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Apache Hive Essentials

You're reading from  Apache Hive Essentials

Product type Book
Published in Feb 2015
Publisher Packt
ISBN-13 9781783558575
Pages 208 pages
Edition 1st Edition
Languages
Author (1):
Dayong Du Dayong Du
Profile icon Dayong Du

Table of Contents (17) Chapters

Apache Hive Essentials
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Overview of Big Data and Hive 2. Setting Up the Hive Environment 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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