Book Image

Elasticsearch Server - Third Edition

By : Rafal Kuc
Book Image

Elasticsearch Server - Third Edition

By: Rafal Kuc

Overview of this book

ElasticSearch is a very fast and scalable open source search engine, designed with distribution and cloud in mind, complete with all the goodies that Apache Lucene has to offer. ElasticSearch’s schema-free architecture allows developers to index and search unstructured content, making it perfectly suited for both small projects and large big data warehouses, even those with petabytes of unstructured data. This book will guide you through the world of the most commonly used ElasticSearch server functionalities. You’ll start off by getting an understanding of the basics of ElasticSearch and its data indexing functionality. Next, you will see the querying capabilities of ElasticSearch, followed by a through explanation of scoring and search relevance. After this, you will explore the aggregation and data analysis capabilities of ElasticSearch and will learn how cluster administration and scaling can be used to boost your application performance. You’ll find out how to use the friendly REST APIs and how to tune ElasticSearch to make the most of it. By the end of this book, you will have be able to create amazing search solutions as per your project’s specifications.
Table of Contents (18 chapters)
Elasticsearch Server Third Edition
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
Index

Elasticsearch caches


Until now we haven't mentioned Elasticsearch caches much in the book. However, as most common systems Elasticsearch users a variety of caches to perform more complicated operations or to speed up performance of heavy data retrieval from disk based Lucene indices. In this section, we will look at the most common caches of Elasticsearch, what they are used for, what are the performance implications of using them, and how to configure them.

Fielddata cache

In the beginning of the book, we discussed that Elasticsearch uses the so called inverted index data structure to quickly and efficiently search through the documents. This is very good when searching and filtering the data, but for features such as aggregations, sorting, or script usage, Elasticsearch needs an un-inverted data structure, because these functions rely on per document data information.

Because of the need for uninverted data, when Elasticsearch was first released it contained and still contains an in memory...