Data stored as Document
Elasticsearch is a distributed document store. Instead of storing information as rows of columnar data, Elasticsearch stores complex data structures that have been serialized as JSON documents. When you have multiple Elasticsearch nodes in a cluster, stored documents are distributed across the cluster and can be accessed immediately from any node.
When a document is stored, it is indexed and fully searchable in near real-time –within 1 second. Elasticsearch uses a data structure called an inverted index that supports very fast full-text searches. An inverted index lists every unique word that appears in any document and identifies all of the documents each word occurs in.
An index can be thought of as an optimized collection of documents and each document is a collection of fields, which are the key-value pairs that contain your data. By default, Elasticsearch indexes all data in every field and each indexed field has a dedicated, optimized data structure. For example, text fields are stored in inverted indices, and numeric and geo fields are stored in BKD trees. The ability to use the per-field data structures to assemble and return search results is what makes Elasticsearch so fast.
Elasticsearch also has the ability to be schema-less, which means that documents can be indexed without explicitly specifying how to handle each of the different fields that might occur in a document. When dynamic mapping is enabled, Elasticsearch automatically detects and adds new fields to the index. This default behavior makes it easy to index and explore your data—just start indexing documents and Elasticsearch will detect and map booleans, floating point and integer values, dates, and strings to the appropriate Elasticsearch data types.
A node is a single running instance(server) of a cluster
A cluster is a collection of nodes. Cluster provides collective indexing and search capabilities across all the nodes for entire data.
It is a collection of different type of documents and their properties. Index also uses the concept of shards to improve the performance. For example, a set of document contains data of a social networking application.
It is a collection of fields in a specific manner defined in JSON format. Every document belongs to a type and resides inside an index. Every document is associated with a unique identifier called the UID.
Indexes are horizontally subdivided into shards. This means each shard contains all the properties of document but contains less number of JSON objects than index. The horizontal separation makes shard an independent node, which can be store in any node. Primary shard is the original horizontal part of an index and then these primary shards are replicated into replica shards.
Elasticsearch allows a user to create replicas of their indexes and shards. Replication not only helps in increasing the availability of data in case of failure, but also improves the performance of searching by carrying out a parallel search operation in these replicas
RDBMS and Elasticsearch