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Hadoop Real-World Solutions Cookbook- Second Edition

You're reading from   Hadoop Real-World Solutions Cookbook- Second Edition Over 90 hands-on recipes to help you learn and master the intricacies of Apache Hadoop 2.X, YARN, Hive, Pig, Oozie, Flume, Sqoop, Apache Spark, and Mahout

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Product type Paperback
Published in Mar 2016
Publisher
ISBN-13 9781784395506
Length 290 pages
Edition 2nd Edition
Tools
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Author (1):
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Tanmay Deshpande Tanmay Deshpande
Author Profile Icon Tanmay Deshpande
Tanmay Deshpande
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Toc

Table of Contents (12) Chapters Close

Preface 1. Getting Started with Hadoop 2.X FREE CHAPTER 2. Exploring HDFS 3. Mastering Map Reduce Programs 4. Data Analysis Using Hive, Pig, and Hbase 5. Advanced Data Analysis Using Hive 6. Data Import/Export Using Sqoop and Flume 7. Automation of Hadoop Tasks Using Oozie 8. Machine Learning and Predictive Analytics Using Mahout and R 9. Integration with Apache Spark 10. Hadoop Use Cases Index

Writing the Map Reduce program in Java to analyze web log data

In this recipe, we are going to take a look at how to write a map reduce program to analyze web logs. Web logs are data that is generated by web servers for requests they receive. There are various web servers such as Apache, Nginx, Tomcat, and so on. Each web server logs data in a specific format. In this recipe, we are going to use data from the Apache Web Server, which is in combined access logs.

Note

To read more on combined access logs, refer to

http://httpd.apache.org/docs/1.3/logs.html#combined.

Getting ready

To perform this recipe, you should already have a running Hadoop cluster as well as an eclipse similar to an IDE.

How to do it...

We can write map reduce programs to analyze various aspects of web log data. In this recipe, we are going to write a map reduce program that reads a web log file, results pages, views, and their counts. Here is some sample web log data we'll consider as input for our program:

106.208.17...
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