Book Image

Mastering Elasticsearch 5.x - Third Edition

By : Bharvi Dixit
Book Image

Mastering Elasticsearch 5.x - Third Edition

By: Bharvi Dixit

Overview of this book

Elasticsearch is a modern, fast, distributed, scalable, fault tolerant, and open source search and analytics engine. Elasticsearch leverages the capabilities of Apache Lucene, and provides a new level of control over how you can index and search even huge sets of data. This book will give you a brief recap of the basics and also introduce you to the new features of Elasticsearch 5. We will guide you through the intermediate and advanced functionalities of Elasticsearch, such as querying, indexing, searching, and modifying data. We’ll also explore advanced concepts, including aggregation, index control, sharding, replication, and clustering. We’ll show you the modules of monitoring and administration available in Elasticsearch, and will also cover backup and recovery. You will get an understanding of how you can scale your Elasticsearch cluster to contextualize it and improve its performance. We’ll also show you how you can create your own analysis plugin in Elasticsearch. By the end of the book, you will have all the knowledge necessary to master Elasticsearch and put it to efficient use.
Table of Contents (20 chapters)
Mastering Elasticsearch 5.x - Third Edition
About the Author
About the Reviewer
Customer Feedback

Chapter 4. Data Modeling and Analytics

In the previous chapter, we discussed searching across different fields with the help of different variants of multimatch queries, then we went through one of the most powerful features of Elasticsearch: function score queries, which give more power to the user for controlling document relevancy by using custom scores. Finally, we covered the scripting module of Elasticsearch in detail. In this chapter, we will see how we can deal with the general problems of structuring data in Elasticsearch and the different data modeling techniques. We will also discuss the aggregation module of Elasticsearch for data analytics purposes. By the end of this chapter, we will have covered the following topics:

  • Data modeling techniques in Elasticsearch

  • Managing relational data in Elasticsearch using parent-child and nested types

  • Data analytics using aggregations

  • The new aggregation category: Matrix aggregation