Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletter Hub
Free Learning
Arrow right icon
timer SALE ENDS IN
0 Days
:
00 Hours
:
00 Minutes
:
00 Seconds
Arrow up icon
GO TO TOP
Mastering Hadoop

You're reading from   Mastering Hadoop Go beyond the basics and master the next generation of Hadoop data processing platforms

Arrow left icon
Product type Paperback
Published in Dec 2014
Publisher
ISBN-13 9781783983643
Length 374 pages
Edition 1st Edition
Tools
Arrow right icon
Author (1):
Arrow left icon
 Karanth Karanth
Author Profile Icon Karanth
Karanth
Arrow right icon
View More author details
Toc

Table of Contents (21) Chapters Close

Mastering Hadoop
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
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 Hadoop for Microsoft Windows Index

MapReduce job counters


Counters are entities that can collect statistics at a job level. They can help in quality control, performance monitoring, and problem identification in Hadoop MapReduce jobs. Since they are global in nature, unlike logs, they need not be aggregated to be analyzed. Counters are grouped into logical groups using the CounterGroup class. There are sets of built-in counters for each MapReduce job.

The following example illustrates the creation of simple custom counters to categorize lines into lines having zero words, lines with less than or equal to five words, and lines with more than five words. The program when run on the grant proposal subset files gives the following output:

14/04/13 23:27:00 INFO mapreduce.Job: Counters: 23
    File System Counters
        FILE: Number of bytes read=446021466
        FILE: Number of bytes written=114627807
        FILE: Number of read operations=0
        FILE: Number of large read operations=0
        FILE: Number of write operations...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $15.99/month. Cancel anytime
Visually different images