Elasticsearch

Elasticsearch is a full-text search engine that's primarily used for searching. It can also be used as a NoSQL database and analytics engine. Elasticsearch is basically schema-less and works in near-real-time. It has a RESTful interface, which helps us to interact with it easily from multiple interfaces. Elasticsearch supports different ways of importing various types of structured or unstructured data; it handles all types of data because of its schema-less behavior, and it's quite easy to scale. We have different clients available in Elasticsearch for the following languages:

• Java
• PHP
• Perl
• Python
• .NET
• Ruby
• JavaScript
• Groovy

Its query API is quite robust and we can execute different types of queries, such as boosting some fields over other fields, writing fuzzy queries, or searching on single or multiple fields, along with field search. Applying a Boolean search or wildcard search aggregation is another important feature of Elasticsearch, which helps us to aggregate different types of data; it has multiple types of aggregations, such as metric aggregation, bucket aggregation, and term aggregation.

The architecture of Elasticsearch has the following components:

• Cluster: A collection of one or more nodes that work together is known as a cluster. By default, the cluster name is elasticsearch, which we can change to any unique name.
• Node: A node represents a single Elasticsearch server, which is identified by a universally unique identifier (UUID).
• Index: A collection of documents where each document in the collection has a common attribute.
• Type: A logical partition of the index to store more than one type of document. Type was supported in previous versions and is deprecated from 6.0.0 onward.
• Document: A single record in Elasticsearch is known as a document.
• Shard: We can subdivide the Elasticsearch index into multiple pieces, which are called shards. During indexing, we can provide the number of shards required.

Elasticsearch is primarily used to store and search data in the Elastic Stack; Kibana picks this data from Elasticsearch and uses it to analyzes or visualizes it in the form of charts and more, which can be combined to create dashboards.