The old debate is over: in 2025, you don’t have to choose between a rigid relational database and a flexible NoSQL option.
Modern relational databases like PostgreSQL now handle JSON data natively. This hybrid approach is winning. A 2025 developer survey named PostgreSQL the most-used database by professionals, signaling a major shift in the industry.
This gives you the best of both worlds: the rock-solid transactional safety of SQL and the schemaless flexibility of NoSQL, all in one system.
This guide breaks down this powerful hybrid approach. We’ll show you the key benefits of using native JSON in a relational database and explain the best practices for building faster, more flexible applications with this modern technique.
Table of Contents
The Hybrid Model: Relational and Schemaless Data
For years, developers had to choose between two worlds: the strict, organized structure of a relational database (like MySQL) and the flexible, schemaless model of a NoSQL database. In September 2025, you don’t have to choose. Modern relational databases now offer a powerful hybrid model, letting you store flexible JSON data right inside your traditional tables. Let’s look at why this is a game-changer.
From Clunky Workarounds to Native JSON: The Best of Both Worlds
The old way of storing dynamic data in a relational database was a clunky and slow pattern that was a nightmare to work with. In response to the popularity of flexible databases like MongoDB, traditional databases like MySQL introduced a native JSON column type.
This isn’t just a simple text field. It’s a highly optimized, binary format that automatically validates your JSON to make sure it’s correct. It also gives you a rich set of tools to query and manipulate the data inside that column.
The Big Win: You get the transactional integrity and reliability of a relational database, plus the flexibility of schemaless data. It’s the best of both worlds, and it has made the old ways of doing things an anti-pattern.
When Should You Use a JSON Column? The Perfect Use Cases
JSON columns are a powerful tool, but they’re not for everything. They are not a replacement for good relational design. They are for a specific job: storing “custom properties”—data that is descriptive and often changes from one record to the next.
Here are the perfect use cases:
- Dynamic Product Attributes: Think of an e-commerce store. A t-shirt needs size and color, while a laptop needs RAM and CPU. A JSON column is perfect for storing these different attributes without creating a messy table.
- User Preferences and Settings: Storing things like a user’s theme choice, notification settings, or dashboard layout is a great use for a JSON column.
- Third-Party API Data: When you’re saving data from an external API, its structure might change unexpectedly. A JSON column can handle that without breaking your application.
- Dynamic Forms: For apps that let users build their own surveys or custom contact forms.
- IoT Data: When different models of a device, like a sensor, send back different types of data payloads.
Foundational Implementation: From Migration to Model
So, you’re sold on the idea of using JSON columns in your Laravel app. The great news is that Laravel makes the implementation incredibly simple and elegant. In September 2025, you can get this powerful feature working with just two key steps: defining the column in your migration and setting up a cast in your Eloquent model.
Step 1: Define the JSON Column in Your Migration Database icon
First, you need to tell your database about the new column. When you create your migration, you simply use the ->json() method on your table schema.
PHP
// database/migrations/xxxx_xx_xx_xxxxxx_create_products_table.php
public function up(): void
{
Schema::create(‘products’, function (Blueprint $table) {
$table->id();
$table->string(‘name’);
$table->json(‘properties’); // Creates a native JSON column
$table->timestamps();
});
}
A Critical Pro-Tip: This is important. Make sure your production database supports native JSON columns (like MySQL version 5.7 or newer). While this works fine in SQLite for local development, it can fail when you deploy if your production database is too old.
Step 2: Let Eloquent Do the Magic with Attribute Casting
This is where the real magic happens. To make Laravel automatically handle converting your JSON to and from a PHP array, you just need to add one line to your Eloquent model. Add your column name to the $casts property and tell it to cast to an array.
PHP
// app/Models/Product.php
class Product extends Model
{
protected $casts = [
‘properties’ => ‘array’,
];
}
With this in place, you can now work with $product->properties as a normal PHP array in your code, and Eloquent will automatically handle the JSON encoding and decoding behind the scenes.
Never manually json_encode() your data before saving; the cast does it for you! Just assign a regular PHP array to the property, and Eloquent will handle the rest.
Putting It All Together: A Practical Example
So what does this look like in a real app? It’s incredibly clean.
- The Form: In your Blade view, you can create a simple form with inputs for key-value pairs.
- The Controller: Your controller logic becomes dead simple. Thanks to the casting, you can just pass the request data straight to your model to create the new product record.
- The View: When you want to display the data, you just access the properties attribute on your model as a normal PHP array and loop through it. The entire process is seamless.
Advanced Data Integrity and Structure
Once you’ve mastered the basics of using JSON columns in Laravel, it’s time to level up. In September 2025, the best developers aren’t just storing JSON; they’re using advanced techniques to ensure their data is clean, structured, and safe. Let’s look at three powerful patterns for taking your JSON data handling to the next level.
