Amongst various database management systems (DBMS) that enable users to weave, retrieve, read, customize, run queries, and remove data from a database, MongoDB and PostgreSQL stand out as two robust solutions, each with their distinct strengths and characteristics. Both are designed with distinct models for diverse use cases. This article explores the features, performance, and differences in MongoDB vs. PostgreSQL. 

What is MongoDB?

It is an advanced, high-performance database service released in February 2009. It is an open-source, non-relational database that can be used for different operating systems. It is worth noting that MongoDB is not exclusively a cloud database service; it can be deployed on various operating systems, including both cloud-based and on-premises environments.

MongoDB has been created to store and handle big data in a flexible, JSON-like structure called Binary JSON. 

MongoDB comprises a distinct structure and syntax that enables schema-free models. MongoDB processes a large volume of data faster and in different stages, formats, and structures. Its performance can be attributed to its support for horizontal scaling, flexible indexing, and features like sharding.

MongoDB is a NoSQL database. NoSQL databases are simple, making MongoDB easy for basic programmers and suitable for various applications. MongoDB is available in both free and paid versions and was programmed in C++.

Features of MongoDB

Let us explore some of the mind-blowing features of MongoDB.

  1. Multi-cloud Clusters: With the help of MongoDB, users can enable reliable and powerful applications that can leverage more than 1 cloud simultaneously. Hence, the app can utilize services and resources to deliver peak performance and smooth functioning of the app.
  2. Performance Advice: When applications are upgraded, MongoDB optimizes performance by offering best-on-demand schema design practices to deliver effective outcomes. 
  3. Load Balancing: MongoDB enables multiple accessing of data on clients' requests by distributing them to other servers. This ensures even load on multiple servers while maintaining data consistency and fast performance. While MongoDB provides effective load balancing, proper sharding strategies and configurations are essential for optimal results.

Use cases 

MongoDB is widely used for data storage by several organizations worldwide. Explore their roles in different services.

  1. Personalization: With MongoDB, millions of users can personalize their experience in real time for various purposes. These may include social media sign-in and/or home page customization. In addition, complicated queries can be resolved directly without concerning data extraction, loading, and transformation.
  2. Payments: While creating a new payment product, MongoDB's ability to handle data flexibly enables the new product to reach the market faster and more smoothly. Users do not have to be concerned about complexity, such as data fragmentation. Users can leverage the agility of MongoDB to use them as consolidated operational data in case of modernizing the payment ecosystem of any mature enterprise. Thus reducing the risk of complications during the process of creating new goods or services. 
  3. Managing Contents: MongoDB enables users to create, serve, or store any type of data and construct any feature within a specific database. MongoDB's productive team and commodity software ensure your success by offering multiple features to generate content-rich applications.

Pros and Cons

Let us know the advantages and disadvantages of MongoDB.

Pros of MongoDB:

  • Higher adaptability
  • Scalability
  • Higher speed
  • Simplicity
  • Quick set-up
  • Easy environment
  • Scalability

Cons of MongoDB:

  • Indexing
  • Limited data size 
  • Nesting
  • Transactions 
  • High memory usage
  • Duplicates

What is PostgreSQL?

PostgreSQL refers to the object-relational database that imitates a traditional schema and syntax for SQL databases. It is a free, open-source platform released in 1987, making it older than MongoDB. Here, the data is stored as structured objects rather than documents. All the components in PostgreSQL are united, thus representing monolithic architecture. However, it still supports various extensions, foreign data wrappers, and can be configured for distributed architectures.

PostgreSQL is a basic data source for multiple applications like analytics, web, geospatial and mobile. PostgreSQL was developed in one of the well-known programming languages, C language. Security in PostgreSQL is maintained by a group of contributors and enthusiasts by consistently identifying and resolving bugs.

Features of PostgreSQL

Here are some key features of PostgreSQL for your reference.

