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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

The Hive architecture

The following diagram shows the Hive architecture. We will look at each component in detail:

The Hive architecture

The Hive metastore

The metastore is a database for system-related metadata. It stores details about the tables, partitions, schemas, column types, and table locations. It can be accessed via the Thrift interface, making it possible to read this data using clients written in many different programming languages. The data is stored in a relational database system and uses an Object-relational mapping (ORM) layer to read and write data into the store. The choice of using an RDBMS for the metastore was made to reduce the latency when serving this information to the Hive query compiler.

The ORM layer of the metastore allows a pluggable model where any RDBMS can be plugged into Hive. The default RDBMS used is Apache Derby, an open source relational data store. In practice, organizations use MySQL and other popular RDBMS suites to host the metastore. The data in the metastore imposes...

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