Open Source Log Analysis with Elasticsearch
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- Learn how to administer Elasticsearch to achieve clean and high performance applications
- Utilize data handling using analyzer, segment API, and garbage collectors
- This is a step-by-step guide on how to work with robust open source log analysis tools such as Elasticsearch, Logstash, and Kibana
Book DetailsLanguage : English
Paperback : 225 pages [ 235mm x 191mm ]
Release Date : October 2014
ISBN : 1783984384
ISBN 13 : 9781783984381
Author(s) : Surendra Mohan
Topics and Technologies : All Books, Big Data and Business Intelligence, Open Source
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What you will learn from this book
- Learn how to set up Elasticsearch, and master the Elasticsearch architecture as well as bootstrap and data communication processes
- Get to know about log analysis, the different types of logs, the various open source log analysis tools, and advantages of using such tools
- Review the different store and discovery types, various gateway modules, Segment API, as well as Elasticsearch caching and filtering
- Understand what a data analyzer is, and additionally about slow query and transaction logs, merge policies and schedulers, and segment merging
- Troubleshoot garbage collection problems and means to avoid swapping, along with learning about the Elasticsearch Java Virtual Machine's memory and garbage collection life-cycle,
- Implement Elasticsearch as a log analysis tool, and log analysis activities using Elasticsearch
- Understand and perform log analysis activities using Logstash and the Kibana dashboard
Simply setting up Elasticsearch isn’t enough these days, especially when you have got to fight for the top most rank of your web product in such a competitive world. Moreover, if you miss a day or two of monitoring or analyzing logs, the search ranking for a specific product or a set of products might drastically fall without any prior notice. Thus, capturing and analyzing logs on regular basis is one of the mandates in order to survive in such a competitive market.
Elasticsearch is a distributed search server similar to Apache Solr with a focus on large datasets, schemaless setup, and high availability. Utilizing the Apache Lucene library, Elasticsearch enables powerful full-text search as well as autocomplete, “morelikethis” search, and multilingual functionality, as well as an extensive search query DSL.
This book provides you with a number of clear, step-by-step exercises, and some unveiled concepts, which will help you explore and use the robust nature of Elasticsearch to the utmost extent so as to efficiently use open source log analysis tools in order to automate the log analysis process, hence boosting the performance and credibility in terms of its search ranking. The book starts with a general introduction and an overview of administrating Elasticsearch. Furthermore, you will learn ways to handle data, and play with the analyzer during indexing and searching. Next, you will move on to learn more about Java memory, and various ways to deal with problems that arise due to garbage collection. Additionally, you will learn about Elasticsearch, Kibana, and Logstash, and their installation process.
By the end of this book, you will be able to create and analyze log data on a Big Data scale, as well as visualize this data with Kibana and Logstash.
This is an easy-to-follow guide full of hands-on and real-world examples. Each topic is explained and demonstrated in a specific and user-friendly manner that enables aspiring Elasticsearch developers to understand this technology in depth.
Who this book is for
This book is aimed at developers who have prior experience working with Elasticsearch and who want to create their own logging and log analysis platform utilizing open source tools central to the Elasticsearch ecosystem.