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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Table Of Contents
Apache Hive Essentials - Second Edition
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Apache Hive Essentials
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Overview of this book
In this book, we prepare you for your journey into big data by frstly introducing you to backgrounds in the big data domain, alongwith the process of setting up and getting familiar with your Hive working environment.
Next, the book guides you through discovering and transforming the values of big data with the help of examples. It also hones your skills in using the Hive language in an effcient manner. Toward the end, the book focuses on advanced topics, such as performance, security, and extensions in Hive, which will guide you on exciting adventures on this worthwhile big data journey.
By the end of the book, you will be familiar with Hive and able to work effeciently to find solutions to big data problems
Table of Contents (12 chapters)
Preface
Free Chapter
Overview of Big Data and Hive
Setting Up the Hive Environment
Data Definition and Description
Data Correlation and Scope
Data Manipulation
Data Aggregation and Sampling
Performance Considerations
Extensibility Considerations
Security Considerations
Working with Other Tools
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