PostgreSQL vs. MongoDB: 2025 Convergence Path for Document & Relational Databases

Database administrators and software engineers face evolving choices when selecting the right database. PostgreSQL and MongoDB, leaders in relational and document databases respectively, are converging in functionality. We explore how PostgreSQL embraces document features through JSONB, while MongoDB adopts ACID transactions, blurring the lines between SQL and NoSQL by 2025. This analysis guides you through choosing the optimal database based on data structure, scalability, and transaction needs, ensuring you select the right solution for your specific requirements.

1. Introduction: The Shifting Sands of Data Management

The world of databases is changing! It used to be pretty clear: you had relational databases and document databases. Now, those lines are starting to blur. Let’s explore what’s happening and what it means for you.

I. Defining Relational and Document Databases

What are these databases, anyway?

• Relational Databases (RDBMS): Think of these like a well-organized spreadsheet. Data is stored in tables with rows and columns. Each table is related to others. This is great for keeping data consistent. PostgreSQL is a popular example of an RDBMS.
• Document Databases: These databases store data in flexible, document-like formats (usually JSON). Each document can have different fields, making them good for data that doesn’t fit neatly into tables. MongoDB is a leading document database.

II. Historical Differences

Historically, these two types of databases have been very different:

• SQL vs. NoSQL: Relational databases use SQL (Structured Query Language) to access and manage data. Document databases often use NoSQL query languages, which are usually easier to learn but can be less powerful for complex queries.
• Schema-on-Write vs. Schema-on-Read: Relational databases enforce a strict schema (structure) before you write data (schema-on-write). Document databases let you write data first and figure out the structure later (schema-on-read). This gives them more flexibility.
• ACID vs. BASE: Relational databases prioritize ACID properties (Atomicity, Consistency, Isolation, Durability) to guarantee data integrity. Document databases often prioritize BASE properties (Basically Available, Soft state, Eventually consistent), which allows for better performance and scalability, but with potential trade-offs in data consistency.

⚠️ Important: These are generalizations. Not all databases fit neatly into these categories.

III. The Convergence Trend

Things are changing! PostgreSQL is adding features that make it more like a document database, and MongoDB is adding features that make it more like a relational database. This means the choice between them isn’t as clear-cut as it used to be.

🎯 By 2025, these two database types will likely be even more similar. They are borrowing ideas from each other to become more versatile.

IV. Blog Post Goal

This blog post will explore this convergence trend. We’ll look at:

• How PostgreSQL is adopting document features.
• How MongoDB is adopting relational capabilities.
• How to choose the right database for your needs in 2025.

We’ll help database administrators and software developers understand these changes so they can make informed decisions.

V. Introducing SQLFlash

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2. PostgreSQL’s Embrace of Document Features

PostgreSQL is leveling up! It’s adding features that let it work more like a document database, giving you the best of both worlds.

I. JSONB Mastery

PostgreSQL has a special data type called JSONB. Think of it as a container for storing flexible data, like a document. JSONB stores JSON (JavaScript Object Notation) data in a binary format, which makes it faster to query and search.

🎯 Why is JSONB so cool? It lets you store data that doesn’t always fit neatly into rows and columns. This is great for things like user profiles, configuration settings, or product details that have different attributes.

💡 PostgreSQL’s JSONB implementation is very powerful. It lets you query the data inside the JSON document. You can search for specific fields, compare values, and even update parts of the JSON document without rewriting the whole thing. Some argue that PostgreSQL’s JSONB offers capabilities that other document databases, like MongoDB, can only dream of.

Here’s a simple example:

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CREATE TABLE products (
    id SERIAL PRIMARY KEY,
    name VARCHAR(255),
    details JSONB
);

INSERT INTO products (name, details) VALUES (
    'Awesome Widget',
    '{
        "description": "A super useful widget!",
        "price": 29.99,
        "colors": ["red", "blue", "green"]
    }'
);

-- Querying for products with a price less than 30
SELECT name FROM products WHERE details ->> 'price' < '30';

In this example, details ->> 'price' extracts the ‘price’ value from the JSON document as text, allowing us to compare it.

