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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

Complex data types in Pig


Pig has primitive data types such as int, long, float, double, chararray, and bytearray. In addition, Pig also supports complex data types. Inputs and outputs to Pig's relational operators are specified using these complex data types. In some cases, the behavior of the operators depends on the complex data type used. These complex data types are as follows:

  • Map: This data type should not be confused with the map function of MapReduce. The Map data type is an associative array data type that stores a chararray key and its associated value. There is no restriction on the data type of the value in a map. It can be a complex type too. If the type of the value cannot be determined, Pig defaults to the bytearray data type. The key and value association is syntactically done via the # symbol. The key values within a map have to be unique:

    [key#value, key1#value1…]

  • Tuple: A Tuple data type is a collection of data values. They are of fixed length and are ordered. They can...

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