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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

Hive query optimizers


After type checking and semantic analysis of the query, a number of rule-based transformations are applied to optimize the query. We will discuss some of these optimizations here. Custom optimizations can be written by implementing the org.apache.hadoop.hive.ql.optimizer.Transform interface. This interface has one method that takes in a ParseContext object and returns another after the transformation. The ParseContext object has the current operator tree, among other information.

The following are the few optimizations that are already available with Hive 0.13.0:

  • ColumnPruner: This operator tree is walked to determine the minimal number of columns in the base table that are required to fulfill the query. Any additional columns in the base table are pruned away by inserting a SELECT statement when reading the base tables. This reduces the amount of data read, processed, and written.

  • GlobalLimitOptimizer: When a LIMIT operator is used in a query, this particular optimizer...

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