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Mastering Hadoop

You're reading from   Mastering Hadoop Go beyond the basics and master the next generation of Hadoop data processing platforms

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Product type Paperback
Published in Dec 2014
Publisher
ISBN-13 9781783983643
Length 374 pages
Edition 1st Edition
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Author (1):
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Sandeep Karanth Sandeep Karanth
Author Profile Icon Sandeep Karanth
Sandeep Karanth
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Table of Contents (15) Chapters Close

Preface 1. Hadoop 2.X FREE CHAPTER 2. Advanced MapReduce 3. Advanced Pig 4. Advanced Hive 5. Serialization and Hadoop I/O 6. YARN – Bringing Other Paradigms to Hadoop 7. Storm on YARN – Low Latency Processing in Hadoop 8. Hadoop on the Cloud 9. HDFS Replacements 10. HDFS Federation 11. Hadoop Security 12. Analytics Using Hadoop A. Hadoop for Microsoft Windows Index

UDF, UDAF, and UDTF


Like in Pig, UDFs are one of the most important extensibility features in Hive. Writing a UDF in Hive is simpler, but the interfaces do not define every override method that is needed to make the UDF complete. This is because UDFs can take any number of parameters, and it is difficult to provide a fixed interface. Hive uses Java reflection under the hood when executing the UDF to figure out the parameter list for the function.

These are the following three kinds of UDFs in Hive:

  • Regular UDFs: These UDFs take in a single row and produce a single row after application of the custom logic.

  • UDAFs: These are aggregators that take in multiple rows but output a single row. SUM and COUNT are examples of in-built UDAFs.

  • UDTFs: These are generator functions that take in a single row and produce multiple rows as outputs. The EXPLODE function is a UDTF.

The following code example shows how a simple UDF is written. Every UDF is extended from the UDF class present in org.apache.hadoop...

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