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  [ home ](https://msaied.com)    [ articles ](https://msaied.com/articles)    Insert 1M Rows to Laravel DB: 6 Ways on MySQL / SQLite / PostgreSQL (Benchmarks)        On this page       1. [  Key Considerations for Large Data Inserts ](#key-considerations-for-large-data-inserts)
2. [  Benchmarking Approaches ](#benchmarking-approaches)
3. [  Performance Insights ](#performance-insights)
4. [  Takeaways ](#takeaways)

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 Insert 1M Rows to Laravel DB: 6 Ways on MySQL / SQLite / PostgreSQL (Benchmarks) 
==================================================================================

     14 May 2026      2 min read    ![Mohamed Said](https://cdn.msaied.com/01KT78WE565VEMM3PSNQAAB0MJ.jpg)  Mohamed Said  

       Table of contents

1. [  01   Key Considerations for Large Data Inserts  ](#key-considerations-for-large-data-inserts)
2. [  02   Benchmarking Approaches  ](#benchmarking-approaches)
3. [  03   Performance Insights  ](#performance-insights)
4. [  04   Takeaways  ](#takeaways)

 When dealing with large datasets in Laravel, efficiently inserting a significant number of records is crucial for performance. This article, based on a detailed video tutorial, benchmarks six distinct methods for seeding one million rows into a database, covering MySQL, SQLite, and PostgreSQL.

While the full details and benchmarks are reserved for premium members, the core objective is to compare the speed of various insertion strategies. The testing environment includes a local MacBook Pro and a budget-friendly $6 Laravel Forge server, providing insights into performance across different hardware and database systems.

Understanding these methods can help developers optimize their data seeding processes, whether for development, testing, or production environments.

Key Considerations for Large Data Inserts
-----------------------------------------

When inserting a large volume of data, several factors come into play:

- **Database System:** MySQL, PostgreSQL, and SQLite each have unique performance characteristics.
- **Insertion Method:** Different approaches, such as single inserts, bulk inserts, or using specific Laravel features, will yield varying results.
- **Hardware/Server Resources:** The performance of the server or local machine directly impacts insertion speed.

### Benchmarking Approaches

The tutorial explores multiple techniques to insert 1 million records. While specific code examples are part of the premium content, the general categories of methods likely include:

- Standard Eloquent `create()` in a loop.
- Using Eloquent `insert()` for bulk operations.
- Leveraging the Query Builder for optimized inserts.
- Potentially using database-specific bulk loading utilities.
- Strategies for handling transactions to improve efficiency.

### Performance Insights

Each method is tested against different database systems (MySQL, SQLite, PostgreSQL) and on varying hardware. The results aim to highlight which approaches are most performant for large-scale data insertion tasks within a Laravel application.

Takeaways
---------

- The choice of database significantly impacts insertion speed.
- Bulk insertion methods are generally faster than individual record inserts.
- Server resources play a critical role in overall performance.
- Optimizing transaction handling can yield performance gains.

For a comprehensive understanding and detailed benchmarks, please refer to the original article.

[Source: Insert 1M Rows to Laravel DB: 6 Ways on MySQL / SQLite / PostgreSQL (Benchmarks)](https://laraveldaily.com/post/insert-1m-rows-to-laravel-db-6-ways-on-mysql-sqlite-postgresql-benchmarks)

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 Frequently Asked Questions 
----------------------------

  2 questions  

     Q01  What database systems are benchmarked for inserting 1 million rows in Laravel?        The benchmark covers MySQL, SQLite, and PostgreSQL database systems. 

      Q02  Are there specific Laravel features recommended for fast data insertion?        The tutorial benchmarks six different approaches, likely including Eloquent methods, Query Builder optimizations, and bulk insertion techniques to identify the fastest options for large datasets. 

  Continue reading

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