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Python Essentials

You're reading from   Python Essentials Modernize existing Python code and plan code migrations to Python using this definitive guide

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Product type Paperback
Published in Jun 2015
Publisher Packt
ISBN-13 9781784390341
Length 298 pages
Edition 1st Edition
Languages
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Author (1):
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Steven F. Lott Steven F. Lott
Author Profile Icon Steven F. Lott
Steven F. Lott
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Toc

Table of Contents (17) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Simple Data Types 3. Expressions and Output 4. Variables, Assignment and Scoping Rules 5. Logic, Comparisons, and Conditions 6. More Complex Data Types 7. Basic Function Definitions 8. More Advanced Functions 9. Exceptions 10. Files, Databases, Networks, and Contexts 11. Class Definitions 12. Scripts, Modules, Packages, Libraries, and Applications 13. Metaprogramming and Decorators 14. Fit and Finish – Unit Testing, Packaging, and Documentation 15. Next Steps Index

Plugging into a MapReduce framework

For background on the Apache Hadoop server, see https://hadoop.apache.org. Here's the summary:

The Apache Hadoop software library is a framework that allows for the distributed processing of large datasets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

One part of the Hadoop distributed processing is the MapReduce module. This module allows us to decompose analysis of data into two complementary operations: map and reduce. These operations are distributed around the Hadoop cluster to be run concurrently. A map operation processes all of the rows of datasets that are scattered around the cluster. The outputs from map operations are then fed to reduce operations to be summarized.

The Hadoop streaming interface can be used by Python programmers. This involves a Hadoop "wrapper" that will present the data to a Python...

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