Optimizing Hadoop for MapReduce

Learn how to configure your Hadoop cluster to run optimal MapReduce jobs Overview Optimize your MapReduce job performance Identify your Hadoop cluster's weaknesses Tune your MapReduce configuration In Detail 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 from this book 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 us