Advantages of using elastic search

• Build on top of lucene
  Elastic search is built on top of Lucene, which is a full-featured information retrieval library, so it provides the most powerful full-text search capabilities of any open source product.
  Also it is good, because it is already familiar to developers.
• Full-text search
  Elastic Search implements a lot of features, such as customized splitting text into words, customized stemming, facetted search, etc.
• Fuzzy Searching
  A fuzzy search is good for spelling errors. You can find what you are searching for even though you have a spelling mistake.
• Autocompletion & Instant Search
  Searching while the user types. It can be simple suggestions of e.g. existing tags, trying to predict a search based on search history, or just doing a completely new search for every keyword. This option is very popular on google. We type elastic search and google suggests elastic search benefits or elastic search success stories.
• Document- oriented
  Elastic search is document-oriented. It stores real world complex entities as structured JSON documents and indexes all fields by default, with a higher performance result.
• Speed
  Elastic search is able to execute complex queries extremely fast. It also caches almost all of the structured queries commonly used as a filter for the result set and executes them only once. For every other request which contains a cached filter, it checks the result from the cache. This saves the time parsing and executing the query improving the speed.
• Scalability
  Software development teams favor Elastic search because it is a distributed system by nature and can easily scale horizontally providing the ability to extend resources and balance the loading between the nodes in a cluster.
• Structured search
  Elastic Search is schema free, it accepts JSON documents, as well as tries to detect the data structure, index the data, and make it searchable.
• Data record
  Elastics earch records any changes made in transactions logs on multiple nodes in the cluster to minimize the chance of data loss.
• Query Fine Tuning
  Elastic search has a powerful JSON-based DSL, which allows development teams to construct complex queries and fine tune them to receive the most precise results from a search. It provides also a way of ranking and grouping results.
• Restful API
  Elastic search is API driven, actions can be performed using a simple Restful API.
• Distributed approach
  Indices can be divided into shards, with each shard able to have any number of replicas. Routing and rebalancing operations are done automatically when new documents are added.
• Use of faceting
  A faceted search is more robust than a typical text search, allowing users to apply a number of filters on the information and even have a classification system based on the data. This allows better organization of the search results and allows users to better determine what information they need to examine.
• Multi-Tenancy
  Often, you have multiple customers or users with separate collections of documents, and a user should never be able to search documents that do not belong to him. This often leads to a design where every user has his own index. More often this leads to have too many indexes. One larger Elastic search index is actually be better.

Elastic search books to read

1. Elastic search Essentials
   Anyone just starting Elastic search needs to know what it is, how it works, and why to use it. With Elastic search Essentials you’ll get all of this condensed into 240 pages of introductory lessons and exercises. You’ll move into custom data modeling for handling intense queries over a search database. As elastic search is best learned through practice, the author offers a nice mix of theory and practice together in each chapter.

2. Elastic search in Action
   Elastic search in Action helps beginners with the core concepts and quickly pushes beyond this into more advanced situations. You’ll learn it through live examples. Along the way you’ll learn about batch searching and indexing results to optimize response times. Elastic search in Action teaches you how to write applications that deliver professional quality search. As you read, you’ll learn to add basic search features to any application, enhance search results with predictive analysis and relevancy ranking, and use saved data from prior searches to give users a custom experience.

3. Elastic search Server
   Here’s another beginner-friendly book that requires absolutely no prior knowledge to get started.
   With this book you will learn the basics of Elastic Search like data indexing, analysis, and dynamic mapping, query and filter Elastic Search for more accurate and precise search results.
   Also, you will learn how to monitor and manage Elastic Search clusters and troubleshoot any problems that arise, configuration and creation of an Elastic Search Index. As well as using Elastic Search query DSL to make all kinds of queries, efficient and precise use of filters without loss of performance, implementing the autocomplete functionality, highlight data and geographical search information for better results and many more.

4. Elastic search Indexing
   By studying how the Elastic search engine stores data you can learn a lot about search indexing and optimization. You’ll learn best practices for mapping strategies and how to handle document metadata for different search queries. You’ll find out how to use analysis and analyzers for greater intelligence in how you organize and pull up search results to guarantee that every search query is met with the relevant results. You’ll explore the anatomy of an Elastic Search cluster, and learn how to set up configurations that give you optimum availability as well as scalability. Also, what is good about this book is that you’ll find real-world solutions to help you improve indexing performance, as well as tips and guidance on safety so you can back up and restore data.

5. Elastic search Blueprints
   This book starts with design patterns for a new server running Elastic search. You will learn how to create a custom search engine for an ecommerce store and how to generate auto-populated search results like Google. Also, you will discover the power of Elastic search by implementing it in a variety of real-world scenarios. And learn how to not only generate accurate search results, but also improve the quality of searches for relevant results. You will find out how to generate real-time visualizations of your data using compelling visualization techniques, such as time graphs, pie charts, and stacked graphs and how to widen the scope of matches using various analyzer techniques, such as lower casing, stemming, and synonym matching.

6. Mastering Elastic search
   To build scalable websites and work on big data projects you’ll need higher-level Elastic search experience. That’s why Mastering Elastic search is a valuable resource for developers with a deep interest in Elastic search applications. You will learn about Apache Lucene and Elastic Search design and architecture to fully understand how this great search engine works.
   You will know how to design, configure, and distribute your index, coupled with a deep understanding of the workings behind it and learn about the advanced features in an easy to read book with detailed examples that will help you understand and use the sophisticated features of Elastic Search.

7. Elastic search Cookbook
   The Elastic search Cookbook contains 130+ different recipes for common setups, pitfalls, and basic extensions you can build on top of the Elastic search API.