This book is the perfect introduction to sophisticated concepts in MapReduce and will ensure you have the knowledge to optimize job performance. This is not an academic treatise; it's an example-driven tutorial for the real world.
MapReduce is the distribution system that the Hadoop MapReduce engine uses to distribute work around a cluster by working parallel on smaller data sets. It is useful in a wide range of applications, including distributed pattern-based searching, distributed sorting, web link-graph reversal, term-vector per host, web access log stats, inverted index construction, document clustering, machine learning, and statistical machine translation.
This book introduces you to advanced MapReduce concepts and teaches you everything from identifying the factors that affect MapReduce job performance to tuning the MapReduce configuration. Based on real-world experience, this book will help you to fully utilize your cluster's node resources to run MapReduce jobs optimally.
This book details the Hadoop MapReduce job performance optimization process. Through a number of clear and practical steps, it will help you to fully utilize your cluster's node resources.
Starting with how MapReduce works and the factors that affect MapReduce performance, you will be given an overview of Hadoop metrics and several performance monitoring tools. Further on, you will explore performance counters that help you identify resource bottlenecks, check cluster health, and size your Hadoop cluster. You will also learn about optimizing map and reduce tasks by using Combiners and compression.
The book ends with best practices and recommendations on how to use your Hadoop cluster optimally.
What You Will Learn
- Learn about the factors that affect MapReduce performance
- Utilize the Hadoop MapReduce performance counters to identify resource bottlenecks
- Size your Hadoop cluster's nodes
- Set the number of mappers and reducers correctly
- Optimize mapper and reducer task throughput and code size using compression and Combiners
- Understand the various tuning properties and best practices to optimize clusters
Table of contents
Optimizing Hadoop for MapReduce
- Table of Contents
- Optimizing Hadoop for MapReduce
- About the Author
- About the Reviewers
- 1. Understanding Hadoop MapReduce
2. An Overview of the Hadoop Parameters
- Investigating the Hadoop parameters
- Hadoop MapReduce metrics
- Performance monitoring tools
- 3. Detecting System Bottlenecks
- 4. Identifying Resource Weaknesses
5. Enhancing Map and Reduce Tasks
- Enhancing map tasks
- Enhancing reduce tasks
- Tuning map and reduce parameters
- 6. Optimizing MapReduce Tasks
- 7. Best Practices and Recommendations
- Title: Optimizing Hadoop for MapReduce
- Release date: February 2014
- Publisher(s): Packt Publishing
- ISBN: 9781783285655
You might also like
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. …
If you are ready to dive into the MapReduce framework for processing large datasets, this practical …
Designing Data-Intensive Applications
Data is at the center of many challenges in system design today. Difficult issues need to …
Go is rapidly becoming the preferred language for building web services. There are plenty of tutorials …