Documenting code, one commit at a time.
We've recently refactored and optimized our AI token usage tracking to improve performance and maintainability. This involved extracting duplicated queries and enhancing user filtering.
The primary focus was on improving the efficiency of retrieving daily and monthly tenant AI usage data. This was achieved through two key changes:
Ensuring application stability and দ্রুত problem resolution hinges on effective error logging. Silent catch blocks, while seemingly benign, can mask critical failures, making them invisible to monitoring systems. This post outlines a strategy for replacing these silent catch blocks with proper logging mechanisms, enhancing overall application observability.
Effective logging is crucial for monitoring and debugging applications, especially in multi-tenant environments. This post explores how to enhance application logging by incorporating tenant-specific context, enabling better isolation and analysis of log data.
In a multi-tenant system, logs from different tenants are often interleaved,
Developers often strive for efficiency, but sometimes, optimizing too early can lead to more problems than solutions.
Optimizing code before understanding its performance bottlenecks can waste time and introduce unnecessary complexity. It's tempting to micro-optimize, but it's crucial to identify the actual performance-critical sections first.
This post delves into optimizing SQL queries for product ranking and data aggregation, focusing on common pitfalls and effective strategies to enhance performance and accuracy. We'll explore techniques to address memory errors, improve query speed, and ensure data integrity when dealing with complex relationships and large datasets.
