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Elasticsearch Essentials

Elasticsearch Essentials

By : Bharvi Dixit
4.3 (6)
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Elasticsearch Essentials

Elasticsearch Essentials

4.3 (6)
By: Bharvi Dixit

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 (12 chapters)
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11
Index

The Elasticsearch out-of-the-box tools

Elasticsearch primarily works with two models of information retrieval: the Boolean model and the Vector Space model. In addition to these, there are other scoring algorithms available in Elasticsearch as well, such as Okapi BM25, Divergence from Randomness (DFR), and Information Based (IB). Working with these three models requires extensive mathematical knowledge and needs some extra configurations in Elasticsearch, which are beyond the scope of this book.

The Boolean model uses the AND, OR, and NOT conditions in a query to find all the matching documents. This Boolean model can be further combined with the Lucene scoring formula, TF/IDF (which we have already discussed in Chapter 2, Understanding Document Analysis and Creating Mappings), to rank documents.

The vector space model works differently from the Boolean model, as it represents both queries and documents as vectors. In the vector space model, each number in the vector is the weight of a term...

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