Book Image

Learning Elasticsearch

By : Abhishek Andhavarapu
Book Image

Learning Elasticsearch

By: Abhishek Andhavarapu

Overview of this book

Elasticsearch is a modern, fast, distributed, scalable, fault tolerant, and open source search and analytics engine. You can use Elasticsearch for small or large applications with billions of documents. It is built to scale horizontally and can handle both structured and unstructured data. Packed with easy-to- follow examples, this book will ensure you will have a firm understanding of the basics of Elasticsearch and know how to utilize its capabilities efficiently. You will install and set up Elasticsearch and Kibana, and handle documents using the Distributed Document Store. You will see how to query, search, and index your data, and perform aggregation-based analytics with ease. You will see how to use Kibana to explore and visualize your data. Further on, you will learn to handle document relationships, work with geospatial data, and much more, with this easy-to-follow guide. Finally, you will see how you can set up and scale your Elasticsearch clusters in production environments.
Table of Contents (11 chapters)
10
Exploring Elastic Stack (Elastic Cloud, Security, Graph, and Alerting)

Searching for same value across multiple fields

The multi_match query is used to match the same value across multiple fields. When a user searches for biking jacket, searching just the product_name field might not find any matches. To widen the search, we should most probably also search the description field along with the product_name field. The document that contains both biking and jacket is shown here:

 {
"product_name": "Men's Water Resistant Jacket",
"description": "Provides comfort during biking and hiking",
"unit_price": 69.99,
"reviews": 5,
"release_date": "2017-03-02"
}

Scoring based on a single field is pretty straightforward, scoring based on multiple fields gets tricky. We can't use a match query with an operator as the terms biking and jacket don't exist in...