1. Clean Your Data Before It’s Saved with Mutators
Sometimes, the data you get from a form can be messy, with empty fields you don’t want to save in your database. An Eloquent mutator is a special method you can add to your model to automatically clean up this data before it gets stored. For example, you can use a mutator to automatically filter out any empty key-value pairs from a list, ensuring the JSON in your database is always clean and concise.
2. The New Best Practice: Cast to Objects, Not Just Arrays
Casting your JSON to a simple array is easy, but it’s not very safe. You’re left guessing what keys are available, and a simple typo in a key name can cause a runtime error. The modern, professional way to handle this is to cast your JSON directly to a Data Transfer Object (DTO).
Using a popular package like spatie/laravel-data, you can create a simple class that defines the “shape” of your JSON data (e.g., an Address object with street and city properties). Then, you just tell your Eloquent model to cast the JSON column to that class.
The Big Win: Now when you access the property, you get a real object, not just an array. This gives you IDE autocompletion and type safety, which makes your code far more readable, maintainable, and less buggy. It’s the best of both worlds: a flexible database column with the safety of a structured object in your code.
3. Enforce Your Schema with Advanced Validation
The flexibility of JSON is great, but it can be a double-edged sword. Without strict validation, you can easily end up with garbage data in your database. Laravel’s Validator is the perfect tool to prevent this.
You can use “dot” notation and the * wildcard in your validation rules to enforce a strict structure on your incoming JSON data. For example, you can write a rule that requires every object in a list of properties to have both a key and a value. This ensures that bad data never even makes it to your model, making your whole application more robust.
Querying and Updating JSON Data
You’ve set up your JSON columns in Laravel, but how do you actually work with the data inside them? In September 2025, Laravel gives you a powerful set of tools for querying your JSON data. However, when it comes to updating that data, there’s a critical challenge you need to understand to avoid serious bugs.
1. The Simple Way to Query: The ‘->’ Operator
The most common way to query a JSON column is with the -> operator. It lets you “point” to a specific key inside your JSON data directly within your where clause. You can even chain it to access nested data.
For example, to find all products with a ‘Blue’ color property, your code would be this simple:
PHP
$blueProducts = Product::where(‘properties->color’, ‘Blue’)->get();
2. Advanced Queries for JSON Arrays
For more complex situations, especially when your JSON contains an array of values (like tags), Laravel provides a set of dedicated, easy-to-read methods.
The most powerful of these is whereJsonContains(). It lets you find all records where a JSON array includes a specific value. For example, to find all products tagged as ‘organic’:
PHP
$organicProducts = Product::whereJsonContains(‘properties->tags’, ‘organic’)->get();
3. The Critical Challenge: How to Update JSON Data Safely
This is the most important part of this guide. When you update a single, nested value within a JSON column, you can easily create serious data consistency bugs if you’re not careful.
The Danger: Race Conditions. Imagine two people trying to update the same product at the exact same time. The last person to save their changes will accidentally overwrite the changes made by the first person. That first update is just lost forever. This is called a “race condition.”
The Unsafe, Common Pattern. The most common way to update JSON in PHP—reading the data into your code, changing it, and then saving it back—is not safe. It’s not an “atomic” operation, and it’s how race conditions happen.
The Only Truly Safe Method. The only 100% reliable way to update a nested JSON value is to use a raw database expression. This pushes the work down to the database itself, which can perform the update as a single, atomic operation, preventing any data from being lost.
The Big Takeaway: To safely update JSON data in a high-traffic app, you can’t just rely on Laravel’s simple tools. You must be familiar with the native JSON functions of your database (like JSON_SET for MySQL).
The Performance Equation: Indexing and Optimization
The flexibility of using JSON columns in your database comes with a huge performance trap if you’re not careful. By default, querying the data inside a JSON column is incredibly slow on large tables. In September 2025, the solution is to use a powerful database feature called Generated Columns to create indexes, and Laravel makes this easy.
The Unindexed Trap: Why Your JSON Queries Are So Slow
By default, your database can’t “see inside” a JSON column to index the data within it. So when you run a query to find all products where the color property is ‘Blue,’ the database has no choice but to perform a full table scan.
This means it has to load every single row from the table, open up the JSON for each one, and then check the value. On a large table with hundreds of thousands of rows, this is a disaster—a single query can take over 5 seconds, making your app feel broken to the user.
The Solution: Indexed Virtual Columns in Your Migration
The key to making these queries fast is to create a special “virtual” column that the database can index. In MySQL, this is called a Generated Column. You can’t index a JSON key directly, but you can index this virtual column.
In Laravel, you can do this easily right inside a database migration using the ->virtualAs() and ->index() methods.
