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Learning Hadoop 2

You're reading from   Learning Hadoop 2 Design and implement data processing, lifecycle management, and analytic workflows with the cutting-edge toolbox of Hadoop 2

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Product type Paperback
Published in Feb 2015
Publisher Packt
ISBN-13 9781783285518
Length 382 pages
Edition 1st Edition
Tools
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Toc

Table of Contents (13) Chapters Close

Preface 1. Introduction 2. Storage FREE CHAPTER 3. Processing – MapReduce and Beyond 4. Real-time Computation with Samza 5. Iterative Computation with Spark 6. Data Analysis with Apache Pig 7. Hadoop and SQL 8. Data Lifecycle Management 9. Making Development Easier 10. Running a Hadoop Cluster 11. Where to Go Next Index

Extending HiveQL


The HiveQL language can be extended by means of plugins and third-party functions. In Hive, there are three types of functions characterized by the number of rows they take as input and produce as output:

  • User Defined Functions (UDFs): are simpler functions that act on one row at a time.

  • User Defined Aggregate Functions (UDAFs): take multiple rows as input and generate multiple rows as output. These are aggregate functions to be used in conjunction with a GROUP BY statement (similar to COUNT(), AVG(), MIN(), MAX(), and so on).

  • User Defined Table Functions (UDTFs): take multiple rows as input and generate a logical table comprised of multiple rows that can be used in join expressions.

Tip

These APIs are provided only in Java. For other languages, it is possible to stream data through a user-defined script using the TRANSFORM, MAP, and REDUCE clauses that act as a frontend to Hadoop's streaming capabilities.

Two APIs are available to write UDFs. A simple API org.apache.hadoop...

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