PostgreSQL vs. MongoDB: Differences, Strengths, and Use Cases

In the world of PostgreSQL vs. MongoDB, both are popular choices for database management, but they cater to different needs. PostgreSQL is a relational database known for strong ACID compliance and handling structured data with complex queries. MongoDB, on the other hand, is a NoSQL database that excels in flexibility and scalability for unstructured or semi-structured data.

This guide will explore the core differences between these two systems, helping you choose the best option for your project.

What is PostgreSQL?

PostgreSQL is an open-source relational database management system (RDBMS) that extends the SQL language. PostgreSQL is backed by over 35 years of active development on its core system by its developer community which contributed to its consistency, integrity, correctness, and stability. 

Because of its reliability, Postgres is a popular choice as the data source for mobile, IoT, and web applications.

It is a fully-featured RDBMS platform that supports a variety of advanced data types like:

• Arrays 
• JSON data
• User-defined types

Postgres also supports advanced data manipulations like 

• Indexing 
• Trigger functions
• Complex queries

PostgreSQL stores data as structured objects and uses schema for SQL databases. Schemas are like the framework of the database. Before you load any data into the database, you have to define a structure of how they are laid out in the database using a set of schema objects which include rows, keys, columns, etc.

PostgreSQL was built using C programming language and the code can be modified by any developer for their requirements as it is open source. PostgreSQL offers community support, and you can get commercial support for hosted versions of Postgres like Google Cloud SQL and Amazon RDS. 

PostgreSQL Security

Furthermore, PostgreSQL is considered to be highly secure as the community took extra steps to focus on security by actively releasing updates. 

Some key features of PostgreSQL security include:

• User authentication and access control: PostgreSQL provides a robust user authentication system, which allows administrators to manage user accounts and permissions for accessing and modifying data.
  
• Encryption: PostgreSQL supports various encryption options for protecting data at rest and in transit, including SSL/TLS encryption for network traffic and Transparent Data Encryption (TDE) for data at rest.
  
• Auditing and logging: PostgreSQL provides detailed auditing and logging features, which enable administrators to monitor database activity and track changes to the database schema and data.
  
• Advanced security options: PostgreSQL also supports advanced security options such as row-level security, which allows administrators to define security policies at the row level, and database roles, which provide a more granular approach to user permissions.

PostgreSQL Use Cases

Some use cases of PostgreSQL include the following:

• Scientific research: PostgreSQL is commonly used in scientific research applications for storing and analyzing large datasets, such as genomic data and climate data.
  
• Financial services: PostgresSQL is ACID ( atomicity, consistency, isolation, and durability) compliant, so it’s suitable for online transaction workloads. Plus you can easily integrate PostgresSQL with mathematical software like Matlab and R programming language to analyze datasets.
  
• GIS data:PostGIS is an extension that provides various functions to PostgreSQL that allows users to collect geographic data. This means you can query data like geographic locations in SQL.
  
• Web applications: PostgreSQL is commonly used as the backend database for web applications, providing a reliable and scalable data storage solution for applications with high concurrency and transaction rates.

What is MongoDB?

MongoDB is one of the most popular NoSQL document-oriented databases available today. This means that instead of making use of traditional rows and columns, it uses collections and documents. Key-value pairs are the basic units of MongoDB documents.

MongoDB stores data as collections of documents in a flexible, binary JSON-like format called BSON (Binary JSON). This allows you to store and retrieve data in a more flexible and scalable way, without having to conform to a rigid data schema. BSON extends the JSON format to include additional data types like dates, binary data, and decimal numbers.

In addition to BSON, MongoDB has its native query language called MongoDB Query Language (MQL). It's an alternative to structured query language (SQL), which is used in relational databases.

MongoDB provides advanced features like sharding, which allows you to add more servers to your MongoDB cluster and distribute your data across them. This horizontal scaling reduces load and creates a highly scalable architecture to handle increased demand, such as sudden spikes in web traffic. One of the key benefits of sharding is that it can be implemented without downtime, ensuring continuous availability of your application.

Additionally, MongoDB also supports vertical scaling, which is a simpler way to scale by adding more resources (RAM, CPU, or storage) to a single server to accommodate growing workloads.

MongoDB Security

Security is an important consideration when it comes to any database management system, and MongoDB provides several security features to help protect your data. Some of the key security features of MongoDB include:

• Authentication and access control: MongoDB supports multiple authentication mechanisms, including username/password authentication and X.509 certificates. It also provides role-based access control (RBAC) to manage user permissions and restrict access to sensitive data.
  
