PostgreSQL vs MongoDB

Competition between businesses is common in today's world, especially when they offer similar products. In the competitive field of Data Analytics, having a majority customer share in the market and offering efficient products and services helps determine the company's profit. When it comes to Database Management, the choice between MongoDB and PostgreSQL is pretty difficult.

This article provides a detailed evaluation of the both databases as well as highlights the key differences between them to assist you in making an informed decision between MongoDB and PostgreSQL. It also gives you a quick overview of both databases and their features. Finally, it discusses some of the difficulties you may encounter when using these databases. Continue reading to learn how to select the best database for your needs.

MongoDB overview:

MongoDB is a schema-free NoSQL database that stores data in JSON-like documents with optional schemas. In terms of syntax and structure, it differs significantly from traditional RDMS. MongoDB Inc. is the company that created it. Because NoSQL databases seem to be fairly straightforward to use, MongoDB is indeed easy to use for people with no prior programming experience.Information is analysed semi-structuredly, allowing you to process large amounts of data at the same time. It was created in C, C++, and JavaScript, and it is presented on cloud platforms such as Google Cloud Platform, Amazon Web Services (AWS), and Microsoft Azure.

MongoDB employs BSON (Binary JSON) and MQL, a SQL-alternative language. BSON supports data types that are not supported by regular JSON, such as long, floating-point, and date. MQL, like SQL, provides similar features with some extra features. Because documents are handled as JSON-type documents, this is necessary.

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Features of MongoDB:

MongoDB provides a wide range of services, making it a better solution than other databases. Some of these features include:

  • The ability to search by field, range query, and regular expressions in MongoDB. It adds support for ad hoc queries in this way.
  • It is a high-performance schema-less database written in C++.
  • It supports Master-Slave replication and can index any field in a document.
  • This has an active power configuration function that allows it to automatically group similar data in its database.
  • It quickly stores documents of any size without confusing the stack and is simple to administer in the event of a failure.
  • Rather than Procedures, it employs JavaScript.
  • For load balancing and accessibility, MongoDB also claims to support the JSON data model, auto-sharding, and built-in replication.

Postgresql overview:

PostgreSQL, often identified as Postgres, is really a free, open-source relational database management system that emphasizes extensibility and SQL compliance. It was created at the University of California, Berkeley, and debuted on July 8, 1996. It was the Ignes database's replacement. PostgreSQL stores data as Structured objects rather than documents. It adheres to the standard SQL format and syntax.

It is written in C and has a legacy system, which indicates that the elements are fully integrated and work in a logical order. It provides community support as well as additional assistance to some of its paid customers. Because of its creative backup mechanisms, it is widespread used during healthcare, financial services, and manufacturing sectors.

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Features of Postgresql:

PostgreSQL has some distinguishing characteristics that make it a viable alternative to other traditional RDBMSs. 

  • PostgreSQL supports a wide range of data types, document types, and customizations, to name a few.
  • It has a network protocol in which all of the components interact in an automated manner.
  • It's indeed optimal for transaction - based workflows, including those found in payment systems, risk analysis, BI (Business Intelligence), and activating a variety of business applications.
  • It has numerous fail-safes and layoffs that ensure storage reliability.
  • Because it is open-source, any customer can have all of its functionalities for free.
  • These have limited scalability because its computational power is dependent on the machine it is running on.
  • This has a strong security system with extra features such as row and column security requirements and multi-factor verification with credentials.
  • It is ACID (Atomicity, Consistency, Isolation, and Durability) compliant and runs on major operating systems.

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Key differences between PostgreSQL vs MongoDB :

Acid compliance:

MongoDB does have the possibility to be ACID compliant, whereas PostgreSQL already is. The ACID properties are the major assets of databases that allow transactions to be properly tracked.
MongoDB is a database system that processes data using BSON, whereas PostgreSQL is a relational database that processes data using traditional SQL.


MongoDB is a schema-free NoSQL database with distributed capabilities. MongoDB employs collections to enforce various rules and triggers in order to maintain the relationship between various attributes in the database. 
PostgreSQL has a SQL-based architecture but also supports some NoSQL features. It has a monolithic architecture, as opposed to MongoDB. Tables are used to set various rules and triggers on the data. It also frameworks the data so that the database or an ETL (Extract, Transform, and Load) tool can process it efficiently.


Both databases support syntax that is quite different from one another. MongoDB, a NoSQL database, stores data in documents and allows users to access it with MQL. PostgreSQL, on the other hand, stores and accesses data using an RDBMS structure and SQL.

Below is the MongoDB syntax for creating a database, checking to see if it is the current database, and displaying the database.

Creating the database:

>use mydb

switched to db mydb

Checking to see if the newly created database is the current database:



Displaying the contents of the database:

>show dbs

local     0.78125GB

test      0.23012GB

The following is the MongoDB syntax for inserting a record into the database.

>{"name":"tutorials point"})

>show dbs

local      0.78125GB

mydb       0.23012GB

test       0.23012GB

The PostgreSQL syntax for creating the table "statements" is shown below.

