Book Image

Elasticsearch 7 Quick Start Guide

By : Anurag Srivastava, Douglas Miller
Book Image

Elasticsearch 7 Quick Start Guide

By: Anurag Srivastava, Douglas Miller

Overview of this book

Elasticsearch is one of the most popular tools for distributed search and analytics. This Elasticsearch book highlights the latest features of Elasticsearch 7 and helps you understand how you can use them to build your own search applications with ease. Starting with an introduction to the Elastic Stack, this book will help you quickly get up to speed with using Elasticsearch. You'll learn how to install, configure, manage, secure, and deploy Elasticsearch clusters, as well as how to use your deployment to develop powerful search and analytics solutions. As you progress, you'll also understand how to troubleshoot any issues that you may encounter along the way. Finally, the book will help you explore the inner workings of Elasticsearch and gain insights into queries, analyzers, mappings, and aggregations as you learn to work with search results. By the end of this book, you'll have a basic understanding of how to build and deploy effective search and analytics solutions using Elasticsearch.
Table of Contents (10 chapters)

What this book covers

Chapter 1, Introduction to Elastic Stack, will give you a brief history and background on Elasticsearch. We will also get introduced to log analysis and will cover some of the core components of the Elastic Stack architecture.

Chapter 2, Installing Elasticsearch, will cover the installation process of Elasticsearch in different environments. We will also look into installation using the Debian and rpm packages, followed by installation on Windows using the MSI installer of Elasticsearch.

Chapter 3, Many as One – the Distributed Model, will cover how to interact with Elasticsearch using REST calls to call different operations. We will also look at how we can handle multiple indices, followed by looking at some of the common options for the API response. We will also learn how to create, delete, and retrieve indices.

Chapter 4, Prepping Your Data – Text Analysis and Mapping, will walk through the details of how full text is analyzed and indexed in Elasticsearch, followed by looking into some of the various analyzers and filters and how they can be configured. We will also learn how Elasticsearch mappings are used for defining documents and fields and storing and indexing them, including how to define multi-fields and custom analyzers.

Chapter 5, Let's Do a Search!, will go into further detail regarding data searches, where we will cover URI search and body search. We will also cover some query examples using term, from/size, sort, and source filtering. Following that, we will also cover highlighting, rescoring, search type, and named queries.

Chapter 6, Performance Tuning, will cover data sparsity and how to improve the performance of Elasticsearch. We will also cover how to adjust the search speed by means of allocating memory to the filesystem cache, faster hardware, document modeling, pre-index data, avoiding replicas, and so on.

Chapter 7, Aggregating Datasets, will cover how to aggregate datasets and will explain the different types of aggregations that Elasticsearch supports.

Chapter 8, Best Practices, will cover the best practices we can follow in order to manage an Elasticsearch cluster.