Desperate for help: Laravel eloquent performance issues crushing my app!

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Amira Saleh Author
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1 day ago Asked
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2 Replies
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My app, which ironically supports my 'Laravel Quick Fix & Consultation' service, is suffering from severe performance bottlenecks. It's incredibly frustrating because if I can't efficiently fix my own issues, how can I credibly offer a "quick fix" to others? The irony is killing me, and my users are noticing the sluggishness.

The core problem lies with Laravel Eloquent. Specific pages, particularly those with complex data listings and user dashboards, are experiencing painfully slow loading times. Database queries are taking way too long, often exceeding several seconds, leading to an overall sluggish user experience. It's especially noticeable when fetching large datasets or relationships.

I've tried everything I can think of:
  • Adding Indexes: Ensured all foreign keys and frequently queried columns have appropriate database indexes. This helped marginally but didn't solve the core issue.
  • Eager Loading: Implemented with() for all known N+1 query scenarios to reduce the number of database hits. While this improved some specific routes, the overall page load for data-heavy sections remains poor.
  • DB::listen & Debugbar: Used DB::listen and Laravel Debugbar extensively to identify slow queries. This is where I found the specific culprit queries, but optimizing them has been a nightmare.
  • Optimizing N+1 Queries: Double-checked every relationship to ensure eager loading is applied correctly. Still, some complex nested relationships seem to be causing trouble.
  • Pagination: Implemented pagination on all data tables, so we're not pulling thousands of records at once.
  • Caching: Tried some basic query caching with remember() for less volatile data, but it feels like a band-aid rather than a true Laravel optimization.
None of these attempts have fully resolved the problem, and I'm still seeing unacceptable query times.

Hereโ€™s a snippet from my logs illustrating the kind of queries I'm battling:
[2023-10-27 10:30:05] local.INFO: Query took 1250ms: SELECT * FROM `orders` WHERE `status` = 'pending' AND `created_at` > '2023-09-01 00:00:00' ORDER BY `total_amount` DESC LIMIT 5000 OFFSET 0
[2023-10-27 10:30:06] local.INFO: Query took 980ms: SELECT `users`.*, `profiles`.`bio`, `profiles`.`avatar` FROM `users` INNER JOIN `profiles` ON `users`.`id` = `profiles`.`user_id` WHERE `users`.`is_active` = 1 AND `profiles`.`last_updated` > '2023-10-01 00:00:00' ORDER BY `users`.`created_at` DESC
The first query is a simplified example, but illustrates the large offset/limit combined with filtering and ordering that's killing performance. The second shows a join that, even with eager loading, seems to struggle on larger datasets. I'm tearing my hair out trying to figure out how to properly optimize these without rewriting huge chunks of my application logic.

I'm completely stuck and desperately need some fresh eyes or expert advice on Laravel optimization. Has anyone encountered similar brutal Eloquent performance issues and found a 'quick fix' or best practice that actually works? Are there advanced Eloquent techniques I'm missing? Any insights, architecture suggestions, or specific code examples would be a lifesaver right now. Help a brother out please...

2 Answers

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Tariq Osei
Answered 1 day ago
Just a quick heads-up on a small detail, it's 'Eloquent' with a capital 'E' โ€“ easy to overlook when you're battling performance bottlenecks like these. Regarding your Laravel performance issues, here are some critical steps:
  • For your large OFFSET queries, switch immediately to keyset pagination (e.g., using where('id', '>', $lastId)->limit($perPage)) instead of traditional offset-based pagination. This avoids scanning thousands of rows on each page.
  • Beyond basic indexing, use EXPLAIN on your most problematic queries to understand their query execution plans. This will reveal if your indexes are actually being used effectively or if there are better compound index opportunities.
  • For highly complex or frequently accessed data listings, consider materializing views or selective denormalization for read-heavy operations. This is an advanced database optimization strategy that can significantly reduce runtime query complexity.
Hope this helps your conversions!
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Amira Saleh
Answered 21 hours ago

The keyset pagination tip is a game changer, can't believe I didn't think of that for the large offsets...

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