Data
Reader small image

You're reading from  Optimizing Hadoop for MapReduce

Product typeBook
Published inFeb 2014
Publisher
ISBN-139781783285655
Edition1st Edition
Tools
Hadoop
Concepts
Data Processing
Right arrow
Author (1)
Khaled Tannir
Khaled Tannir
author image
Khaled Tannir

Khaled Tannir has been working with computers since 1980. He began programming with the legendary Sinclair Zx81 and later with Commodore home computer products (Vic 20, Commodore 64, Commodore 128D, and Amiga 500). He has a Bachelor's degree in Electronics, a Master's degree in System Information Architectures, in which he graduated with a professional thesis, and completed his education with a Master of Research degree. He is a Microsoft Certified Solution Developer (MCSD) and has more than 20 years of technical experience leading the development and implementation of software solutions and giving technical presentations. He now works as an independent IT consultant and has worked as an infrastructure engineer, senior developer, and enterprise/solution architect for many companies in France and Canada. With significant experience in Microsoft .Net, Microsoft Server Systems, and Oracle Java technologies, he has extensive skills in online/offline applications design, system conversions, and multilingual applications in both domains: Internet and Desktops. He is always researching new technologies, learning about them, and looking for new adventures in France, North America, and the Middle-east. He owns an IT and electronics laboratory with many servers, monitors, open electronic boards such as Arduino, Netduino, RaspBerry Pi, and .Net Gadgeteer, and some smartphone devices based on Windows Phone, Android, and iOS operating systems. In 2012, he contributed to the EGC 2012 (International Complex Data Mining forum at Bordeaux University, France) and presented, in a workshop session, his work on "how to optimize data distribution in a cloud computing environment". This work aims to define an approach to optimize the use of data mining algorithms such as k-means and Apriori in a cloud computing environment. He is the author of RavenDB 2.x Beginner's Guide, Packt Publishing. He aims to get a PhD in Cloud Computing and Big Data and wants to learn more and more about these technologies. He enjoys taking landscape and night time photos, travelling, playing video games, creating funny electronic gadgets with Arduino/.Net Gadgeteer, and of course, spending time with his wife and family. You can reach him at contact@khaledtannir.net.
Read more about Khaled Tannir

Right arrow

Chapter 3. Detecting System Bottlenecks

How do you know whether your Hadoop MapReduce job is performing its work optimally? One of the most common performance-related requests we receive in our consulting practice is to find out why a specific job took a long time to execute, and to troubleshoot bottleneck incidents.

In Chapter 1, Understanding Hadoop MapReduce, and Chapter 2, An Overview of the Hadoop Parameters, we learned about factors that may impact Hadoop MapReduce performance and Hadoop MapReduce common parameters' settings. In this chapter, we will continue our journey and learn how to detect potential system bottlenecks.

This chapter presents the performance tuning process, the importance of creating a baseline before any tuning job, and how to use this baseline to tune your cluster. You will also learn how to identify resource bottlenecks and what to do in order to break these bottlenecks.

In this chapter, we will cover the following:

  • Introducing the performance tuning process

  • Creating...

Performance tuning

The fundamental goal of performance tuning is to ensure that all available resources (CPU, RAM, I/O, and network) in a given cluster configuration are available to a particular job and are used in a balanced way.

Hadoop MapReduce resources are classified into categories such as computation, memory, network bandwidth, and input/output storage. If any of these resources perform badly, this will impact Hadoop's performance, which may cause your jobs to run slowly. Therefore, tuning Hadoop MapReduce performance is getting balanced resources on your Hadoop cluster and not just tuning one or more variables.

In simple words, tuning a Hadoop MapReduce job process consists of multiple analyses that investigate the Hadoop metrics and indicators in order to learn about execution time, memory amount used, and the number of bytes to read or store in the local filesystem, and so on.

Hadoop performance tuning is an iterative process. You launch a job, then analyzes Hadoop counters, adjust...

