ElasticSearch powered by Lucene, an powerful open-source search and analytic 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.
Cluster: It is a collection of one or more nodes (servers) that holds the entire data and provides organized indexing and search capabilities across all nodes. It is identified by a unique name.
Node: A node is a single server which stores the 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. This name is important for administration purposes where you want to identify which servers in your network correspond to which nodes in your Elastic Search cluster.
Index: An index is a collection 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 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 can potentially save a large amount of data that can outrun 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 allocated 5 primary shards and 1 replica.
Features of Elastic Search
Elastic Search has some notable features that can be helpful to an application:
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.
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.
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.
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.
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.