It’s an open-source NoSQL database developed for high performance, high availability,
and easy scalability. Collection and document are the two primarily used terms/concepts
in MongoDB. Here, Collection is referred to a group of these documents, which is like
an RDBMS table.
What is MongoDB?
As a definition, MongoDB is an open-source database that uses a
data model and a non-structured query language. It is one of the
powerful NoSQL systems and databases around, today.
MongoDB Atlas is a cloud database solution for contemporary
available globally. This best-in-class automation and established
fully managed MongoDB across AWS, Google Cloud, and Azure.
It also ensures availability, scalability, and compliance with the
security and privacy requirements. MongoDB Cloud is a unified
includes a global cloud database, search, data lake, mobile, and
ElasticSearch is a fast growing technology built on Lucene. The
main scope of ElasticSearch is to be a search engine. It also provides
a lot of features that allow you to use it for data storage and data
analysis. ElasticSearch has many innovative features like:
JSON/REST-based api and natively distributed in a node/cluster.
ElasticSearch can be set up on a physical or virtual server depending
on RAM, CPU and disk space.
Elasticsearch vs RDBMS vs MongoDB
ElasticSearch RDBMS MonogoDB
Index (Indices) Database Database
Shard Shard Shard
Mapping/Type Table Collection
Field Field Field
Object (JSON Object) Record (Tuples) Record (BSON Object)
Feature of Elasticsearch
•Powerful query DSL
Limitations - Elasticsearch is not the perfect data store for all
scenarios. It has a few limitations that need to be taken into
account when choosing the right data store for your application.
Feature of MongoDB
•Distributed document storage
•Powerful queries and aggregations
•Great indexing capabilities
•GridFS to store any size document
Limitation -MongoDB are its inability to provide full-text search
at speed and its lack of some search functions, like tokenizing
Speed of search is better in Elasticsearch compared to
Backup - In MongoDB, you need to use the MongoDB oplog,
which is a capped collection. It is also possible to create a
backup of a MongoDB deployment by taking a snapshot of the
file system. This makes a copy of MongoDB’s underlying data
Elasticsearch performs incremental backups
using _snapshot REST endpoint with the help of plugins, and its
backup destinations can vary from file systems to cloud storage.
You can delete old snapshots easily, and the recovery of
Programming Language Support – MongoDB is more popular
because it has support for more languages starting from C, C++,
supported, and therefore, the reach of the database has increased
importantly .Net. DotNet is not yet supported by MongoDB.
Third Party Support – Even though MongoDB and Elasticsearch
started almost together, MongoDB due to its simplicity has grown
That is why there are so many different 3rd party supports
Cloud are the two 3rd party service support that Elasticsearch has.
data with BI and SQL tools. It is going to speed by query running
A Java-based NoSQL database is called A C++-based document-
Elasticsearch can handle JSON documents in
documents cannot be converted to binary.
It has the capacity to manage JSON documents
JSON to BSON (a Binary version of JSON).
To design the finest application, programmers
Because MongoDB is a user-friendly database,
don’t need to pay as much attention to it.
Full-text searches can be carried out using It enables CRUD operations without the need for
Elasticsearch wins the search engine category
In terms of document storage databases,
first, and fifth overall.
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