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.

Preface

What this book covers

What you need for this book

Who this book is for

Reader feedback

Customer support

Free Chapter

Introduction to Elasticsearch

Introduction to Elasticsearch

Basic concepts of Elasticsearch

Interacting with Elasticsearch

How does search work?

Scalability and availability

Setting Up Elasticsearch and Kibana

Setting Up Elasticsearch and Kibana

Installing Elasticsearch

Installing Kibana

Query format used in this book (Kibana Console)

Using cURL or Postman

Health of the cluster

Modeling Your Data and Document Relations

Modeling Your Data and Document Relations

Difference between full-text search and exact match

Core data types

Complex data types

Specialized data type

Mapping the same field with different mappings

Handling relations between different document types

Indexing and Updating Your Data

Indexing and Updating Your Data

Indexing your data

Updating your data

Using Kibana to discover

Using Elasticsearch in your application

Primary and Replica shards

Organizing Your Data and Bulk Data Ingestion

Organizing Your Data and Bulk Data Ingestion

Bulk operations

Organizing your data

All About Search

All About Search

Different types of queries

Querying Elasticsearch

Searching for same value across multiple fields

More Than a Search Engine (Geofilters, Autocomplete, and More)

More Than a Search Engine (Geofilters, Autocomplete, and More)

Correcting typos and spelling mistakes

Making suggestions based on the user input

Handling document relations using parent-child

Handling document relations using nested

Reverse search using the percolate query

Geo and Spatial Filtering

Search templates

Querying Elasticsearch from Java application

How to Slice and Dice Your Data Using Aggregations

How to Slice and Dice Your Data Using Aggregations

Aggregation basics

Types of aggregations

Using Kibana to visualize aggregations

Production and Beyond

Production and Beyond

Configuring Elasticsearch

Multinode cluster

How nodes discover each other

Elasticsearch server logs

Exploring Elastic Stack (Elastic Cloud, Security, Graph, and Alerting)

Exploring Elastic Stack (Elastic Cloud, Security, Graph, and Alerting)

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Graph

In this section, we will discuss Graph, which is part of the X-Pack Gold and Platinum subscription. Graph lets you discover and analyze relationships in your data. It works on your existing indexes and doesn't require any special configuration. The Graph has two components:

The functionality required for Elasticsearch to compute the Graph.
The UI in Kibana to visualize the graphical representation.

To better explain the functionality of Graph, let's build a recommendation system for an online store. We want to know the relations between items frequently bought together and use that information to make suggestions to the users. This information can be very valuable. For example, in a physical store, items frequently bought together can be placed adjacent to each other. We can also use this information to give the user a coupon or e-mail the user about various...