . . . .>   name string,
    . . . . . . .>   employee_id int,
    . . . . . . .>   sin_number string,
    . . . . . . .>   start_date date
    . . . . . . .> )
    . . . . . . .> ROW FORMAT DELIMITED
    . . ...
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