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Elasticsearch vs. MongoDB: 5 Key Differences

Elasticsearch vs. MongoDB: 5 Key Differences

As you continue reading, more questions come to mind. Do you require Elasticsearch to be used? What are the benefits and drawbacks of ElasticSearch and MongoDB? ElasticSearch vs. MongoDB: How do the two stack up?

Business Intelligence

Business Intelligence makes use of software and services to convert data into useful insights that influence the strategic and tactical business decisions of an organization. To give users in-depth insight into the condition of the business, BI tools access and analyze data sets and show analytical findings in reports, summaries, dashboards, graphs, charts, and maps.

Let’s examine Elasticsearch vs MongoDB in more detail by comparing the following:

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MongoDB vs Elasticsearch

MongoDB vs Elasticsearch

MongoDB and Elasticsearch are now the two most widely used databases. MongoDB is well known for its user-friendly approach, while Elasticsearch is gaining popularity for enabling programmers to create only the greatest applications. Let’s discuss this in more detail for better understanding.


Today’s most powerful database is MongoDB, an open-source database that employs a doc-oriented database developed in C++ with a non-structured query language. You can build numerous databases with MongoDB, and each database can include numerous collections.

Businesses benefit from databases in a number of ways. They basically attack the foundation of database administration techniques, which eliminates dependencies between database documents and other information pertaining to the activities or programs.

Using built-in replication, secondary replicas keep a copy of the primary’s data on hand. The replica set automatically runs an election procedure to choose which secondary should replace a failing primary replica. The use of secondary storage for reading operations is optional, however, the default consistency of the data there is just eventual.

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ElasticSearch is a sophisticated collection of projects that creates capacity structures and stacks the information that manifests in the way that the customers and projects want it to. Additionally, it has the ability to hide data from a particular user or user group. As a result, it is a secure strategy, which is actually the main factor in why many organizations favor it.

It is also true that a number of activities on information, such as updating, recovering, abusing, and erasing, are carried out entirely under the control of users. Therefore, you don’t need to worry. On information that has been given the supervisors’ approval, only those clients are allowed to carry out such operations.

ElasticSearch can be thought of as a tool that enables clients to use information concurrently without interfering with one another. This is recognized as one of ElasticSearch’s top features. Simply said, the majority of the advantages that databases have over traditional record structures are supported by a number of programs.

Businesses constantly require a single database that can meet all of their requirements without experiencing any issues. Actually, the type of applications and the method data need to be managed to play a big role in choosing a database. In addition, it relies on the specific requirements that businesses have for their database. ElasticSearch may be relied upon because it is equipped to handle every task.

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Difference Between Elasticsearch and MongoDB

Even though they may seem like different applications of similar technologies, Elasticsearch and MongoDB have important differences. Here is a table that contrasts and compares, Elasticsearch and MongoDB:

       Elasticsearch            MongoDB
A Java-based NoSQL database is called Elasticsearch.A C++-based document-oriented NoSQL database, MongoDB.
Elasticsearch can handle JSON documents in indices, but JSON documents cannot be converted to binary.It has the capacity to manage JSON documents and convert JSON to BSON (a Binary version of JSON).
To design the finest application, programmers must pay close attention.Because MongoDB is a user-friendly database, programmers don’t need to pay as much attention to it.
Full-text searches can be carried out using Elasticsearch.It enables CRUD operations without the need for full-text support.
Elasticsearch wins the search engine category and comes in seventh overall.In terms of document storage databases, MongoDB is ranked first, and fifth overall.

Elasticsearch Advantages and Disadvantages

Let’s see the Advantages and Disadvantages of Elasticsearch which are given below:

Elasticsearch Advantages and Disadvantages

Advantages of Elasticsearch

  • Various types of searches, including those using structured, unstructured, geospatial, and metric data, can be carried out and combined.
  • Data can be obtained via a query in any format needed.
  • Elasticsearch is possible to quickly evaluate billions of records.
  • Additionally, it offers aggregates for investigating data trends and patterns.
  • Even from very large data sets, Elasticsearch finds the best matches for your full-text searches quickly.