Creating a performance baseline

Let's begin by creating a performance baseline for our system. When creating a baseline, you should keep the Hadoop default configuration settings and use the TeraSort benchmark tool, which is a part of the example JAR files provided with the Hadoop distribution package. TeraSort is accepted as an industry standard benchmark to compare the performance of Hadoop. This benchmark tool tries to use the entire Hadoop cluster to sort 1 TB of data as quickly as possible and is divided into three main modules:

  • TeraGen: This module is used to generate a file of the desired size as an input that usually ranges between 500 GB up to 3 TB. Once the input data is generated by TeraGen, it can be used in all the runs with the same file size.

  • TeraSort: This module will sort the input file across the Hadoop cluster. TeraSort stresses the Hadoop cluster on both layers, MapReduce and HDFS, in terms of computation, network bandwidth, and I/O response. Once sorted, a single reduced...

Identifying resource bottlenecks

Typically, a bottleneck occurs when one resource of the system consumes more time than required to finish its tasks and forces other resources to wait, which decreases the overall system performance.

Prior to any deep-dive action into tuning your Hadoop cluster, it is a good practice to ensure that your cluster is stable and your MapReduce jobs are operational. We suggest you verify that the hardware components of your cluster are configured correctly and if necessary, upgrade any software components of the Hadoop stack to the latest stable version. You may also perform a MapReduce job such as TeraSort or PI Estimator to stress your cluster. This is a very important step to get a healthy and optimized Hadoop cluster.

Once your cluster hardware and software components are well configured and updated, you need to create a baseline performance stress-test. In order to stress your Hadoop cluster, you can use the Hadoop microbenchmarks such as TeraSort, TestDFSIO...

Summary

In this chapter, we introduced the performance tuning process cycle and learned about Hadoop counters. We covered the TeraSort benchmark with its TeraGen module and learned to generate a performance baseline that will be used as a reference when tuning the Hadoop cluster. We also learned the approach to tune a Hadoop cluster illustrated by a three-node Hadoop cluster example and suggested tuning some setting parameters to improve the cluster's performance.

Then we moved ahead and discussed resource bottlenecks and what component is involved at each MapReduce stage or may potentially be the source of a bottleneck. For each component (CPU, RAM, storage, and network bandwidth), we learned how to identify system bottlenecks with suggestions to try to eliminate them.

In the next chapter, we will learn how to identify Hadoop cluster resource weaknesses and how to configure a Hadoop cluster correctly. Keep reading!

lock icon
The rest of the chapter is locked
Previous Chapter
Chapter 10 of 15
Next Chapter
You have been reading a chapter from
Optimizing Hadoop for MapReduce
Published in: Feb 2014Publisher: ISBN-13: 9781783285655
© 2014 Packt Publishing Limited All Rights Reserved
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.
Sign up now
undefined
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
Start free trial
Renews at $15.99/month. Cancel anytime

Author (1)

author image
Khaled Tannir

Khaled Tannir has been working with computers since 1980. He began programming with the legendary Sinclair Zx81 and later with Commodore home computer products (Vic 20, Commodore 64, Commodore 128D, and Amiga 500). He has a Bachelor's degree in Electronics, a Master's degree in System Information Architectures, in which he graduated with a professional thesis, and completed his education with a Master of Research degree. He is a Microsoft Certified Solution Developer (MCSD) and has more than 20 years of technical experience leading the development and implementation of software solutions and giving technical presentations. He now works as an independent IT consultant and has worked as an infrastructure engineer, senior developer, and enterprise/solution architect for many companies in France and Canada. With significant experience in Microsoft .Net, Microsoft Server Systems, and Oracle Java technologies, he has extensive skills in online/offline applications design, system conversions, and multilingual applications in both domains: Internet and Desktops. He is always researching new technologies, learning about them, and looking for new adventures in France, North America, and the Middle-east. He owns an IT and electronics laboratory with many servers, monitors, open electronic boards such as Arduino, Netduino, RaspBerry Pi, and .Net Gadgeteer, and some smartphone devices based on Windows Phone, Android, and iOS operating systems. In 2012, he contributed to the EGC 2012 (International Complex Data Mining forum at Bordeaux University, France) and presented, in a workshop session, his work on "how to optimize data distribution in a cloud computing environment". This work aims to define an approach to optimize the use of data mining algorithms such as k-means and Apriori in a cloud computing environment. He is the author of RavenDB 2.x Beginner's Guide, Packt Publishing. He aims to get a PhD in Cloud Computing and Big Data and wants to learn more and more about these technologies. He enjoys taking landscape and night time photos, travelling, playing video games, creating funny electronic gadgets with Arduino/.Net Gadgeteer, and of course, spending time with his wife and family. You can reach him at contact@khaledtannir.net.
Read more about Khaled Tannir

