As web applications grow increasingly complex, user expectations for site performance continue to rise. Metrics like page load speed and response time directly impact user experience and business conversion rates. This article presents a comprehensive performance analysis of a video streaming page built on the ThinkPHP framework, identifying potential bottlenecks and proposing optimization strategies.

1. Performance Overview

The analysis examines an HTTP GET request recorded on August 21, 2025 at 13:11:42 with the following characteristics:

  • Request Information: HTTP/1.1 GET method
  • Execution Metrics:
    • Total runtime: 0.212385 seconds
    • Throughput: 4.71 requests/second
    • Memory consumption: 4,600.42 KB
    • Files loaded: 75
  • Database Operations:
    • 10 read queries
    • 3 write operations
  • Cache Utilization:
    • 0 cache reads
    • 1 cache write
  • Configuration: 72 configuration files loaded

2. File Loading Analysis

The system loads 75 files including framework core files, third-party libraries, application configurations, and template files. Key observations:

  • Multiple framework core files ranging from 0.72 KB to 21.07 KB
  • Third-party dependencies including Composer autoload files
  • Template files and runtime temporary files

Optimization Recommendations:

  • Combine static assets (CSS/JS) to reduce HTTP requests
  • Implement Gzip/Brotli compression for static resources
  • Deploy static content via CDN for faster delivery
  • Audit include/require statements to prevent redundant loading

3. Behavior Analysis

The execution log reveals several system behaviors:

  • Redis cache initialization
  • AliOSS plugin execution (2.193ms runtime)
  • Language package loading
  • Route resolution to www/Index/videoPlayer controller

Key Considerations:

  • Verify Redis cache configuration and hit rates
  • Evaluate AliOSS plugin performance impact
  • Simplify complex routing configurations

4. Database Query Analysis

The system executed 13 database operations including:

  • Blacklist checks (0.471ms average)
  • Video metadata retrieval (10.315ms)
  • User information queries (14.529ms)
  • Schema inspection operations (multiple 1-7ms queries)

Optimization Strategies:

  • Implement proper indexing on frequently queried fields
  • Replace SELECT * with specific column selections
  • Cache frequently accessed static data
  • Address potential N+1 query patterns
  • Optimize connection pool configuration

5. Blacklist Mechanism

The security system implements IP-based blacklisting with:

  • Individual IP checks (34.96.45.60)
  • IP range monitoring (34.96.%)
  • Logging of suspicious activity

Improvement Opportunities:

  • Ensure proper indexing on blacklist tables
  • Cache blacklist data in memory
  • Consider efficient pattern matching algorithms

6. Framework Configuration

ThinkPHP framework implementation shows:

  • Extensive configuration loading (72 files)
  • Module-based routing structure
  • Template engine utilization

Framework Optimization:

  • Streamline configuration files
  • Simplify routing rules
  • Enable template caching
  • Consider alternative template engines if needed

7. Comprehensive Optimization Recommendations

Based on the analysis, the following optimization approaches are recommended:

  1. Asset Delivery:
    • Implement resource bundling and minification
    • Enable compression for all static content
    • Migrate assets to CDN infrastructure
  2. Caching Strategy:
    • Optimize Redis cache configuration
    • Cache static database content
    • Implement blacklist caching
  3. Database Performance:
    • Comprehensive index analysis
    • Query optimization and restructuring
    • Connection pooling adjustments
  4. Framework Tuning:
    • Configuration file consolidation
    • Routing simplification
    • Template caching activation

Conclusion

This performance analysis identifies multiple optimization opportunities across various system components. Implementing these recommendations can significantly improve page load times, reduce server load, and enhance overall user experience. Continuous performance monitoring and periodic optimization reviews are essential for maintaining optimal system performance as application complexity grows.