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Programming MapReduce with Scalding

You're reading from   Programming MapReduce with Scalding A practical guide to designing, testing, and implementing complex MapReduce applications in Scala

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Product type Paperback
Published in Jun 2014
Publisher
ISBN-13 9781783287017
Length 148 pages
Edition 1st Edition
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Author (1):
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Antonios Chalkiopoulos Antonios Chalkiopoulos
Author Profile Icon Antonios Chalkiopoulos
Antonios Chalkiopoulos
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Table of Contents (11) Chapters Close

Preface 1. Introduction to MapReduce FREE CHAPTER 2. Get Ready for Scalding 3. Scalding by Example 4. Intermediate Examples 5. Scalding Design Patterns 6. Testing and TDD 7. Running Scalding in Production 8. Using External Data Stores 9. Matrix Calculations and Machine Learning Index

Logfile analysis

The results of this data-processing job will be displayed on a web application that presents on an interactive map, the geographic locations where users log in from. This web application will allow filtering data based on the device used.

Our job is to analyze 10 million rows of logs and generate such a report in a JSON file that can drive the web application. Because of the nature of the web application, the maximum size of that file should not exceed a few hundred kilobytes. The challenge here is how to manage data in such a way as to efficiently construct this report.

It is all about the data, and we will be using Scalding to start exploring. Around 10 million rows of data exist in tab-separated files in a Hadoop cluster in the location hdfs:///log-files/YYYY/MM/DD.

The TSV files contain nine columns of data. We discover that the 'activity column contains values such as login, readArticle, and streamVideo, and we are interested only in the login events. Also, if we...

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