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Mastering Elasticsearch 5.x

You're reading from   Mastering Elasticsearch 5.x Master the intricacies of Elasticsearch 5 and use it to create flexible and scalable search solutions

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Product type Paperback
Published in Feb 2017
Publisher Packt
ISBN-13 9781786460189
Length 428 pages
Edition 3rd Edition
Languages
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Author (1):
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Bharvi Dixit Bharvi Dixit
Author Profile Icon Bharvi Dixit
Bharvi Dixit
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Table of Contents (13) Chapters Close

Preface 1. Revisiting Elasticsearch and the Changes FREE CHAPTER 2. The Improved Query DSL 3. Beyond Full Text Search 4. Data Modeling and Analytics 5. Improving the User Search Experience 6. The Index Distribution Architecture 7. Low-Level Index Control 8. Elasticsearch Administration 9. Data Transformation and Federated Search 10. Improving Performance 11. Developing Elasticsearch Plugins 12. Introducing Elastic Stack 5.0

Scaling Elasticsearch

As we already know, Elasticsearch is a highly scalable search and analytics platform. We can scale it both horizontally and vertically.

Vertical scaling

When we talk about vertical scaling, we often mean adding more resources to the server Elasticsearch is running on; we can add memory and we can switch to a machine with better CPU or faster disk storage. Of course, with better machines, we can expect increase in performance; depending on our deployment and its bottleneck, there can be smaller or higher improvement. However, there are limitations when it comes to vertical scaling. For example, one is the maximum amount of physical memory available for your servers or the total memory required by the JVM to operate. When you have large enough data and complicated queries, you can very soon run into memory issues, and adding new memory may not be helpful at all.

For example, you may not want to go beyond 31 GB of physical memory given to the JVM because of garbage collection...

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