Preface

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.

What this book covers

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.

What you need for this book

This book was written using Elasticsearch version 2.0.0, and all the examples and functions should work with it. Using Oracle Java 1.7 u55 and above is recommended for creating Elasticsearch clusters. In addition to this, you'll need a command that allows you to send HTTP requests, such as curl, which is available for most operating systems. In addition to this, this book covers all the examples using Python and Java.

For Java examples, you will need to have Java JDK (Java Development Kit) installed and an editor that will allow you to develop your code (such as Eclipse). Apache Maven has been used to build Java codes.

To run the Python examples, you will need Python 2.7 and above and will also need to install Elasticsearch-Py, the official Python client for Elasticsearch.

In addition to this, some chapters may require additional software such as Elasticsearch plugins and other software but it has been explicitly mentioned when certain types of software are needed.

Who this book is for

Anyone who wants to build efficient search and analytics applications can choose this book. It is also beneficial for skilled developers, especially ones experienced with Lucene or Solr, who now want to learn Elasticsearch quickly. A basic knowledge of Python or Java and Linux is expected.

In addition to this, readers who want to see how to improve their query relevancy, and how to use Elasticsearch Java and Python API, may find this book interesting and useful.

Conventions

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "REST endpoints also enable users to make changes in clusters and indices settings dynamically rather than manually pushing configuration updates to all the nodes in a cluster by editing the elasticsearch.yml file and restarting the node."

A block of code is set as follows:

{
  "int_array": [1, 2,3],
  "string_array": ["Lucene" ,"Elasticsearch","NoSQL"],
  "boolean": true,
  "null": null,
  "number": 123,
  "object": {
    "a": "b",
    "c": "d",
    "e": "f"
  },
  "string": "Learning Elasticsearch"
}

Any command-line input or output is written as follows:

wget https://download.elastic.co/elasticsearch/elasticsearch/
elasticsearch-2.0.0.deb
sudo dpkg -i elasticsearch-2.0.0.deb

Note

Warnings or important notes appear in a box like this.

Note

Tips and tricks appear like this.

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