SQL vs NoSQL – PostgreSQL vs MongoDB with Real Use Case, Performance & Code Comparison

SQL vs NoSQL: Comparing PostgreSQL and MongoDB with Real-World Examples

Choosing the right database is a key decision that directly impacts the scalability, performance, and maintainability of your application. For most developers, the choice often boils down to SQL vs NoSQL — and two of the most popular representatives of each category are PostgreSQL and MongoDB.

In this blog, we’ll break down the differences between SQL and NoSQL, compare PostgreSQL with MongoDB through a real-world use case, and evaluate their performance, flexibility, and developer experience.

What is SQL vs NoSQL?

SQL (Structured Query Language) databases like PostgreSQL are relational and use structured schemas to define data. They're best suited for applications with complex relationships and strong consistency requirements.

NoSQL databases like MongoDB are non-relational, schema-less, and use document-based storage (typically JSON or BSON). They're great for fast iteration, flexible data models, and horizontal scalability.

• SQL: Tables, rows, columns, relationships via foreign keys
• NoSQL: Collections, documents, nested structures, flexible schemas

PostgreSQL: The Power of Structure

PostgreSQL is a mature, open-source relational database known for reliability, data integrity, and advanced SQL features. It supports strict schemas, ACID transactions, and powerful querying through JOINs, CTEs, window functions, and more.

Ideal for: Financial systems, analytics dashboards, complex business logic, structured data.

PostgreSQL Highlights:

• ACID-compliant for full transactional integrity
• Supports complex joins and relations
• Strong indexing and query optimization
• Schema migrations with tools like Prisma or Knex

MongoDB: Flexibility Meets Speed

MongoDB is a document-oriented NoSQL database that stores data in flexible, JSON-like documents. It excels in handling unstructured or semi-structured data, rapid prototyping, and use cases that require horizontal scaling.

Ideal for: Real-time applications, IoT, CMS platforms, event logs, microservices.

MongoDB Highlights:

• Schema-less document structure (great for flexibility)
• Easy to scale horizontally using sharding
• Fast writes, great for high-ingestion workloads
• Relationship handling via .populate() in Mongoose

Real-World Use Case: Task Management App

Let’s imagine a simple project management app with the following structure:

• Users can create Projects
• Projects contain multiple Tasks

In PostgreSQL:

-- Create tables with foreign keys
CREATE TABLE users (
  id SERIAL PRIMARY KEY,
  name TEXT
);

CREATE TABLE projects (
  id SERIAL PRIMARY KEY,
  name TEXT,
  user_id INTEGER REFERENCES users(id)
);

CREATE TABLE tasks (
  id SERIAL PRIMARY KEY,
  title TEXT,
  project_id INTEGER REFERENCES projects(id)
);

In MongoDB (Mongoose):

// Define schema models
const UserSchema = new mongoose.Schema({ name: String });

const ProjectSchema = new mongoose.Schema({
  name: String,
  user: { type: mongoose.Schema.Types.ObjectId, ref: 'User' },
});

const TaskSchema = new mongoose.Schema({
  title: String,
  project: { type: mongoose.Schema.Types.ObjectId, ref: 'Project' },
});

Fetching tasks with related data:

-- PostgreSQL
SELECT tasks.*, projects.name AS project_name, users.name AS user_name
FROM tasks
JOIN projects ON tasks.project_id = projects.id
JOIN users ON projects.user_id = users.id;

// MongoDB with Mongoose
Task.find()
  .populate({
    path: 'project',
    populate: { path: 'user' }
  })
  .exec((err, tasks) => { /* handle result */ });

Performance Comparison: JOIN vs .populate()

MongoDB:

• .populate() internally performs multiple queries
• Slower on deeply nested population or large datasets
• Higher memory consumption with large nested data

PostgreSQL:

• JOINs are executed in a single optimized query
• Faster for large datasets with complex relationships
• Better performance consistency

Developer Experience: Prisma vs Mongoose

• Prisma: Auto-generated, type-safe, powerful CLI, easy migrations
• Mongoose: Flexible and fast, but more prone to runtime errors
• Schema evolution: Easier and safer in Prisma

When to Use PostgreSQL vs MongoDB

Use Case	Best Choice
Structured business data with relations	PostgreSQL
Flexible schema, fast iterations	MongoDB
ACID-compliant transactions	PostgreSQL
High write throughput with loose structure	MongoDB
Heavy analytical or reporting queries	PostgreSQL
Logging, tracking, sensor data	MongoDB

Final Thoughts

There is no one-size-fits-all when it comes to databases. Both PostgreSQL and MongoDB have their strengths and trade-offs.

• Choose PostgreSQL for structured, relational data and complex queries.
• Choose MongoDB when schema flexibility and horizontal scaling matter more.

The best decision depends on your application’s structure, growth plans, and developer preference. In many cases, hybrid (polyglot) architectures combining both are also becoming popular.