Book Image

Elasticsearch Essentials

Book Image

Elasticsearch Essentials

Overview of this book

With constantly evolving and growing datasets, organizations have the need to find actionable insights for their business. ElasticSearch, which is the world's most advanced search and analytics engine, brings the ability to make massive amounts of data usable in a matter of milliseconds. It not only gives you the power to build blazing fast search solutions over a massive amount of data, but can also serve as a NoSQL data store. This guide will take you on a tour to become a competent developer quickly with a solid knowledge level and understanding of the ElasticSearch core concepts. Starting from the beginning, this book will cover these core concepts, setting up ElasticSearch and various plugins, working with analyzers, and creating mappings. This book provides complete coverage of working with ElasticSearch using Python and performing CRUD operations and aggregation-based analytics, handling document relationships in the NoSQL world, working with geospatial data, and taking data backups. Finally, we’ll show you how to set up and scale ElasticSearch clusters in production environments as well as providing some best practices.
Table of Contents (18 chapters)
Elasticsearch Essentials
Credits
About the Author
Acknowledgments
About the Reviewer
www.PacktPub.com
Preface
Index

Summary


In this chapter, we learned about one of the most powerful features of Elasticsearch, that is, aggregation frameworks. We went through the most important metric and bucket aggregations along with examples of doing analytics on our Twitter dataset with Python and Java API.

This chapter covered many fundamental as well complex examples of the different facets of analytics, which can be built using a combination of full-text searches, term-based searches, and multilevel aggregations. Elasticsearch is awesome for analytics but one should always keep in mind the memory implications, which we covered in the last section of this chapter, to avoid the over killing of nodes.

In the next chapter, we will learn to work with geo spatial data in Elasticsearch and we will also cover analytics with geo aggregations.