Dan Noble - Monitoring ElasticSearch
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- Understand common performance and reliability pitfalls in ElasticSearch
- Use popular monitoring tools such as ElasticSearch-head, BigDesk, Marvel, Kibana, and more
- This is a step-by-step guide with lots of case studies on solving real-world ElasticSearch cluster issues
ElasticSearch is a distributed search server similar to Apache Solr with a focus on large datasets, a schema-less setup, and high availability. This schema-free architecture allows ElasticSearch to index and search unstructured content, making it perfectly suited for both small projects and large big data warehouses with petabytes of unstructured data.
This book is your toolkit to teach you how to keep your cluster in good health, and show you how to diagnose and treat unexpected issues along the way. You will start by getting introduced to ElasticSearch, and look at some common performance issues that pop up when using the system. You will then see how to install and configure ElasticSearch and the ElasticSearch monitoring plugins. Then, you will proceed to install and use the Marvel dashboard to monitor ElasticSearch. You will find out how to troubleshoot some of the common performance and reliability issues that come up when using ElasticSearch. Finally, you will analyze your clusters historical performance, and get to know how to get to the bottom of and recover from system failures.
This book will guide you through several monitoring tools, and utilizes real-world cases and dilemmas faced when using ElasticSearch, showing you how to solve them simply, quickly, and cleanly.
What you will learn- Explore your cluster with ElasticSearch-head and BigDesk
- Access the underlying data of the ElasticSearch monitoring plugins using the ElasticSearch API
- Analyze your clusters performance with Marvel
- Troubleshoot some of the common performance and reliability issues that come up when using ElasticSearch
- Analyze a clusters historical performance, and get to the bottom of and recover from system failures
- Use and install various other tools and plugins such as Kibana and Kopf, which is helpful to monitor ElasticSearch
Dan Noble is a software engineer with a passion for writing secure, clean, and articulate code. He enjoys working with a variety of programming languages and software frameworks, particularly Python, Elasticsearch, and frontend technologies. Dan currently works on geospatial web applications and data processing systems.
Dan has been a user and advocate of Elasticsearch since 2011. He has given talks about Elasticsearch at various meetup groups, and is the author of the Python Elasticsearch client rawes. Dan was also a technical reviewer for the Elasticsearch Cookbook, Second Edition, by Alberto Paro.
Table of Contents- Introduction to Monitoring Elasticsearch
- Installation and the Requirements for Elasticsearch
- Elasticsearch-head and Bigdesk
- Marvel Dashboard
- System Monitoring
- Troubleshooting Performance and Reliability Issues
- Node Failure and Post-Mortem Analysis
- Looking Forward
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Copyright 2016 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: July 2016
Production reference: 1200716
Published by Packt Publishing Ltd.
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Birmingham B3 2PB, UK.
ISBN 978-1-78439-780-7
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Author
Dan Noble
Reviewers
James A. Cubeta
Joseph E McMahon
Parkhe Kishor B.
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Merint Mathew
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Cover Work
Melwyn Dsa
Dan Noble is a software engineer with a passion for writing secure, clean, and articulate code. He enjoys working with a variety of programming languages and software frameworks, particularly Python, Elasticsearch, and frontend technologies. Dan currently works on geospatial web applications and data processing systems.
Dan has been a user and advocate of Elasticsearch since 2011. He has given talks about Elasticsearch at various meetup groups, and is the author of the Python Elasticsearch client rawes . Dan was also a technical reviewer for the Elasticsearch Cookbook , Second Edition , by Alberto Paro.
I would like to thank my beautiful wife, Julie, for putting up with me while I wrote this book. Thanks for supporting me every step of the way.
I would also like to thank my friends and colleagues James Cubeta, Joe McMahon, and Mahmoud Lababidi, who shared their insight, time, and support. I would like to give a special thanks to Abe Usher you have been an incredible mentor over the years.
Finally, thanks to everyone at Packt Publishing for helping to make this book happen. A special thanks to Merint Mathew, Sonali Vernekar, Hussain Kanchwala, and Amey Varangaonkar for your valuable and careful feedback.
James A. Cubeta is a computer scientist with more than 20 years of experience, spanning government contracting, commercial companies, and research organizations. With a technical emphasis on data management, ETL, multi-tier application development, and relational and NoSQL databases, he has also helped author Java courses at Sun Microsystems and served as a technical reviewer for O'Reilly Media's extremely successful book Head First Java . He is currently a senior technologist at the HumanGeo Group, LLC.
Joseph E McMahon is a computer scientist with over 25 years of experience developing distributed systems with a focus on effective and performant messaging components. He has vast experience integrating geospatial solutions and data collection architectures. Currently, he leads a division in The HumanGeo Group supporting a variety of government customers. When not coding, he enjoys cooking, woodworking, and traveling with his wife and three children.
Parkhe Kishor B. received a BSc degree and an MTech degree in industrial mathematics and computer applications from the University of Pune. He joined High Mark Credit Information Service in 2012, where he worked as senior software engineer in the research and development department. He also has experience working in information retrieval, big data, and distributed computing. He is currently working as a design engineer at Introp Software Solutions, India. He is frequently a consultant to the industry in the area of big data, NoSQL, machine learning, artificial intelligence, and business intelligence.
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Welcome to Monitoring Elasticsearch !
There are many books and online tutorials that cover the Elasticsearch API and how to configure a cluster. But, until now, there hasn't been a thorough, accessible resource for monitoring and troubleshooting purposes. We've found that Elasticsearch monitoring tools drastically improve our ability to solve cluster issues and greatly increase cluster reliability and performance as a result. We wrote this book to share those use cases and the insights that came out of them.
This book covers how to use several popular open source and commercial Elasticsearch monitoring tools, namely, Elasticsearch-head, Bigdesk, Marvel, Kopf, and Kibana. There's also a section on the Elasticsearch cat API and how to use Nagios to perform general system monitoring. Moreover, we will discuss several case studies with real-world examples of troubleshooting Elasticsearch issues using these tools.
We believe that the best way to learn is to do. In this book, we'll go over how to set up a sample Elasticsearch cluster and load it with data. At times, we'll deliberately introduce problems into the cluster so that we can see how the errors are tracked using our various monitoring tools. Following along with these examples in your own cluster will help you learn both how to use the monitoring tools and how to tackle new and unknown issues that may arise.
After reading this book, we hope that you will be better equipped to run and maintain an Elasticsearch cluster. You will also be more prepared to diagnose and solve cluster issues, such as a node going down, the Elasticsearch process dying, configuration errors, shard errors,
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