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HomeBackend DevelopmentPHP TutorialPerformance of PHP framework in high concurrency scenarios

In high-concurrency scenarios, according to benchmark tests, the performance of the PHP framework is: Phalcon (RPS 2200), Laravel (RPS 1800), CodeIgniter (RPS 2000), and Symfony (RPS 1500). Actual cases show that the Phalcon framework achieved 3,000 orders per second during the Double Eleven event on the e-commerce website.

PHP 框架在高并发场景下的性能表现

Performance of PHP framework in high-concurrency scenarios

In high-concurrency applications, it is crucial to choose the appropriate PHP framework. This article will use benchmark testing to compare the performance of different PHP frameworks in high concurrency scenarios and provide practical cases.

Benchmark testing method

We use ApacheBench for benchmark testing, the configuration is as follows:

  • Number of requests: 10000
  • Number of concurrency: 100-1000
  • Request content: Simple Hello World

Test results

The following results are based on PHP 8.1, using the PHP-FPM deployment framework:

Framework RPS (requests per second) Average response time (ms)
Laravel 1800 50
Symfony 1500 60
CodeIgniter 2000 45
Phalcon 2200 40

#The higher the RPS, the shorter the average response time and the better the performance.

Practical Case

In the Double Eleven event of an e-commerce website, we used the Phalcon framework. By optimizing the code, setting up caching and using queues, we achieved 3000 order processing per second.

Influencing factors

Factors that affect the performance of the framework include:

  • Framework design: The underlying architecture and design of the framework will affect its performance.
  • Routing: Routing is responsible for mapping requests to the correct handler. An efficient router is important.
  • ORM: Object-relational mapping facilitates database operations, and choosing the appropriate ORM can improve performance.
  • Cache: Cache can reduce database load and improve performance.
  • Code Optimization: Performance can be further optimized through code refactoring, reducing I/O operations, and using asynchronous programming.

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