• Encryption: MongoDB offers four security features.
  
  
  • Queryable Encryption allows you to encrypt sensitive data fields, store them, and query them in their encrypted form.
    
  • Client-side Field Level Encryption: This feature allows you to implement end-to-end encryption for sensitive data fields, ensuring that only authorized users with the proper decryption key can access the data. This encryption occurs at the field level, so you can selectively encrypt only the sensitive data within a document.
    
  • Encryption at Rest: This feature allows you to encrypt data that is not actively being transmitted. MongoDB provides built-in support for encryption at rest through encryption key management.
    
  • TSL/SSL( Transport Encryption): This feature provides encryption for all MongoDB network traffic. It asserts that only the intended users can read MongoDB’s network traffic.
    
• Auditing and logging: MongoDB provides auditing and logging features to help track user activity and monitor the database for security issues. This means it will show who made changes to your database and when.
  
• Network security: MongoDB can be configured to only accept connections from trusted IP addresses and to use secure communication protocols.
  
• Vulnerability scanning: MongoDB provides built-in support for vulnerability scanning and remediation through the MongoDB Enterprise Advanced subscription.
  
• MongoDB Change Data Capture: Additionally, MongoDB offers advanced capabilities like MongoDB Change Data Capture (CDC), which allows you to track and capture changes made to your MongoDB database in real-time. This feature is particularly useful for maintaining data synchronization and supporting various use cases such as data warehousing, event sourcing, and more
  

MongoDB Use Cases

• Content Management Systems: MongoDB is a great choice for content management systems, where it can store a wide variety of content, including text, images, videos, and more. Its flexible data model allows for easy querying and indexing of content, making it easy to retrieve data quickly and efficiently.
  
• Internet of Things (IoT): MongoDB is well-suited for IoT applications, where it can store large amounts of sensor data generated by devices. Its scalability and ability to handle unstructured data make it a popular choice for IoT use cases.
  
• Real-time Analytics: MongoDB’s ability to handle large volumes of data quickly and efficiently makes it a popular choice for real-time analytics applications. It can easily handle complex queries and aggregations, making it easy to extract insights from large datasets.

PostgreSQL vs. MongoDB: A Feature Comparison

This section will compare two of the most popular databases available in the market to assist you in identifying the best one for your needs.

We will be using the following points as yardsticks for our comparisons:

• Architecture
• ACID compliance
• Extensibility
• Replication
• Language and Syntax
• Foreign key support
• Query processing model
• Pricing model

To quickly summarize the key differences between PostgreSQL and MongoDB, let's take a look at a side-by-side comparison:

1. PostgreSQL Vs MongoDB - Architecture

PostgreSQL is an RBDMS based on a client-server model, where clients connect to the PostgreSQL server to access the database.

PostgreSQL’s architecture includes several components, such as the query planner, executor, and optimizer, which work together to manage and optimize SQL queries. It also includes a transaction manager that enforces atomicity, consistency, isolation, and durability (ACID) properties to ensure data integrity. 

PostgreSQL’s architecture is optimized for complex queries involving multiple tables, making it a good choice for applications that require complex data modeling and can benefit from its various scaling options.

On the other hand, MongoDB is a NoSQL database that stores data in a flexible, document-oriented format. It uses modified JSON to store data and provides a rich query language for retrieving and manipulating data. MongoDB’s architecture is based on a distributed system model, where data is spread across multiple servers.

MongoDB’s architecture includes a query router, which directs queries to the appropriate server, and a shard manager, which manages data distribution across multiple servers. MongoDB’s architecture is optimized for scalability and performance, making it a good choice for applications that require high availability and low-latency data access. MongoDB has good flexibility which makes it a good choice for consolidating data from different sources.

2. PostgreSQL Vs MongoDB - ACID compliance

PostgreSQL, being a relational database, provides a full guarantee for atomicity, consistency, isolation, and durability (ACID). PostgreSQL ensures atomicity by making sure each transaction fails or succeeds as a singular unit. It ensures consistency by enforcing constraints. It ensures isolation by providing different levels of transaction isolation from read committed to serializable. It ensures durability by writing changes to the disk before acknowledging a transaction commit.

In contrast to earlier versions, MongoDB now offers ACID compliance. Since version 4.0, MongoDB provides ACID guarantees at the document level and also supports multi-document ACID transactions. This means that even in scenarios where strong transactional guarantees are required, MongoDB can ensure data reliability and consistency.

While MongoDB's data model, which often stores related data within a single document, can reduce the need for complex, multi-document transactions, the availability of ACID compliance is a significant advantage.

3. PostgreSQL Vs MongoDB - Extensibility 

Extensibility refers to a database's ability to adapt and incorporate new features or functionalities. Both PostgreSQL and MongoDB offer extensibility, but they approach it differently:

PostgreSQL's Extensibility:

• Rich Extension Ecosystem: PostgreSQL boasts a vast library of extensions that provide specialized capabilities, such as full-text search (pg_trgm), geospatial indexing (PostGIS), and even integration with machine learning libraries. This allows users to tailor PostgreSQL to their specific needs without modifying the core code.
• Custom Data Types and Functions: PostgreSQL allows developers to create custom data types and functions using languages like SQL, PL/pgSQL, Python, or C. This provides immense flexibility for modeling and processing data in unique ways.
• Foreign Data Wrappers: These allow PostgreSQL to access and query data from external sources like other databases, file systems, or web services.

MongoDB's Extensibility:

• Flexible Schema: MongoDB's document model inherently allows for greater schema flexibility than PostgreSQL's rigid relational structure. This means new data attributes or types can be added without requiring schema migrations, making it adaptable to evolving data requirements.
• Aggregation Framework: MongoDB's aggregation pipeline provides a powerful way to transform and process data within the database, allowing for complex calculations and data manipulations.
• User-Defined Functions (UDFs): MongoDB supports UDFs written in JavaScript, allowing developers to extend its query capabilities with custom logic.
• Pluggable Storage Engines: While not as extensive as PostgreSQL's extensions, MongoDB offers pluggable storage engines (e.g., WiredTiger, In-Memory) to optimize for different workloads.

4. PostgreSQL Vs MongoDB - Replication

Replication is a database feature that ensures failure tolerance and high availability.

PostgreSQL uses a streaming replication method where changes made to the primary server are sent to replica servers through WAL files in real time. This ensures that replica servers have an up-to-date copy of the primary database. PostgreSQL also supports synchronous replication, where a transaction is not considered committed until all replicas have confirmed receipt of the transaction.

On the other hand, MongoDB uses a replica set architecture, where each replica set consists of a primary node and one or more secondary nodes. The primary node receives write operations and replicates the changes to the secondary nodes. If a primary node fails, one of the secondary nodes will be elected as the new primary.

5. PostgreSQL Vs MongoDB - Language and syntax

Both databases support a variety of languages. MongoDB provides driver support for most database languages like Python, Java, R, Scala, C, C++, C#, Nodejs, and more.

PostgreSQL supports several procedural languages, including PL/pgSQL (a PostgreSQL-specific language similar to SQL), PL/Python, PL/Perl, and PL/Java. These languages allow you to write stored procedures and trigger in your language of choice.

6. PostgreSQL Vs MongoDB - Foreign key support

Foreign key support is an important aspect of relational databases. A foreign key helps link two tables together by referencing the primary key of that table.

PostgreSQL: PostgreSQL fully supports foreign keys, allowing you to define constraints that ensure referential integrity. This means that data in a foreign key column must match an existing primary key in another table, preventing inconsistencies and orphaned records.

MongoDB:  As a document-oriented database, MongoDB does not have the concept of tables or foreign keys. However, it offers a similar mechanism called references. References allow one document to store the unique identifier (_id) of another document, creating a link between them. While references don't enforce the same level of strictness as foreign keys, they provide a way to represent relationships within the document-based structure.

7. PostgreSQL Vs MongoDB - Query Processing

PostgreSQL is a relational database system that supports SQL (Structured Query Language) as its primary query language. It boasts a sophisticated query planner and optimizer that can effectively handle complex SQL queries, including joins, subqueries, and aggregations across multiple tables. PostgreSQL's cost-based query optimizer analyzes various query plans and selects the most efficient one based on factors like table statistics, indexes, and available resources. This optimization can significantly improve performance, especially for complex queries.

Furthermore, PostgreSQL supports advanced SQL features such as window functions, common table expressions (CTEs), and stored procedures, further enhancing its query capabilities and allowing for more intricate data manipulations.

MongoDB, on the other hand, is a document-oriented NoSQL database system that uses the MongoDB Query Language (MQL). MQL is designed for flexibility and expressiveness, enabling nested queries and deep filtering of document structures. MongoDB also supports aggregation pipelines, allowing for the processing of documents through a sequence of operations like filtering, grouping, and sorting. This proves particularly useful for performing complex analytics tasks.