PHP
// database/migrations/xxxx_xx_xx_xxxxxx_add_virtual_color_to_products_table.php
public function up(): void
{
Schema::table(‘products’, function (Blueprint $table) {
$table->string(‘product_color’)
->virtualAs(“JSON_UNQUOTE(JSON_EXTRACT(properties, ‘$.color’))”)
->index();
});
}
This migration creates a new, indexed product_color column that is always derived from the color key inside your JSON. Now, you can query this new, fast column instead of the slow JSON path. This one change can make your queries over 36 times faster.
The Big Takeaway: The ‘Schemaless’ Trade-Off
This powerful solution comes with an important strategic trade-off.
The initial appeal of JSON columns is that they are “schemaless”—you can add new properties without needing a database migration. However, to make any of those new properties fast to query or sort on, you must run a migration to create an indexed virtual column for it.
This means you have to anticipate which custom properties you will need to filter by and then formalize those paths in your database schema. You still get the flexibility for storing data, but high performance is only possible on the paths you explicitly plan for and index.
Community Insights and Architectural Analysis
You’ve seen the how-to, but what’s it actually like to work with JSON columns in a real project? In September 2025, the developer community has a lot of experience and strong opinions. Let’s synthesize their insights to understand the real-world trade-offs and avoid common pitfalls.
The “Slippery Slope”: Real-World Advice from Developers
A common story you’ll hear on forums like Reddit is about the “slippery slope” of JSON columns. A developer starts using one for its flexibility but later regrets it when they realize how complex it is to query and safely update the nested data.
Here’s the most important piece of advice from experienced developers: If you find yourself needing to frequently and safely update a specific piece of data inside your JSON, that’s a huge red flag. It’s a sign that the data should have been its own regular column or table from the start.
However, developers agree that when used for the right job—like storing user settings, unstructured metadata, or dynamic product attributes—JSON columns are an excellent tool.
The Final Tally: A Balanced Look at the Pros and Cons
Here’s a quick summary of the key trade-offs.
The Good:
- Schema Flexibility: You can add new attributes without needing to run a database migration.
- Simpler (for simple data): Can help you avoid creating an extra table and a complex join for simple parent-child data.
The Bad:
- Complex Queries: Querying the data inside the JSON is harder and less intuitive than standard SQL.
- Slow by Default: Queries are very slow on large tables unless you set up indexed virtual columns, which adds complexity.
- No Relational Integrity: You lose the ability to use database features like foreign keys or “not null” constraints on the data inside your JSON.
- Easy to Misuse: It’s very easy to fall into the trap of creating a “database within a database,” which can become a maintenance nightmare.
The Verdict: The Right Tool for the Job
So, if JSON columns have these challenges, what’s the alternative for storing dynamic data? For years, developers used a pattern called EAV, but the community verdict is clear: EAV is an obsolete anti-pattern in the modern world. It’s slow, and the queries are a mess.
Despite their trade-offs, native JSON columns are the vastly superior and correct tool for storing semi-structured data in a relational database today.
Final Recommendations: When Are JSON Columns the Right Choice?
So, when should you use a flexible JSON column, and when should you stick with a traditional, structured table? In September 2025, this is a key architectural decision. Getting it right will save you from major headaches down the road. Here’s a simple framework to help you choose.
Your Quick Decision-Making Guide
Before you decide, ask yourself these questions about your data:
- Is the structure consistent or does it change? If every record will have the same, well-defined fields (like a user’s name and email), use a traditional table. If the attributes change from one record to the next (like product specs), a JSON column is a great fit.
- How will you query it? If you’ll mostly be reading the whole block of data at once, JSON is fine. If you need to run complex filters, sorts, or reports on the individual pieces of data inside, you need a traditional table.
- How important are database-level rules? If you need strict rules like foreign keys to link to other tables, a traditional table is the only way. If you can handle the validation in your app’s code, JSON is an option.
When to Use a JSON Column: The Perfect Use Cases
JSON columns are the perfect choice for storing a collection of semi-structured, descriptive attributes that you usually read and write as a single unit. They are the ideal modern solution for:
- User-configurable settings and preferences.
- Storing non-critical, variable metadata from an external API.
- Defining the structure of user-generated content, like custom forms.
In these cases, the flexibility of a schemaless format is a huge win.
When to Avoid a JSON Column: The Anti-Patterns
The biggest mistake you can make is using a JSON column when your data is actually relational. It’s an anti-pattern that will cause long-term pain. Avoid them if:
- You’re storing a list of distinct things, like a user’s blog posts or a product’s tags. These almost always belong in their own, separate database table.
- You need to run complex reports or aggregations on the data inside the JSON.
- You need to frequently and safely update individual values inside the JSON from multiple users at once.
- The data is a core part of your app’s main search and filter functionality.
In these cases, the initial convenience of a single JSON column will quickly turn into a nightmare of bad performance, data integrity issues, and complex code. Stick with a traditional relational design.