ElasticSearch is an Open-source Enterprise REST based Real-time Search and Analytics Engine. It’s core Search Functionality is built using Apache Lucene, but supports many other features. Like MongoDB, ElasticSearch is also a Document-based NoSQL Data Store.

- An Open-source

- Light Weight with REST API

Highly Available. 

- Easily and Highly Scalable

- Supports Caching Data

- Schema Free

- Fast Search Performance

- Supports both Structured and UN-Structured Data

- Supports Distributed, Sharding, Replication, Clustering and Multi-Node Architecture

- Supports Bulk Operations

- Build Charts and Dashboards within no time

- Does NOT support MapReduce operations

- Not an ACID compliant Data Store

- Does not support Transactions and Distributed Transactions

- Does NOT have built-in authentication or authorization feature

A collection of Documents. For instance, “bookstore” is a Document. Index is used for indexing, searching, updating and deleting Documents. It must be in lower case.

An Index is similar to Database in Relation Database World.

A category of similar Documents. That means we can group a set of similar Documents into a Type. As we know in real-world, a “bookstore” contains different kinds of items: a collection of “Books”, a collections Pens, Pencils, CDs etc. In the same way, “bookstore” Document (One kind of Index) can contain a collection of Types: books, pens,CDs etc.

A Type is similar to Table in Relation Database World.

An instance of a Type. It contains Data with Key and Value fairs. For instance, “title”:”Functional Programming In Java” is a Key:Value fair of a Document of Type:”Books”. Each Document has an id.

A Document is similar to a Row in a Table in Relation Database World. Key is Column name and value is Column value.