PostgreSQL vs MongoDB: A Comprehensive Comparison

Picking the right open-source database for your next project can be a challenging task, as there is no one-size-fits-all solution. In this article, we will compare PostgreSQL and MongoDB based on some common criteria, such as database structure, deployment, replication, clustering, support, and documentation.

What is PostgreSQL?

🐘 PostgreSQL is a relational database, which means that it stores data in tables, where each row represents a record and each column represents a field. Tables can be linked by foreign keys, which are references to primary keys of other tables. PostgreSQL enforces a schema, which is a predefined structure and type of the data in each table. PostgreSQL supports various data types, such as text, numeric, boolean, date, time, array, JSON, etc. PostgreSQL also supports SQL (Structured Query Language), which is a standard and powerful language for querying and manipulating data in relational databases.

Pros:

• Suitable for structured and consistent data, where the schema and relationships are well-defined and stable.
• Postgres offers more data integrity, consistency, and reliability, as it enforces rules and constraints on the data.
• Support for advanced features, such as transactions, triggers, functions, views, indexes, etc.

Cons:

• Can be more complex and rigid, as it requires more planning and design upfront.
• Can be less scalable and adaptable, as it can be hard to change the schema and handle large and diverse data.

What is MongoDB?

🍃 MongoDB is a non-relational database, which means that it stores data in collections, where each document represents a record and each field represents a value. Documents can be nested, which means that they can contain other documents or arrays of values. MongoDB does not enforce a schema, which means that the documents in a collection can have different structures and types of data. MongoDB supports a subset of JSON (JavaScript Object Notation), which is a lightweight and flexible format for representing data. MongoDB also supports BSON (Binary JSON), which is a binary-encoded version of JSON that allows for more data types, such as binary, decimal, object ID, etc. MongoDB uses its own query language, which is based on JSON and JavaScript syntax, for querying and manipulating data in non-relational databases.

Pros:

• Suitable for unstructured and dynamic data, where the schema and relationships are not fixed and can vary over time.
• MongoDB offers more simplicity and agility, as it does not require a pre-defined schema and allows for more flexible and expressive data models.
• Scalable and performant, as it can handle large and diverse data more efficiently and distribute it across multiple servers.

Cons:

• Can be less reliable and consistent, as it doesn’t enforce rules or constraints on the data.
• Less functionality, as it doesn’t support some features, such as transactions, joins, subqueries, etc. and it doesn’t use the SQL langauge.

Performance and scaling

🍃 MongoDB has high performance for read and write operations for unstructured and complex data.

🐘 PostgreSQL has high performance for analytical and transactional operations o structured and relational data.

The performance can vary greatly depending on the use-case, data volume, and system configuration.

Both database management systems offer Vertical Scaling which refers to increasing the processing power of a single server or a cluster, and Horizontal scaling which refers to bringing additional servers or clusters to share the load.

Querying

🍃 MongoDB queries are based on Javascript, and are represented as a JSON-like structure, which might require more careful reading and understanding. It’s not as rich as SQL and has a few limitations such as a lack of JOIN operations, subqueries, and transactions. MongoDB provides some SQL-like operators, such as $and, $or, $in, and $group, but they are not equivalent to the SQL operators.

🐘 PostgreSQL on the other hand uses structured query language, which is a standard for relational databases, it uses a more traditional query structure with SELECT,FROM, and WHERE clauses. SQL can perform advanced functions like filters, merge, joins, and aggregation. It’s often considered more readable and easier to use, especially for complex queries.

Extensibility

🍃 MongoDB is a flexible and adaptable database that supports extensibility in various ways, such as adding new data types, operators, indexes, and functions. MongoDB also provides a rich set of APIs and drivers for different languages and frameworks. Moreover, MongoDB offers some tools and services, such as MongoDB Stitch, MongoDB Realm, and MongoDB Charts, that allow you to extend your database with serverless functions, mobile sync, and data viz.

🐘 PostgreSQL is a powerful and versatile DBMs that supports extensibility in numerous ways, such as stored functions and procedures, access from procedural languages such as PL/pgSQL, Perl, Python and more, SQL/JSON Path Expressions, and foreign data wrappers, which connect to other databases or streams using the standard SQL interface. PostgreSQL also provides a wide range of extensions, such as PostGIS, pgcrypto, and hstore, that add new features and capabilities to your database.

Replication and clustering

🍃 MongoDB supports replication and clustering through replica sets, which are a group of MongoDB servers that maintain the same data set and provide redundancy and high availability. Replica sets provide automatic failover and data redundancy, but they don’t distribute the data across multiple servers.

🐘 PostgreSQL supports replication and clustering through streaming replication, which is a mechanism for copying data from one database server to another. PostgreSQL also supports logical replication, which is a mechanism for copying data changes from one database to another. PostgreSQL supports sharding, which is a mechanism for distributing data across multiple servers. PostgreSQL also supports foreign data wrappers, which are extensions that allow you to access data from other databases or streams using the standard SQL interface.

Deployment

🍃 MongoDB can be deployed on-premise or in the cloud. MongoDB Atlas is a fully managed cloud database service that provides automated provisioning and scaling of MongoDB clusters. MongoDB Atlas is available on AWS, Azure, and Google Cloud.

🐘 PostgreSQL can be deployed on-premise or in the cloud. Amazon RDS for PostgreSQL is a fully managed cloud database service that provides automated provisioning and scaling of PostgreSQL clusters. Amazon RDS for PostgreSQL is available on AWS.

Support

🍃 MongoDB offers a free community edition, which is suitable for small projects and startups. MongoDB also offers a paid enterprise edition, which is suitable for large projects and enterprises. MongoDB offers support through a community forum, a knowledge base, and a ticketing system.

🐘 PostgreSQL offers a free community edition, which is suitable for small projects and startups. PostgreSQL also offers a paid enterprise edition, which is suitable for large projects and enterprises. PostgreSQL offers support through a community forum, a knowledge base, and a ticketing system.

Conclusion

Both MongoDB and PostgreSQL are powerful and versatile database management. The choice between them depends on your specific needs. If you are storing structured data that requires complex queries and high levels of data integrity, PostgreSQL is likely the better choice. If you are storing unstructured data that requires flexibility and scalability, MongoDB is likely the better choice. Regardless of which system you choose, both PostgreSQL and MongoDB are reliable and stable systems that can handle large amounts of data with ease.