II. Indexing JSONB Data

Storing JSON data is great, but searching it quickly is even better! PostgreSQL lets you create indexes on JSONB columns to speed up queries.

💡 There are different types of indexes you can use:

• GIN Index: Good for searching for keys or values within the JSON document. This is a general-purpose index that works well for most JSONB queries.
• B-Tree Index: Useful for indexing specific fields within the JSONB data. This is best when you know which fields you’ll be searching on most often.

Here’s how to create a GIN index:

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CREATE INDEX idx_products_details ON products USING GIN (details);

And here’s how to create an index on a specific field within the JSONB data:

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CREATE INDEX idx_products_price ON products ((details ->> 'price'));

⚠️ Choosing the right index depends on how you plan to query your data. Experiment to see what works best for your specific use case.

III. Benefits of Combining Relational and Document

Why combine relational and document features? Because it gives you flexibility and power!

• Flexibility: Store unstructured data in JSONB without forcing it into a rigid schema.
• Relationships: Maintain relationships between your JSON documents and other relational data.
• ACID Transactions: Ensure data consistency with ACID (Atomicity, Consistency, Isolation, Durability) transactions. This is a big advantage over some document databases. If one part of your transaction fails, the whole thing is rolled back, keeping your data safe.
• Data Validation: Use relational constraints to validate the data stored in your JSONB documents.

Here’s an example of how ACID transactions help:

Imagine you’re updating a user’s profile (stored as JSONB) and also updating their order history (stored in a relational table). With ACID transactions, you can ensure that both updates happen together, or neither happens at all. This prevents data inconsistencies.

IV. Real-world Use Cases

PostgreSQL’s hybrid approach is useful in many situations:

• User Preferences: Store user preferences as JSONB, while keeping core user data (name, email) in relational columns.
• Product Catalogs: Store product attributes (size, color, materials) as JSONB, while keeping core product information (ID, name) in relational columns.
• Event Logging: Store event data as JSONB, allowing you to capture different types of events with varying attributes.

Here’s a table summarizing these use cases:

By combining relational and document features, PostgreSQL provides a powerful and flexible platform for managing your data.

3. MongoDB’s Move Towards Relational Capabilities

MongoDB, known for its flexibility as a document database, is also evolving. It’s adding features that bring it closer to the capabilities of relational databases. This gives developers more options and control over their data.

I. ACID Transactions

MongoDB now supports ACID (Atomicity, Consistency, Isolation, Durability) transactions across multiple documents. 💡 This is a big deal!

• Atomicity: All changes in a transaction happen, or none of them do. It’s like flipping a light switch – it’s either on or off.
• Consistency: The transaction takes the database from one valid state to another.
• Isolation: Transactions don’t interfere with each other. Each transaction acts like it’s the only one running.
• Durability: Once a transaction is complete, the changes are permanent, even if the system crashes.

Before, it was difficult to ensure data integrity when updating multiple documents at once. Now, with ACID transactions, you can be confident that your data stays accurate, even in complex operations.

⚠️ Example: Imagine you’re transferring money between two bank accounts. You need to deduct the amount from one account and add it to the other. An ACID transaction ensures that both operations happen together. If one fails, the entire transaction is rolled back, preventing any inconsistencies.

II. Secondary Indexes

MongoDB has strong secondary indexing capabilities. 🎯 Indexes help you find data faster. Think of it like the index in a book. Instead of reading the whole book, you can use the index to quickly find the information you need.

MongoDB allows you to create indexes on any field in your documents. This means you can efficiently query your data based on different criteria, not just the primary key. This is especially helpful when you have diverse query patterns.