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Disadvantages of Elasticsearch

  • Split-brain issues can occasionally arise in Elasticsearch.
  • With exception of Apache Solr, Elasticsearch is unable to manage request and response data in several languages.
  • Elasticsearch is a terrible alternative for a data store compared to other options like MongoDB, Hadoop, etc.
  • It is a powerful and flexible distributed database search engine, but it can be difficult to comprehend.
  • Particularly in terms of office search usage, it is not as simple as a box search.

MongoDB Advantages and Disadvantages

Let’s see the Advantages and Disadvantages of MongoDB which are given below:

MongoDB Advantages and Disadvantages

Advantages of mongoDB

  • We have the freedom and flexibility to store various types of data due to MongoDB.
  • By dividing it over numerous servers linked to the program, we can store a lot of data.
  • MongoDB is 100 times faster than a relational database in terms of speed.
  • Replication and GridFS are functionalities available in MongoDB. These aspects contribute to MongoDB’s increased data availability.
  • The fact that MongoDB is a horizontally scalable database is a huge benefit.

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Disadvantages of mongoDB

  • Like a relational database, MongoDB does not support joins.
  • Data redundancy exists as a result of joins’ lack of capability. As a result, memory is used more frequently than necessary.
  • Your document size is limited to 16 MB.
  • More than 100 levels of document nesting are not permitted.
  • Performance issues and slow execution are possible with MongoDB.

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Use case of Elasticsearch and MongoDB

Let’s discuss the use-case of Elasticsearch and MongoDB which are given below:

Use case of Elasticsearch and MongoDB

Use case of Elasticsearch

Analyzing logs and logging

This one shouldn’t come as a surprise to anyone who is familiar with Elasticsearch. One of the simplest to use and scale logging solutions is Elasticsearch because of the ecosystem that has grown up around it.

 Numerous customers of our platform have taken advantage of this to either include logging in their primary use case or using us only for logging.

Combining and Scraping Public Data

The Elastic Stack offers a tonne of features to make capturing and indexing distant data simple, just like log data. Elasticsearch, like most document stores, has the flexibility to accept data from a variety of sources while yet keeping it all organized and searchable due to the lack of a rigid structure.

Our Twitter connector, which enables you to set up hashtags to watch on Twitter and then collect all tweets with those hashtags and analyze them in Kibana, is a great illustration of this.

Full-Text Lookup

It also comes as no surprise that Elasticsearch’s primary feature, full-text search, is ranked highly on this list. The applications of this within our customer base, which go far beyond conventional Enterprise search or E-commerce, are what’s surprising.

Our users have demonstrated that Elasticsearch’s search capabilities are strong, and versatile, and contain a wide range of tools to make the search easier across a variety of use cases, from fraud detection/security to collaboration and beyond.

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Use case of MongoDB

Let’s discuss the use case of MOngoDB that is given below:


The largest biometrics database in the world is part of India’s Unique Identification project. MongoDB is the database used by the Aadhar Project to hold a vast amount of demographic and biometric information on more than 1.2 billion Indians. In the Aadhar project, image storage is handled by MongoDB.


One of the most well-known websites for online photo sharing, Shutterfly, uses MongoDB to store and manage more than 6 billion images at a transaction rate of up to 10,000 per second. Initially using Oracle, Shutterfly switched to MongoDB later.


A pioneer in insurance, annuities, and employee benefit plans is MetLife. The Middle East, Europe, Asia, Latin America, Japan, and the United States collectively have more than 90 million customers. The Wall, a cutting-edge customer service solution from MetLife, runs on MongoDB.

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Both MongoDB and Elasticsearch are designed with specific use cases in mind, however, there may be some common situations where picking one tool over the other may be trickier. To assist you in making these more challenging selections, we have analyzed and contrasted various features of both technologies in this article. If you are looking forward to kickstarting your career in this field, We hope this blog was helpful to clear all doubts about Elasticsearch vs MongoDB.

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About the Author

Technical Reseach Analyst - Data Engineering

Abhijit is a Technical Research Analyst specializing in Deep Learning. He holds a degree in Computer Science with a focus on Data Science. Being proficient in Python, Scala, C++, Dart, and R, he is passionate about new-age technologies. Abhijit crafts insightful analyses and impactful content, bridging the gap between cutting-edge research and practical applications.