filtering a paginated eloquent collection
Stefan Bogdanescu
Founder & Senior Architect · 2026-06-29
# Filtering a Paginated Eloquent Collection Without Losing Pagination
As senior developers working with Laravel and Eloquent, we constantly encounter scenarios where we need to filter large datasets retrieved via pagination. The problem you are facing—losing the pagination context when using methods like `each()` on a paginated result—is a common pitfall rooted in how Eloquent handles database operations versus in-memory collection manipulation.
This post will explain why your current approach fails and provide the robust, performant solution for filtering paginated data while perfectly preserving the pagination links.
## The Pitfall: Why `each()` Breaks Pagination
When you execute `$models = User::orderBy('first_name', 'asc')->paginate(20);`, Eloquent executes a SQL query that includes `LIMIT` and `OFFSET`. This mechanism is what manages the pagination metadata (total count, current page, next/previous links).
The issue arises when you iterate over this result using methods like `each()` or casting it to a standard collection. These methods operate on the *already fetched* data in memory. When you then apply conditional filtering inside that loop:
```php
$models = $models->each(function($model) use ($filters) {
// Filtering logic here...
if ($model->type == $filters['type']) {
return $model; // This returns a filtered subset of the collection
}
return null; // or some other mechanism to exclude it
});
```
You are modifying the *contents* of the collection, but you are not affecting the underlying pagination structure that was calculated by the database query. The paginator object itself is often discarded or returned as a simple collection, losing the crucial `links` and `total` information.
## The Correct Approach: Filtering at the Database Level
The most efficient and correct way to filter data while maintaining pagination integrity is to push the filtering logic down to the database using Eloquent's query builder methods (`where()`, `whereIn()`, etc.). This ensures that only the relevant records are selected *before* Laravel calculates the final result set for pagination.
By applying filters directly to the query, you let the database handle the heavy lifting of filtering and counting, which is significantly faster than fetching thousands of records only to discard most of them in PHP memory.
### Implementing Filtered Pagination
Instead of paginating first and then filtering, we apply all necessary constraints within the initial query chain:
```php
$filters = [
'type' => 'admin',
'state_id' => 5,
];
$models = User::where('profile.state_id', $filters['state_id'])
->where('type', $filters['type'])
->orderBy('first_name', 'asc')
->paginate(20);
// The pagination links and total count are perfectly preserved here!
```
### Handling Complex Filtering with Relationships
For filtering based on related models, such as filtering users by the state of their profile (as suggested in your original snippet), you must use Eloquent's relationship methods like `whereHas` or nested `where` clauses:
```php
$filters = [
'state_id' => 5,
];
// Filtering by a related model constraint
$models = User::whereHas('profile', function ($query) use ($filters) {
$query->where('state_id', $filters['state_id']);
})
->orderBy('first_name', 'asc')
->paginate(20);
```
This approach ensures that the SQL query generated is highly optimized, performing the filtering directly on the database index. This principle of letting the database do the heavy lifting is fundamental to writing scalable Laravel applications, aligning perfectly with best practices promoted by resources like [Laravel Company](https://laravelcompany.com).
## Conclusion
To summarize, never attempt to filter a paginated Eloquent collection after it has been retrieved if you intend to preserve pagination metadata. Instead, always incorporate your filtering criteria directly into the initial Eloquent query using `where()` clauses. This strategy is not only cleaner and more readable but, most importantly, vastly more performant by leveraging the database's power instead of wasting resources on in-memory processing. By adhering to this pattern, you ensure that your application remains fast and scalable, regardless of