Book Image

Advanced Elasticsearch 7.0

By : Wai Tak Wong
Book Image

Advanced Elasticsearch 7.0

By: Wai Tak Wong

Overview of this book

Building enterprise-grade distributed applications and executing systematic search operations call for a strong understanding of Elasticsearch and expertise in using its core APIs and latest features. This book will help you master the advanced functionalities of Elasticsearch and understand how you can develop a sophisticated, real-time search engine confidently. In addition to this, you'll also learn to run machine learning jobs in Elasticsearch to speed up routine tasks. You'll get started by learning to use Elasticsearch features on Hadoop and Spark and make search results faster, thereby improving the speed of query results and enhancing the customer experience. You'll then get up to speed with performing analytics by building a metrics pipeline, defining queries, and using Kibana for intuitive visualizations that help provide decision-makers with better insights. The book will later guide you through using Logstash with examples to collect, parse, and enrich logs before indexing them in Elasticsearch. By the end of this book, you will have comprehensive knowledge of advanced topics such as Apache Spark support, machine learning using Elasticsearch and scikit-learn, and real-time analytics, along with the expertise you need to increase business productivity, perform analytics, and get the very best out of Elasticsearch.
Table of Contents (25 chapters)
Free Chapter
1
Section 1: Fundamentals and Core APIs
8
Section 2: Data Modeling, Aggregations Framework, Pipeline, and Data Analytics
13
Section 3: Programming with the Elasticsearch Client
16
Section 4: Elastic Stack
20
Section 5: Advanced Features

Mapping parameters

The mapping parameters define the ways to store the document fields, the methodologies and options to index, the information of the fields to expose, the additional procedure to perform, and how the mapped data is analyzed during index and search. We will endeavor to explain all the supported mapping parameters in the following tables:

Mapping parameter

Description

Example

How it stores

store

Whether to store the field value. The default value is false.

"field_1":{"type":"type_1", "store":true}

doc_values

It stores the same values as _source, but in a column-oriented manner. It is designed for sorting and aggregations. The default value is true.

"field_1":{"type":"text", "doc_values":false}

term_vector

Whether to store the information of the terms...