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Mastering Elastic Stack

You're reading from   Mastering Elastic Stack Dive into data analysis with a pursuit of mastering ELK Stack on real-world scenarios.

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Product type Paperback
Published in Feb 2017
Publisher Packt
ISBN-13 9781786460011
Length 526 pages
Edition 1st Edition
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Authors (2):
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Ravi Kumar Gupta Ravi Kumar Gupta
Author Profile Icon Ravi Kumar Gupta
Ravi Kumar Gupta
Yuvraj Gupta Yuvraj Gupta
Author Profile Icon Yuvraj Gupta
Yuvraj Gupta
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Toc

Table of Contents (13) Chapters Close

Preface 1. Elastic Stack Overview FREE CHAPTER 2. Stepping into Elasticsearch 3. Exploring Logstash and Its Plugins 4. Kibana Interface 5. Using Beats 6. Elastic Stack in Action 7. Customizing Elastic Stack 8. Elasticsearch APIs 9. X-Pack: Security and Monitoring 10. X-Pack: Alerting, Graph, and Reporting 11. Best Practices 12. Case Study-Meetup

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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