How to search in json array object with laravel query builder?
Stefan Bogdanescu
Founder & Senior Architect · 2026-06-29
# How to Search in JSON Array Objects with the Laravel Query Builder
As developers working with modern APIs, handling semi-structured data like JSON arrays is a daily occurrence. While JSON provides flexibility, querying deeply nested array elements using traditional SQL or Eloquent can often feel cumbersome. The core question we face is: how do we effectively search for specific values within an array embedded in a JSON object using the Laravel Query Builder?
This post will dive deep into the challenges of searching JSON arrays and present both functional—but less performant—and architecturally superior solutions.
## The Challenge of Searching Nested JSON Arrays
Consider the JSON structure provided:
```json
"section_4": {
"data": [
{ "grade": "3.0", "degree": "BSCSE", ... },
{ "grade": "4.0", "degree": "BSCSE", ... }
],
"type": "education",
"title": "Educations"
}
```
When you fetch this data into a database, it resides in a JSON column. Directly querying an array within that structure using standard `where()` clauses is tricky because relational databases excel at comparing flat columns, not nested array elements by default. Trying to use simple path notation often requires advanced database functions or casting.
The goal is usually to find records where *any* element in the `data` array matches a specific condition (e.g., finding all records where the grade is '4.0').
## Solution 1: Searching via JSON Operators (Database Dependent)
If your underlying database (like MySQL or PostgreSQL) supports native JSON operators, you can leverage them through Eloquent's raw query methods to perform deep searches. This method is concise but performance heavily depends on the database setup and indexing.
For example, if you wanted to find education entries where *any* grade in the array is '4.0', you would typically use functions like `JSON_CONTAINS` or specialized JSON path operators within a `whereRaw` clause.
```php
// Example using whereRaw for deep searching (Syntax varies based on DB)
$gradeToFind = '4.0';
$results = YourModel::whereRaw("JSON_CONTAINS(section_4->data, '{\"grade\": \"{$gradeToFind}\" })")
->get();
```
**Caveat:** While this works for existence checks, searching for specific values *within* an array often requires complex JSON path expressions that can be slow and difficult to maintain across different database environments. This approach bypasses the elegant simplicity Laravel aims to provide when building powerful data interactions, a principle we champion at [laravelcompany.com](https://laravelcompany.com).
## Solution 2: The Best Practice – De-normalization for Performance
As a senior developer, my strongest recommendation is to avoid searching deep within complex JSON structures in production environments if the search operation is frequent. Searching unstructured data is inherently slower than querying indexed relational columns.
The most robust and performant solution is **Data De-normalization**. Instead of storing the entire array as a single JSON blob, you should extract the critical array elements into their own related tables.
**Steps for De-normalization:**
1. **Create a Related Table:** Create an `education_records` table linked to your main model.
2. **Store Individual Records:** Each object in the `data` array becomes a separate row in this new table, containing fields like `grade`, `degree`, `start_date`, etc.
| id | user_id | grade | degree | start_date |
|----|---------|-------|--------|------------|
| 1 | 5 | 3.0 | BSCSE | ... |
| 2 | 5 | 4.0 | BSCSE | ... |
**Querying with the Query Builder:**
Once de-normalized, searching becomes trivial and extremely fast using the standard Laravel Query Builder:
```php
// Searching for all education entries where the grade is '4.0'
$results = UserMeta::whereHas('educationRecords', function ($query) {
$query->where('grade', '4.0');
})->with(['user.resume'])
->paginate(10);
```
This approach leverages the relational power of the database, allowing you to utilize indexes effectively, which is crucial for scalability. This principle—modeling your data to fit the query pattern—is central to effective application architecture in Laravel.
## Conclusion
Searching within complex JSON arrays using the Laravel Query Builder is possible, but it often leads to convoluted `whereRaw` statements that compromise performance and maintainability. For high-traffic applications, the superior architectural choice is **data de-normalization**. By converting repeating array elements into separate, indexable relational tables, you unlock the full power of the Eloquent Query Builder, resulting in cleaner code, vastly improved query speeds, and a more robust application structure. Always prioritize data modeling that supports your intended queries when building solutions on [laravelcompany.com](https://laravelcompany.com).