ElasticSearch powered by Lucene, a powerful open-source search and analytics engine designed for scalability, reliability, and easy management which supports a good amount of enterprise search use cases. It is an Enterprise search server developed in Java and is released as open source under the terms of the Apache License. It combines the speed of search with the power of analytics through a refined query language covering structured, unstructured, and time-series data.

ElasticSearch is able to get fast search responses because it searches an index instead of searching the text directly. For an instance while searching for any query in a Stackoverflow, it automatically suggests you similar questions, where you can index the title of each query or create a group of text fields in a index, so that when you are searching for specific words or part of that query, the server will search for all text and then sort and return the relevant data.

Fundamental Terminology

Cluster: It is a collection of many nodes that incorporates complete data and provides organized indexing and search capabilities across different nodes. It is identified by a different name.

Node: Node is a single server that contains data and participates in the cluster’s indexing and search capabilities. A node is also identified by a name just like cluster which by default is a random Marvel character name. The different name is helpful in administration purposes as it helps to identify which node serves in your Elastic search cluster.

Index: An index is a bundle of documents having similar properties, like customer or order data. It is also identified by a name which is used to refer the index while performing operations like indexing, search, update, and delete against the documents.

Type: A type is a logical category/partition of an index. It is defined for the documents having a set of common fields.

Document: A document is a basic unit of data that can be indexed. It is like a row of a table in a relational database, which consist the information either for a customer, product or orders and is described in JSON format.

Shards and Replicas: An index has the ability to save a lump sum amount of data that can exceed the hardware limits of single node. A shard allows the content to scale horizontally which divide indexes into multiple pieces and a replica allows users to make copies of index shards. Replica is important as it provides high availability when a shard/node fails. By default, each index in Elastic Search is having 5 primary shards and 1 replica.

Features of Elastic Search

Elastic Search has some notable features that can be helpful to an application:

Full-Text Search

Elastic search implements many features such as splitting text into word, context-sensitive suggestions with the multi-language and Geo-location support type special search feature.

Index

You can save complex and real-time entities as structured JSON documents. All fields have a default index and to get an accurate result, you can use all indexed data in a single query.

Mapping

It is the process of how the document should be mapped in the search engine. Elastic search generates mapping automatically depending on the data sent in the form of a string, integer and so on. By defining a new mapping, the default mapping can be overridden.

High Availability

Elastic search clusters are flexible since they automatically detect and remove node defeat. You can setup multiple index data and query each of them independently or in a combination.

Powerful search experiences

The thing that increases the conversion rates is by utilizing a variety of words or even finding results by synonyms.  You can improve the reliability of search result with this feature.

Analytics Engine

Elasticsearch is the engine that gives you both the power and the speed, with the ability to perform super-fast data extractions from effectively all organized or unorganized data references for real-time analytics.

Use of faceting

 A faceted search is more durable than a normal text search, which allows users to apply a number of filters for the information so that they can get the information which they need to examine.

ElasticSearch is used by many companies such as GitHub, StackOverflow, LinkedIn and WordPress. Elasticsearch is preferred solution for finding a result which is effective for productive searching over large amount of data. It is time to empower your enterprise product with power of elasticsearch.