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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 FREE CHAPTER 2. Setting Up the Hive Environment 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

Linking data with JOIN

JOIN is used to link rows from two or more tables together. Hive supports most SQL JOIN operations, such as INNER JOIN and OUTER JOIN. In addition, HQL supports some special joins, such as MapJoin and Semi-Join too. In its earlier version, Hive only supported equal join. After v2.2.0, unequal join is also supported. However, you should be more careful when using unequal join unless you know what is expected, since unequal join is likely to return many rows by producing a Cartesian product of joined tables. When you want to restrict the output of a join, you should apply a WHERE clause after join as JOIN occurs before the WHERE clause. If possible, push filter conditions on the join conditions rather than where conditions to have data filtered earlier. What's more, all types of left/right joins are not commutative and always left/right associative, while...

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