In this article, I will be giving you brief information about Elasticsearch, its installation, and some examples of usage.

ElasticSearch — basic concepts

Elasticsearch is a real-time distributed and open-source full-text search and analytics engine. It is accessible from a RESTful web service interface and uses schema-less JSON (JavaScript Object Notation) documents to store data. It is built on Java programming language, which enables Elasticsearch to run on different platforms. It enables users to explore a very large amount of data at a very high speed.

There are some Elasticsearch basics that -once you’ve internalized them- make the learning curve less traumatic. I’ve put together 4 of the most important concepts:

1. Fields: the smallest individual unit of data in Elasticsearch. Each field has a defined datatype; the core data-types (strings, numbers, dates, booleans), or complex data-types (object and nested).
2. Index: a collection of different types of documents and document properties. It can be compared to a database in the world of relational databases.
3. Documents: a collection of fields defined in the JSON format in a specific manner. Every document belongs to a type and resides inside an index. In the world of relational databases, documents can be compared to a row in a table.
4. Mapping: a collection of documents sharing a set of common fields present in the same index. Again, it’s like a schema in the world of relational databases.

⚠️ It is worth mentioning that Elasticsearch can’t be used as a database, it wasn’t built for this purpose. Due to that, it’s best if you use it as an additional service in your project next to PostgreSQL, MySQL, or other databases.

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