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

Introducing search types in Elasticsearch


Elasticsearch provides the following search types to be executed:

  • query_then_fetch: This is the default search type available in Elasticsearch. It follows a two-phase search execution. In the first phase (query), the query goes to a coordinating node that further forwards the query to all the relevant shards. Each shard searches the documents, sorts them locally, and returns the results to the coordinating node. The coordinating node further merges all the results, sorts them, and returns the result to the caller. The final results are of the maximum size specified in the size parameter with the search request.

  • dfs_query_then_fetch: This is similar to the query_then_fetch search type, but asks Elasticsearch to do some extra processing for more accurate scoring of documents. In the fetch phase, all the shards compute the distributed term frequencies.

  • scan: The scan search type differs from normal search requests because it does not involve any scoring...