CREATE TABLE statements (

                user_id serial PRIMARY KEY,

                username VARCHAR ( 50 ) UNIQUE NOT NULL,

                password VARCHAR ( 50 ) NOT NULL,

                email VARCHAR ( 255 ) UNIQUE NOT NULL,

                created_on TIMESTAMP NOT NULL,

                last_login TIMESTAMP 


The syntax for inserting a record into the table in PostgreSQL is shown below.

INSERT INTO statements(user_id serial, username, password, email, created_on, last_login) 


Foreign key support:

  • A Foreign Key is a column or set of columns in one table that refers to another column (usually the primary key) in another table and establishes a relationship between them. Foreign Keys are not supported by MongoDB, but they are supported by PostgreSQL.
  • It may be a core aspect for some users because these constraints prohibit any actions that remove links from one table to another and can prevent the insertion of invalid data into foreign key columns.

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Query Processing data:

To process queries, MongoDB employs aggregation pipelines. These pipelines are made up of several stages that help transform data. PostgreSQL, on the other hand, processes and runs queries using the GROUP BY function.

Maintaining the data:

MongoDB uses redundant replica sets to keep data safe. These sets enable you to record and replay processes as needed. MongoDB employs synchronous replication, that also includes various repositories or systems updating concurrently.

PostgreSQL uses two-safe replication to keep its data safe. PostgreSQL can update both records at the same time, reducing the number of errors while also maintaining a complete and accurate backup.

Relationship among the tables:

Relationships among multiple tables in your database add value to your database's analysis and storage capabilities. Indexes are used by MongoDB to connect multiple tables. Indexes are a type of data structure that can store a small amount of information in an easily readable format. They are only one component of a join, but they make your data easier to understand and, as a result, assist you in resolving any queries.

Joins are used in PostgreSQL to incorporate data from multiple tables into a single table. In PostgreSQL, as long as you have two tables, you can combine them using joins. PostgreSQL, like traditional SQL, has four types of joins: inner, left, right, and full. A Full Join can be used to combine all of the data from both tables into a single table.

Pricing models:

MongoDB allows the user to select your pricing model from three distinct price plans. They are as follows: shared-$0 per month, dedicated-$57 per month, multi-regional-$97 per month.Because PostgreSQL is open-source and free, anyone can use all of its features for free.

Challenges of MongoDB:

When you've a good understanding of MongoDB, it is crucial to understand some of the difficulties that you may face while working with MongoDB. The difficulties with MongoDB are as follows:

  • It is difficult to uncover insights quickly and simultaneously.
  • MongoDB has a difficult time integrating data from multiple sources (Big Data) and storing it in a common format.
  • It has a reasonable security architecture and is susceptible to some security threats.
  • MongoDB has difficulty validating data, which causes issues in Data Governance.

Challenges of Postgresql:

Now that you have a good understanding of PostgreSQL, it is time to look at some of its drawbacks. PostgreSQL's challenges are as follows: 

  • Although PostgreSQL is simple to deploy on multiple platforms, it does not perform equally well on all of them.
  • PostgreSQL doubles the storage capacity of databases that need to be upgraded.
  • PostgreSQL indexes cannot be used to return the queries directly.
  • The execution plans for queries are not cached.
  • If any bulk bounding operations are performed, those processes may become CPU bound.
  • The current data engineering solutions that businesses require for data and query processing necessitate a steep learning curve, which PostgreSQL lacks.

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This article provided a thorough examination of the two most popular database technologies on the market today: MongoDB and PostgreSQL. It discusses both databases, as well as their features and limitations. It also provided the criteria for judging each of the Databases. Overall, the decision between MongoDB and PostgreSQL is entirely dependent on the company's goals and available resources.

If you really want your database to be infinitely performant and have a high computation and processing power, MongoDB is a good option. It can also be used if users do not have programming skills because it is simple to learn and does not use traditional SQL syntax.If you have fewer resources but are well-versed in sql Database syntax and procedures, PostgreSQL may be a better choice. Aside from these minor distinctions, both databases are equally powerful in terms of performance and will work well with any organization, customer, or business requirement.

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As a senior Technical Content Writer for HKR Trainings, Gayathri has a good comprehension of the present technical innovations, which incorporates perspectives like Business Intelligence and Analytics. She conveys advanced technical ideas precisely and vividly, as conceivable to the target group, guaranteeing that the content is available to clients. She writes qualitative content in the field of Data Warehousing & ETL, Big Data Analytics, and ERP Tools. Connect me on LinkedIn.

PostgreSQL vs MongoDB FAQ's

If you need an SQL database, then you can go with PostgreSQL. Otherwise, if you need a distributed database for modern analytical applications, then MongoDB will be the best option.

PostgreSQL is useful when you need high-level security, and you predict the scale in terms of queries. Further, it enables you to store large-scale data securely. On the other hand, MongoDB deals with unstructured data storage.

Both are popular databases and vary in terms of various benchmarks. Regarding data warehousing and analysis, PostgreSQL performs much faster than MongoDB. In terms of the relational data model and inserting raw data into the system, MongoDB is the best option.

When it comes to working with large data sets along with complex queries, PostgreSQL works much better. So, it is the best choice for Big Data analysis.

PostgreSQL is the best database to work with Big Data sets, and it will release a new version 16 in 2023. Also, it is an advanced RDBMS with open-source availability that is less prone to data errors.

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