In terms of performance, MongoDB can be faster than PostgreSQL for specific use cases. This is especially true for simple queries or those operating primarily within a single document due to its avoidance of complex joins. However, PostgreSQL's query optimizer often outperforms MongoDB when dealing with complex queries involving multiple tables, as it can intelligently select the most efficient execution plan.

The optimal choice for query processing ultimately depends on your specific use case:

• If your data is highly structured and you need to perform complex queries across multiple tables, PostgreSQL is generally a better fit.
• If your data is less structured or hierarchical and you primarily need to query and manipulate documents, MongoDB may be a more suitable choice.

8. PostgreSQL Vs MongoDB - Pricing Model

PostgreSQL is entirely open-source and free to use. This means you can download, modify, and distribute it without any licensing fees. It's a great option for businesses and individuals looking for a powerful, feature-rich database without incurring software costs.

However, while the software itself is free, there are potential costs associated with PostgreSQL:

• Infrastructure: If you're not self-hosting, you'll need to pay for cloud infrastructure to run PostgreSQL. Many cloud providers offer managed PostgreSQL services at varying price points. 
• Support: While community support is available, some businesses might opt for paid enterprise support from companies specializing in PostgreSQL.

MongoDB also offers a free and open-source version called MongoDB Community Server. This is a great starting point for smaller projects or for those who want to explore MongoDB's features.

However, MongoDB has several paid tiers:

• MongoDB Atlas: This is a fully managed cloud database service that offers various pricing tiers based on your usage and storage needs. 
• MongoDB Enterprise Advanced: This is a self-managed enterprise server with additional features like advanced security and auditing capabilities. Pricing is based on the number of servers and cores.

PostgreSQL vs MongoDB: Making the Right Choice for Your Project

Choose PostgreSQL if your application requires:

• A relational database model with strong ACID compliance: Ideal for financial applications, e-commerce platforms, or any system where data integrity is paramount.
  
• Complex SQL queries and joins between multiple tables: Well-suited for reporting and analytics applications that require sophisticated data manipulation.
  
• Advanced SQL features: PostgreSQL's rich feature set allows for complex logic and optimizations within the database itself.
  
• Full-text search capabilities: If your application needs robust search functionality, PostgreSQL has powerful built-in tools.
  
• Support for geographic data: Geospatial data types and functions make PostgreSQL a good choice for location-based applications.

Choose MongoDB if your application requires:

• A flexible document-oriented data model: Perfect for content management systems, social networks, or any application with evolving data structures.
  
• High scalability and performance for large datasets: MongoDB's distributed architecture allows it to handle massive amounts of data with ease.
  
• Complex queries on document structures: MongoDB's query language is optimized for navigating and filtering nested documents.
  
• High availability and fault tolerance: MongoDB's replica sets and sharding features ensure your data is always accessible.
  
• Support for geospatial queries: Like PostgreSQL, MongoDB offers robust support for location-based data.

Hybrid Scenarios

In some cases, using both PostgreSQL and MongoDB can be the best approach. For example, you might use PostgreSQL for storing core transactional data and MongoDB for handling less structured data like user activity logs or product reviews.

Ultimately, the decision between PostgreSQL and MongoDB depends on the specific needs of your application. It’s important to carefully evaluate the strengths and weaknesses of each system and choose the one that best meets your requirements.

Summary

In this in-depth exploration of PostgreSQL vs. MongoDB, we've covered each database's fundamentals, security measures, and diverse use cases. Our detailed feature comparison highlighted each system's strengths and weaknesses, revealing that the best choice depends on your specific project needs.

PostgreSQL, with its relational model and robust ACID compliance, shines when data integrity and complex queries are paramount. MongoDB, on the other hand, offers a flexible document model and excels in scenarios where scalability and agility are crucial.

Choosing the right database is a critical decision that depends on your project's specific needs.  Which database aligns best with your data requirements and application goals? Careful consideration of the factors discussed in this article will guide you towards the optimal choice for your next project.

Estuary Flow allows you to connect data in Postgres or MongoDB to other systems in your data stack. See the docs to learn more about:

• Change data capture from PostgreSQL
  
• Materializing data to PostgreSQL
  
• Change data capture from MongoDB
  
• Materializing data to MongoDB

And more blogs on these topics:

• Postgres Change Data Capture
  
• MongoDB Change Data Capture