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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

Data types


Hive supports all the primitive numeric data types such as TINYINT, SMALLINT, INT, BIGINT, FLOAT, DOUBLE, and DECIMAL. In addition to these primitive data types, Hive also supports string types such as CHAR, VARCHAR, and STRING data types. Like SQL, the time indicator data types such as TIMESTAMP and DATE are present. BOOLEAN and BINARY miscellaneous types are available too.

A number of complex types are also available. Complex types can be composed from other primitive or complex types. The complex types available are as follows:

  • STRUCTS: These are groupings of data elements similar to a C-struct. The dot notation is used to dereference elements within a struct. A field within column C defined as STRUCT {x INT, y STRING} can be accessed as A.x or A.y.

    The syntax for this is STRUCT<field_name : data_type>

  • MAPS: These are key-value data types; providing the key within square braces can access a value. A value of a map column M that maps from key x to value y can be accessed...

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