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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 OUTER JOIN and CROSS JOIN statements


Besides INNER JOIN, Hive also supports regular OUTER JOIN and FULL JOIN. The logic of such JOIN is the same to what's in the RDBMS. The following table summarizes the differences of a common JOIN:

Common JOIN type

Logic

Rows returned (assume table_m has m rows and table_n has n rows)

table_m

JOIN

table_n

This returns all rows matched in both tables.

m ∩ n

table_m

LEFT [OUTER] JOIN

table_n

This returns all rows in the left table and matched rows in the right table. If there is no match in the right table, return null in the right table.

m

table_m

RIGHT [OUTER] JOIN

table_n

This returns all rows in the right table and matched rows in the left table. If there is no match in the left table, return null in the left table.

n

table_m

FULL [OUTER] JOIN

table_n

This returns all rows in both the tables and matched rows in both the tables. If there is no match in the left or right table, return null instead.

m + n - m ...

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