- 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 cluster’s 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 cluster’s performance with Marvel
- Troubleshoot some of the common performance and reliability issues that come up when using ElasticSearch
- Analyze a cluster’s 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
About the Author
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