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
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Chapter 2. Understanding Document Analysis and Creating Mappings

Search is hard, and it becomes harder when both speed and relevancy are required together. There are lots of configurable options Elasticsearch provides out-of-the-box to take control before you start putting the data into it. Elasticsearch is schemaless. I gave a brief idea in the previous chapter of why it is not completely schemaless and how it creates a schema right after indexing the very first document for all the fields existing in that document. However, the schema matters a lot for a better and more relevant search. Equally important is understanding the theory behind the phases of document indexing and search.

In this chapter, we will cover the following topics:

  • Full text search and inverted indices

  • Document analysis

  • Introducing Lucene analyzers

  • Creating custom analyzers

  • Elasticsearch mappings