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Mastering Elastic Stack

Mastering Elastic Stack

By : Kumar Gupta, Gupta
1 (1)
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Mastering Elastic Stack

Mastering Elastic Stack

1 (1)
By: Kumar Gupta, Gupta

Overview of this book

Even structured data is useless if it can’t help you to take strategic decisions and improve existing system. If you love to play with data, or your job requires you to process custom log formats, design a scalable analysis system, and manage logs to do real-time data analysis, this book is your one-stop solution. By combining the massively popular Elasticsearch, Logstash, Beats, and Kibana, elastic.co has advanced the end-to-end stack that delivers actionable insights in real time from almost any type of structured or unstructured data source. If your job requires you to process custom log formats, design a scalable analysis system, explore a variety of data, and manage logs, this book is your one-stop solution. You will learn how to create real-time dashboards and how to manage the life cycle of logs in detail through real-life scenarios. This book brushes up your basic knowledge on implementing the Elastic Stack and then dives deeper into complex and advanced implementations of the Elastic Stack. We’ll help you to solve data analytics challenges using the Elastic Stack and provide practical steps on centralized logging and real-time analytics with the Elastic Stack in production. You will get to grip with advanced techniques for log analysis and visualization. Newly announced features such as Beats and X-Pack are also covered in detail with examples. Toward the end, you will see how to use the Elastic stack for real-world case studies and we’ll show you some best practices and troubleshooting techniques for the Elastic Stack.
Table of Contents (13 chapters)
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Searching and indexing performance

So far we have uncovered some of the best practices in terms of resources. As memory, CPU, I/O, disks, and network play a big part in choosing the preferred set of system configurations; we can tweak a few settings to improve resources usage for searching and indexing in Elasticsearch and Lucene.

Filter cache

By default, the filters used in Elasticsearch for querying are cached, which means when the query uses filter, Elasticsearch finds the documents related to the filter and stores the filter used as cache. After caching, if any query with the same filters are used it will provide quicker results as filters have been cached to memory. As internally it uses memory, it is wise to set a property to limit the usage of the Filter cache. Though each filter uses less memory, JVM heap size can take a hit if a large number of filters are used. By using the following property, we can limit the amount of Heap memory that can be used for the filter cache:

indices.cache...

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