Choosing the right database management system is critical for any business that relies on data-driven decision-making. This article provides a detailed description of the differences between two of the most popular databases in the market, PostgreSQL vs MongoDB.
Today, we’ll gain a comprehensive understanding of these databases and break down the differences between them, so you can make an informed decision when selecting a database to suit the specific needs of your data stack.
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:
- JSON data
- User-defined types
It also supports advanced data manipulations like
- 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.
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-based 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, 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.
Binary JSON allows certain data types like floating-points, long, and date types alongside the datatypes supported by JSON.
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.
MQL is designed to be intuitive and easy to use, with support for complex queries and aggregation operations that allow you to perform advanced data analysis and manipulation. MQL syntax is like JSON format, which provides a range of operators and expressions that allows users to sort and transform filter data in MongoDB.
MongoDB provides advanced features like sharding, which allows you to add more servers to your MongoDB cluster and partition your data to each of these servers. This reduces load and creates a scalable architecture to handle varied demand; for example, a sudden hike in web traffic due to ad placement. What makes it more attractive is the fact that there is no downtime when implementing this feature.
MongoDB also supports vertical scaling which is an easier way to scale MongoDB. You simply add more resources like RAM, CPU, or hard disk to cushion the effect of increasing load.
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.
- Queryable Encryption allows you to encrypt sensitive data fields, store them, and query them in their encrypted form.
- 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: Important 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:
- ACID compliance
- Language and Syntax
- Foreign key support
- Query processing model
- Pricing model
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. PostgreSQL is good for a primary transactional database or as a data source.
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.
PostgreSQL Vs MongoDB ACID compliance
PostgreSQL provides a full guarantee for atomicity, consistency, isolation, and durability. PostgreSQL ensures atomicity by making sure each transaction fails or succeeds as a singular unit. It is not possible for transactions of the same unit to fail and not succeed at the same time, this is called partial failure which can be a complex problem if it ever happens.
PostgreSQL ensures consistency by enforcing constraints. Constraints are rules used to limit the values that can be inserted into a column or set of columns in a table. For example, a primary key constraint ensures that each row in a table has a unique identifier, while a foreign key constraint ensures that a value in one table references a valid value in another table.
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 PostgreSQL, not every NoSQL database is ACID compliant but MongoDB is.
However, according to MongoDB’s model and best practice, it is advised that related data is stored in a single document using a variety of data types, including arrays and embedded documents. Because MongoDB stores related data in a single document, it doesn’t need to perform joins to retrieve related data, as is often necessary for a relational database. This can lead to improved performance and scalability, especially when dealing with large amounts of data.
In this case, the need for ACID compliance may be reduced. ACID compliance is most important in situations where transactions must be guaranteed to be executed in a consistent, isolated, and durable manner, such as in financial transactions. However, in MongoDB’s data model, transactions are typically less complex, and the need for strong ACID compliance may be less important.
PostgreSQL Vs MongoDB Extensibility
Extensibility means the database is designed to be able to add new capabilities or functionalities.
PostgreSQL is very much extensible. It supports a variety of extensions or plugins that can be used to add new functionalities such as full-text search and GIS capabilities. This makes it easy to customize PostgreSQL to meet the specific needs of a particular application.
Furthermore, what makes PostgreSQL extensive is catalog-driven events since it can dynamically manage and adapt to changes in the database schema. This makes it easier for developers to add new features and extensions to the system without having to modify its core codebase.
In contrast, MongoDB does not offer as much flexibility as PostgreSQL. MongoDB’s extensibility is based on its flexible document-oriented data model, which allows developers to store and retrieve data in a variety of ways.
MongoDB’s extensibility is also supported by its rich set of built-in features and APIs that allow developers to integrate with other systems and tools. For example, MongoDB supports a variety of query languages, including MongoDB Query Language (MQL), which is similar to SQL, and Aggregation Pipeline, which provides a flexible way to manipulate and transform data.
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.
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.
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.
By enabling foreign key constraints, PostgreSQL can avoid the insertion of invalid data into foreign key columns.
On the other hand, MongoDB does not support foreign keys because it has no tables and operates as a document-based database. Instead, it has the concept of references, which allows a document to reference another document’s _id field.
PostgreSQL Vs MongoDB Query Processing
PostgreSQL is a relational database system that supports SQL as its query language. It has a sophisticated query planner and optimizer that can optimize complex SQL queries for efficient execution. PostgreSQL uses a cost-based query optimizer that selects the most efficient query execution plan based on the estimated cost of different query plans.
PostgreSQL also supports advanced SQL features such as window functions, common table expressions, and stored procedures that allow for more complex data manipulations.
MongoDB is a document-oriented NoSQL database system that uses the MongoDB Query Language (MQL) for querying documents. MQL is designed to be more flexible and expressive than SQL, allowing for nested queries and deep filtering of document structures. MongoDB also supports aggregation pipelines, which allow for the processing of documents through a sequence of operations such as filtering, grouping, and sorting. This can be useful for performing complex analytics tasks.
In terms of performance, MongoDB’s query processing can be faster than PostgreSQL’s for certain use cases because it does not have to perform complex joins between multiple tables. However, for complex queries involving multiple tables, PostgreSQL’s query optimizer can provide better performance by selecting the most efficient query execution plan.
PostgreSQL Vs MongoDB Pricing Model
PostgreSQL is completely free of cost and it is open-source. It is open to contributions or extending the code to create a database based on your requirement. You can also pay for managed PostgreSQL instances through a variety of cloud providers.
MongoDB is also open-source but has paid tiers like Enterprise and Atlas for the cloud.
PostgreSQL vs MongoDB: Which One to Choose
Choose PostgreSQL if your application requires:
- A relational database model with strong ACID compliance.
- Complex SQL queries and joins between multiple tables.
- Advanced SQL features such as window functions, common table expressions, and stored procedures.
- Full-text search capabilities.
- Support for geographic data.
Choose MongoDB if your application requires:
- A flexible document-oriented data model.
- A high degree of scalability and performance for large datasets.
- Complex queries that involve deep filtering of document structures.
- A high degree of availability and fault tolerance.
- Support for geospatial queries.
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.
So far, we discussed PostgreSQL and MongoDB from their very basics and definition, their security, and their use cases. We have also taken a deep look into some features and made comparisons with them. It is now left for you to choose the best database for your business.
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: