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Apache Hive Essentials

You're reading from   Apache Hive Essentials Immerse yourself on a fantastic journey to discover the attributes of big data by using Hive

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Product type Paperback
Published in Feb 2015
Publisher Packt
ISBN-13 9781783558575
Length 208 pages
Edition 1st Edition
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Author (1):
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Dayong Du Dayong Du
Author Profile Icon Dayong Du
Dayong Du
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Table of Contents (12) Chapters Close

Preface 1. Overview of Big Data and Hive FREE CHAPTER 2. Setting Up the Hive Environment 3. Data Definition and Description 4. Data Selection and Scope 5. Data Manipulation 6. Data Aggregation and Sampling 7. Performance Considerations 8. Extensibility Considerations 9. Security Considerations 10. Working with Other Tools Index

SerDe


SerDe stands for Serializer and Deserializer. It is the technology that Hive uses to process records and map them to column data types in Hive tables. To explain the scenario of using SerDe, we need to understand how Hive reads and writes data.

The process to read data is as follows:

  1. Data is read from HDFS.

  2. Data is processed by the INPUTFORMAT implementation, which defines the input data split and key/value records. In Hive, we can use CREATE TABLE ... STORED AS <FILE_FORMAT> (see Chapter 7, Performance Considerations, for available file formats) to specify which INPUTFORMAT it reads from.

  3. The Java Deserializer class defined in SerDe is called to format the data into a record that maps to column and data types in a table.

For an example of reading data, we can use JSON SerDe to read the TEXTFILE format data from HDFS and translate each row of the JSON attribute and value to rows in Hive tables with the correct schema.

The process to write data is as follows:

  1. Data (such as using an INSERT...

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