With constantly evolving and growing datasets, organizations have the need to find actionable insights for their business. Elasticsearch, which is the world's most advanced search and analytics engine, brings the ability to make massive amounts of data usable in a matter of milliseconds. It not only gives you the power to build blazingly fast search solutions over a massive amount of data, but can also serve as a NoSQL data store.
Elasticsearch Essentials will guide you to become a competent developer quickly with a solid knowledge and understanding of the Elasticsearch core concepts. In the beginning, this book will cover the fundamental concepts required to start working with Elasticsearch and then it will take you through more advanced concepts of search techniques and data analytics.
This book provides complete coverage of working with Elasticsearch using Python and Java APIs to perform CRUD operations, aggregation-based analytics, handling document relationships, working with geospatial data, and controlling search relevancy.
In the end, you will not only learn about scaling Elasticsearch clusters in production, but also how to secure Elasticsearch clusters and take data backups using best practices.
Chapter 1, Getting Started with Elasticsearch, provides an introduction to Elasticsearch and how it works. After going through the basic concepts and terminologies, you will learn how to install and configure Elasticsearch and perform basic operations with Elasticsearch.
Chapter 2, Understanding Document Analysis and Creating Mappings, covers the details of the built-in analyzers, tokenizers, and filters provided by Lucene. It also covers how to create custom analyzers and mapping with different data types.
Chapter 3, Putting Elasticsearch into Action, introduces Elasticsearch Query-DSL, various queries, and the data sorting techniques. You will also learn how to perform CRUD operations with Elasticsearch using Elasticsearch Python and Java clients.
Chapter 4, Aggregations for Analytics, is all about the Elasticsearch aggregation framework for building analytics on data. It provides many fundamental as well complex examples of data analytics that can be built using a combination of full-text search, term-based search, and multi level aggregations. The user will master the aggregation module of Elasticsearch by learning a complete set of practical code examples that are covered using Python and Java clients.
Chapter 5, Data Looks Better on Maps: Master Geo-Spatiality, discusses geo-data concepts and covers the rich geo-search functionalities offered by Elasticsearch including how to create mappings for geo-points and geo-shapes data, indexing documents, geo-aggregations, and sorting data based on geo-distance. It includes code examples for the most widely used geo-queries in both Python and Java.
Chapter 6, Document Relationships in NoSQL World, focuses on the techniques offered by Elasticsearch to handle relational data using nested and parent-child relationships and creating a schema for the same using real-world examples. The reader will also learn how to create mappings based on relational data and write code for indexing and querying data using Python and Java APIs.
Chapter 7, Different Methods of Search and Bulk Operations, covers the different types of search and bulk APIs that every programmer needs to know while developing applications and working with large data sets. You will learn examples of bulk processing, multi-searches, and faster data reindexing using both Python and Java, which will help you throughout your journey with Elasticsearch.
Chapter 8, Controlling Relevancy, discusses the most important aspect of search engines—relevancy. It covers the powerful scoring capabilities available in Elasticsearch and practical examples that show how you can control the scoring process according to your needs.
Chapter 9, Cluster Scaling in Production Deployments, shows how to create Elasticsearch clusters and configure different types of nodes with the right resource allocations. It also focuses on cluster scalability using the best practices in production environment.
Chapter 10, Backups and Security, focuses on the different mechanisms of creating data backups of an Elasticsearch cluster and restoring them back into the same or an other cluster. A step-by-step guide to setting up NFS (Network File System) is also provided. Finally, you will learn about setting up Nginx to secure Elasticsearch and load balance requests.