Personalised recommendations for you

Based on your interests and search pattern

Et al.

Et al.

Ever wonder why speech recognition systems don't understand the Scottish accent, or what would happen if an astronaut only ate mac 'n' cheese, or other spurious reflections you'd have at a bar? We did, then collated those deliberations into absurd research articles with fake figures and methodologies inspired by even more fictionally absurd studies.

BookAug 2023230 pages5
Generative AI with LangChain

Generative AI with LangChain

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

BookDec 2023360 pages4
Generative AI with LangChain

Generative AI with LangChain

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

BookDec 2023360 pages5
Generative AI with LangChain

Generative AI with LangChain

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

BookDec 2023360 pages1
Generative AI with LangChain

Generative AI with LangChain

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

BookDec 2023360 pages5
Mastering Tableau 2023

Mastering Tableau 2023

This book is a comprehensive resource to mastering your Tableau skills and becoming a BI expert. As you progress, you will learn how to build advanced dashboards and improve your storytelling to derive key business insight, as well as make you well-versed with advanced functionalities of Tableau in the business intelligence domain.

BookAug 2023684 pages
Building AI Applications with ChatGPT APIs

Building AI Applications with ChatGPT APIs

This guide covers all ChatGPT API features for effortless creation of robust AI powered apps. With its help, you’ll be able to leverage ChatGPT’s cutting-edge NLP models to take your app development skills to the next level. You’ll also work on ten exciting projects that will give you the practical know-how that you can apply to your existing applications.

BookSep 2023258 pages5
Building AI Applications with ChatGPT APIs

Building AI Applications with ChatGPT APIs

This guide covers all ChatGPT API features for effortless creation of robust AI powered apps. With its help, you’ll be able to leverage ChatGPT’s cutting-edge NLP models to take your app development skills to the next level. You’ll also work on ten exciting projects that will give you the practical know-how that you can apply to your existing applications.

BookSep 2023258 pages2
Data Engineering with AWS

Data Engineering with AWS

Embark on a journey to master data engineering pipelines on AWS! Our book offers a hands-on experience of AWS services for ingesting, transforming, and consuming data. Whether you're an absolute beginner or someone with basic data engineering experience, this guide is an indispensable resource.

BookOct 2023636 pages5
Modern Data Architecture on AWS

Modern Data Architecture on AWS

Every organization wants an agile, performant, and cost-effective data platform that meets all their current and future business needs. Purpose-built AWS analytics services and their features play a big part in building such a modern data platform. This book brings to you all the design and architectural patterns that’ll help you achieve this goal.

BookAug 2023420 pages5
Practical Guide to Applied Conformal Prediction in Python

Practical Guide to Applied Conformal Prediction in Python

Discover the power of Conformal Prediction with the "Practical Guide to Applied Conformal Prediction in Python." Master the latest techniques to quantify uncertainty in machine learning and computer vision models, and seamlessly apply them to your industry applications.

BookDec 2023240 pages
TinyML Cookbook

TinyML Cookbook

With over 70 project-based recipes, the TinyML Cookbook is a practical guide that will help you to get the most out of your microcontrollers. It provides a comprehensive understanding of the theoretical foundations while giving you hands-on experience training ML models for deployment on Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis Nano microcontrollers.

BookNov 2023664 pages