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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

Compiling Pig scripts


The Pig architecture is layered to facilitate pluggable execution engines. Hadoop's MapReduce is an execution platform that is plugged into Pig. There are three main phases when compiling and executing a Pig script: preparing the logical plan, transforming it into a physical plan, and finally, compiling the physical plan into a MapReduce plan that can be executed in the appropriate execution environment.

The logical plan

The Pig statements are first parsed for syntax errors. Validation of the input files and input data structures happens during parsing. Type checking in the presence of a schema is done during this phase. A logical plan, a DAG of operators as nodes, and data flow as edges are then prepared. The logical plan cannot be executed and is agnostic of the execution layer. Optimizations based on in-built rules happen at this stage. Some of these rules are discussed later in the chapter. The logical plan has a one-to-one correspondence with the operators available...

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