III. MongoDB’s Schema Validation

While MongoDB is known for its schema-less nature, it also offers schema validation. Schema validation lets you set rules for the structure and data types of your documents. 💡 This adds a level of data integrity and consistency.

You can define the required fields, data types, and even the allowed values for each field. If a document doesn’t meet these rules, MongoDB will reject it. This helps prevent errors and ensures that your data is consistent.

Why use Schema Validation?

• Data Quality: Ensures that data entering the database meets specific criteria.
• Consistency: Enforces a consistent structure across all documents in a collection.
• Reduced Errors: Prevents invalid data from being stored, reducing the risk of application errors.

IV. Real-world Use Cases

MongoDB’s improved relational features make it suitable for a wider range of applications.

• Financial Transactions: Handling financial transactions that require ACID guarantees, such as money transfers and order processing.
• E-commerce: Managing complex product catalogs with varying attributes while ensuring data consistency across different collections (e.g., products, orders, customers).
• Inventory Management: Tracking inventory levels across multiple warehouses and ensuring accurate stock counts.

These are just a few examples. As MongoDB continues to evolve, it will become an even more versatile database solution.

4. Choosing the Right Database in 2025: A Decision Framework

By 2025, the lines between PostgreSQL and MongoDB will continue to blur. Choosing the right database will depend on your specific needs. Let’s look at a framework to help you decide.

I. Data Structure and Relationships

How complex are the relationships between your data? This is a key question.

• Relational Data: If your data has many connections and needs to be organized in a strict way, PostgreSQL is a good choice. Think of an e-commerce platform with customers, orders, products, and categories. These all relate to each other.
• Document Data: If your data is mostly self-contained and doesn’t need many connections, MongoDB might be better. Think of storing blog posts, each with its own title, content, and tags.

II. Scalability Requirements

How much data do you expect to store and how many people will use your application?

• PostgreSQL: PostgreSQL scales well, especially with techniques like read replicas and connection pooling. Vertical scaling (adding more power to a single server) is also effective.
• MongoDB: MongoDB is designed for horizontal scaling (adding more servers to a cluster). This can be good for very large datasets and high traffic. However, make sure to carefully plan your sharding strategy. ⚠️ Some argue that MongoDB’s scalability benefits are overstated, so be sure to test your specific use case.

Consider these factors when thinking about scalability:

• Read-heavy vs. Write-heavy: Is your application mostly reading data or writing data?
• Data Volume: How much data will you store?
• User Load: How many users will be accessing the database at the same time?

III. Transaction Needs

Do you need strong guarantees that your data is always correct?

• ACID Transactions: If you need ACID transactions (Atomicity, Consistency, Isolation, Durability), PostgreSQL is the clear winner. This is important for financial applications and other situations where data integrity is critical.
• Eventual Consistency: MongoDB offers eventual consistency, which means that data might not be immediately consistent across all servers. This is fine for some applications, but not for others.

IV. Development Team Expertise

What skills does your team already have?

• SQL Knowledge: If your team knows SQL, PostgreSQL will be easier to learn and use.
• NoSQL Experience: If your team has experience with NoSQL databases, MongoDB might be a better fit.

If you want to switch databases, consider training your team. There are many online courses and resources available.

V. Cost Considerations

How much will it cost to use each database?

• PostgreSQL: PostgreSQL is open source, so there are no licensing fees. However, you will need to pay for hardware, maintenance, and development.
• MongoDB: MongoDB has a community edition that is free to use. However, if you need enterprise features, you will need to pay for a commercial license.

Think about the total cost of ownership (TCO) for both databases. This includes:

• Licensing Fees: If applicable
• Hardware Costs: Servers, storage
• Maintenance Costs: Database administration, backups
• Development Costs: Development time, training

By considering these factors, you can choose the right database for your needs in 2025 and beyond. 💡

Recommended reading

• SQL Optimization Course
• SQL optimization knowledge
• Database industry knowledge

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• SQLFlash + PostgreSQL