MongoDB vs PostgreSQL - KEY Features

The open-source database MongoDB and the relational database PostgreSQL comparisons between the two for data storing, backup capabilities, and security.

Digital Delivery
8 min
Digital Delivery
MongoDB vs PostgreSQL - KEY Features

One of a company's most valuable assets is its data. This data provides key insight into customer behavior and is critical in supporting decisions regarding the business strategy.

Storing such massive amounts of information requires a platform that can handle growth, backup capabilities, security with efficiency.

This overview discusses some key differences between PostgreSQL vs. MongoDB to help you decide which is best for your needs.

What is MongoDB

MongoDB is an open-source database. It uses a document-oriented architecture that uses collections and documents built on key-value pairs.

MongoDB uses NoSQL as the query language.

However, it also supports standard SQL and JSON querying. As a non-relational database, it provides an elastic storage model.

Users can store and query multivariate data types.

Key MongoDB Features

This highly flexible database platform is well known for its efficient horizontal scaling and load balancing capabilities for large datasets.

The platform has several additional benefits that include:

Schema-less Database

As a document-oriented database model, MongoDB is schema-less.

It stores multiple documents in a collection each containing keys.

The structure of a schema-less database means these keys might not be available in other documents in the database.

Document Oriented

With this versatile database information is stored in documents with key-value pairs.

A document is the equivalent of a row in a relational database. The key is equivalent to a column name.

The value of the key represents what can be thought of as the respective row’s value.


As applications grow, so does the amount of data. As data grows, the application may experience degraded performance.

MongoDB handles this through a specialized approach to database partitioning.

Sharding is a way of distributing the data across multiple collections. Each collection is known as a shard.

Given this approach, MongoDB can easily scale horizontally with high throughput for large datasets.


BSON stands for Binary JSON. It is a storage format that uses binary-coded serialization of JSON-like documents.

The benefit of BSON is that it supports data types not supported in JSON such as date and binary.

Replication with Automatic Failover

MongoDB performs replication by distributing data across different machines using primary and secondary nodes which form a replica set.

The primary node performs reads and writes. The secondary nodes are used for backup and only perform reads.

When one of the secondary nodes can’t reach the primary node for 10 seconds, one of the secondary nodes gets promoted to primary (automatic failover).

This process is seamless and ensures data is always available.

What is PostgreSQL

PostgreSQL is a relational database that uses rows and columns to store information.

Information is stored in rows, with columns that represent the data attributes in the row.

It is an enterprise-class open source database that can support more complex data structures than traditional databases.

PostgreSQL runs on Linux and supports SQL and JSON as the query language.

Key PostgreSQL Features

PostgreSQL's popularity lies in its ability to handle both object-relational data and user-defined objects.

Additional features include:

Data Types

This versatile database supports an extensive set of standard data types including numeric, string, boolean, and data types.

However, it supports advanced data structures such as monetary, enumerated, bit string, array, and XML.

PostgreSQL also allows users to create custom data types.

Table Inheritance

PostgreSQL supports the object-oriented principle of inheritance for tables. T

his approach enables users to derive a child table based on a parent table. The child table will include the same columns as the parent.

Non-Atomic Columns

Many databases require columns to be atomic. Atomic columns must contain a single non-divisible value.

PostgreSQL supports non-atomic columns. These columns can have sub-values that can be accessed in queries.

Geospatial Data

Pairing PostgreSQL with the PostGIS plugin enables the database to hold geometrical coordinates.

Users can execute queries on the coordinates to calculate things such as distance or area.


PostgreSQL supports replication with synchronous, asynchronous, and logical replication.


PostgreSQL using a sophisticated query planner to optimize performance.

It also uses transactions and nested transactions, Multi-Version Concurrency Control (MVCC) as well table partitioning.


PostgreSQL is highly reliable with critical disaster recovery techniques through several functions such as:

  • Active Standbys
  • Write-ahead Logging (WAL)
  • Point-in-time-recovery (PITR)
  • Tablespaces


The database supports various security protocols such as Lightweight Directory Access Protocol (LDAP), Security Support Provider Interface (SSPI), SCRAM-SHA-256, and more.

Main Differences between PostgreSQL vs. MongoDB

While both databases offer similar core functionality, each has differences that affect performance and growth.

Query Language

PostgreSQL relies on SQL to query data stored in tables. The information is stored in separate tables and is accessed by using foreign keys and joins.

A disadvantage of this approach is that the schema and data relationships before populating the information.

MongoDB does not store information in tables. Instead, it stores information in documents and uses its own query language (MQL), to query the data.

The benefit of MongoDB is that the schema and relationships do not need to be created upfront.


MongoDB provides automatic horizontal scaling through database sharding.

Scalability in PostgreSQL depends on whether the scenario calls for the reading of information or writing information. For writes, a primary machine has significant processing power to scale vertically.

For reads, scalability uses replica machines to read the information thus providing horizontal scaling. These replicas contain a full copy of the database.


PostgreSQL performs well for systems that require fast read and write speeds.

As such, PostgreSQL is ideally suited to Business Intelligence and Data Warehousing use cases. MongoDB offers better performance for use cases where documents must be stored. Thus it is not ideal for analytics.

When to Use MongoDB

Both databases are highly capable.

The decision over using MongoDB vs. PostgreSQL lies in the application’s needs.

Here are a few items to consider;

Data Structure

For use cases where the ability to store both structured and unstructured data, PostgreSQL is the best choice. Use cases that only store unstructured information MongoDB fits the bill.


Applications that need to modify the fields in JSON should go with MongoDB. PostgreSQL does provide this feature, however, it requires extracting the entire document and writing it back to the database after the update.


Sometimes, developers may not know the requirements for a schema up front. MongoDB provides flexibility for the schema to evolve with the application.

Use cases that can define a schema upfront should use PostgreSQL.

Scaling Requirements

MongoDB offers the fastest horizontal scaling. Because it does not rely on processing power to scale.

Instead, it splits processing between multiple machines. If the application requires a significant amount of document storage, MongoDB's approach to horizontal scaling is better.

Companies require database systems to store and process their valuable company data. This information is critical to driving business strategy.

Determining which to choose requires evaluation of the features of PostgreSQL vs. MongoDB and deciding which fits specific use cases.

Contact us for more information on how we can support your digital transformation initiatives.

Published on
October 14, 2021

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