Elasticsearch 7 Quick Start Guide: Get up and running with the distributed search and analytics capabilities of Elasticsearch


Elasticsearch 7 Quick Start Guide: Get up and running with the distributed search and analytics capabilities of Elasticsearch
Authors: Anurag Srivastava - Douglas Miller
ISBN-10 书号: 1789803322
ISBN-13 书号: 9781789803327
Publisher Finelybook 出版日期: 2019-10-24
pages 页数: 186 pages


Book Description
Get the most out of Elasticsearch 7’s new features to build,deploy,and manage efficient applications
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.

What you will learn
Install Elasticsearch and use it to safely store data and retrieve it when needed
Work with a variety of analyzers and filters
Discover techniques to improve search results in Elasticsearch
Understand how to perform metric and bucket aggregations
Implement best practices for moving clusters and applications to production
Explore various techniques to secure your Elasticsearch clusters
contents
1 Introduction to Elastic Stack
2 Installing Elasticsearch
3 Many as One – the Distributed Model
4 Prepping Your Data – Text Analysis and Mapping
5 Let's Do a Search!
6 Performance Tuning
7 Aggregating Datasets
8 Best Practices

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