  1. JSON Data Support: PostgreSQL supports JSON for querying and storing data, which leads to running a NoSQL workload. In case users have to extract multiple information from diverse sources but do not know the format. They can work with JSON as it does not require any predefined columns. This ensures storing unstructured data from multiple sources, making it versatile for uncertainties and alterations.
  2. Window Function: Aggregate functions return a single row in a query; however, window functions can help handle multiple rows by returning the same number of rows in a query. This feature makes it a favorite for analytics purposes.
  3. Non-Atomic Columns: Generally, relational databases have a rule of holding a single value or information in a column called atomic value. However, PostgreSQL provides additional benefits of incorporating sub-values or nested structures.  This enables users to extract intrinsic data from single columns conveniently.

Use cases 

Let us explore where PostgreSQL can be efficiently useful.

  1. Manufacturing: PostgreSQL enables innovation and growth of industrial manufacturers by considering generating focused strategies and enhanced supply chain performance. PostgreSQL is an efficient tool that holds customer complex data and helps quickly access desired information when required to optimize operations effectively. This ensures an increase in the growth and profitability of the businesses. 
  2. Scientific Data: Research involves dealing with and generating huge amounts of data. This requires effective management of data in a structured way. With PostgreSQL, SQL engines with rigorous analytical performance that handles terabytes of data in a breeze. 
  3. Federated Hub Database: PostgreSQL is a central hub for polyglot database systems. It means the database supports foreign data wrappers and JSON to link with multiple other sources along with NoSQL types.

Pros and Cons

Here are the advantages and disadvantages of PostgreSQL for your reference.


  • Flexible full-text search
  • Effectively functions with SQL standards
  • Complex data type processing
  • Enables generation of own functions when possible (such as data types and triggers)


  • Comparatively slow performance
  • Low reading speed

MongoDB vs. PostgreSQL: Head-to-Head Comparison

Know the key differences between MongoDB vs. PostgreSQL from the following table.

Difference in Terms of



ACID Compliance

Atomic, Consistent, Isolated, and Durable (ACID) refers to components that function in compliance with data validity in transactional workflow databases.

MongoDB is capable of ACID compliance but is not fully compliant by default. 

In contrast to MongoDB, PostgreSQL possesses a built-in feature of ACID compliance.


MongoDB is a non-relational database or NoSQL that offers flexibility in storing all data types as JSON documents. This ensures quick access to data retrieval, modification, and management.

It refers to an object-relational database that stores and manages data as a table incorporating rows and columns. When it comes to MongoDB vs. PostgreSQL performance, intrinsic data can be stored in a single column, implying they possess subsets or nested structures. 


Through the MongoDB document model, users can map to objects with application code. These documents tend to showcase hierarchical relationships to store advanced structures and arrays conveniently.

The architecture model comprises two processes: 

  1. Client-side: Applications are leveraged by users to connect with databases. The easy interface links users and databases via APIs.
  2. Server-side: Applications specifically for operations, connections, status, and dynamic assets for distinct performances like server shutdown, background process run, managing connection requests, recovery, and initializing server. 


Extensibility is the tendency to incorporate new functions and capabilities. In MongoDB, users can generate and use functions within the framework.

Extensibility is supported in PostgreSQL in multiple ways, such as procedures and functions. Thus, the catalog-driven feature of PostgreSQL depicts its extensibility.


Collaboration is simple between teams and developers through MongoDB. It eliminates the incorporation of intermediates between the same and avoids complicated communications.

It incorporates role inheritance, table, and user-level privileges. This helps manage users and provides them authorization to read and write the same.


It possesses field-level and client-side encryption. It helps users to encrypt crucial data before sending it through the network. 

PostgreSQL rules over MongoDB when it comes to security. The strict compliance for the database structure enables PostgreSQL to be quite a secure environment and thus utilized in banking sectors.


No foreign key is available in MongoDB. However, DBRef standards are present that work as standards for developing references.

It supports foreign key constraints as SQL compliant. It means once a foreign key is active, it helps validate inserting data in the table.

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Users must wisely choose the database management system (DBMS) after an in-depth study of MongoDB vs. PostgreSQL. With the evolving business world, there is an increased amount of data to handle, requiring professionals. There are several tools and technologies that must be learned, including language, to serve tasks for business development. 

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