Optimizing Hadoop for MapReduce : learn how to configure your Hadoop cluster to run optimal MapReduce jobs /

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This book is an example-based tutorial that deals with Optimizing Hadoop for MapReduce job performance. If you are a Hadoop administrator, developer, MapReduce user, or beginner, this book is the best choice available if you wish to optimize your clusters and applications. Having prior knowledge of...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Tannir, Khaled
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham, UK : Packt Pub., 2014.
Colección:Community experience distilled.
Temas:
Apache Hadoop.
Apache Hadoop
Electronic data processing > Distributed processing.
Cluster analysis > Data processing.
Traitement réparti.
Classification automatique (Statistique) > Informatique.
COMPUTERS > Computer Literacy.
COMPUTERS > Computer Science.
COMPUTERS > Data Processing.
COMPUTERS > Hardware > General.
COMPUTERS > Information Technology.
COMPUTERS > Machine Theory.
COMPUTERS > Reference.
Cluster analysis > Data processing
Electronic data processing > Distributed processing
Acceso en línea:Texto completo
Texto completo
Tabla de Contenidos:
  • Cover; Copyright; Credits; About the Author; Acknowledgments; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Understanding Hadoop MapReduce; The MapReduce model; Overview of Hadoop MapReduce; Hadoop MapReduce internals; Factors affecting the performance of MapReduce; Summary; Chapter 2: An Overview of the Hadoop Parameters; Investigating the Hadoop parameters; The mapred-site.xml configuration file; The CPU-related parameters; The disk I/O related parameters; The memory-related parameters; The network-related parameters; The hdfs-site.xml configuration file
  • The core-site.xml configuration fileHadoop MapReduce metrics; Performance monitoring tools; Using Chukwa to monitor Hadoop; Using Ganglia to monitor Hadoop; Using Nagios to monitor Hadoop; Using Apache Ambari to monitor Hadoop; Summary; Chapter 3: Detecting System Bottlenecks; Performance tuning; Creating a performance baseline; Identifying resource bottlenecks; Identifying RAM bottlenecks; Identifying CPU bottlenecks; Identifying storage bottlenecks; Identifying network bandwidth bottlenecks; Summary; Chapter 4: Identifying Resource Weaknesses; Identifying cluster weakness
  • Checking the Hadoop cluster node's healthChecking the input data size; Checking massive I/O and network traffic; Checking for insufficient concurrent tasks; Checking for CPU contention; Sizing your Hadoop cluster; Configuring your cluster correctly; Summary; Chapter 5: Enhancement of Map and Reduce Tasks; Enhancing Map tasks; Input data and block size impact; Dealing with small and unsplittable files; Reducing spilled records during the Map phase; Calculating map tasks' throughput; Enhancing Reduce tasks; Calculating reduce task throughput; Improving Reduce execution phase
  • Tuning map and reduce parametersSummary; Chapter 6: Optimizing MapReduce Tasks; Using Combiners; Using compression; Using appropriate Writable types; Reusing types smartly; Optimizing mappers and reducers code; Summary; Chapter 7: Best Practices and Recommendations; Hardware tuning and OS recommendations; Hadoop cluster checklists; The Bios tuning checklist; OS configuration recommendations; Hadoop best practices and recommendations; Deploying Hadoop; Hadoop tuning recommendations; Using a MapReduce template class code; Summary; Index

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