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:
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.
Our application relies on the AutoSyncGeneratePostJob to synchronize data. Recently, we encountered issues with exceeding the rate limits imposed by Azure, leading to job failures and data inconsistencies. To address this, we implemented rate-limiting middleware specifically for this job.
The AutoSyncGeneratePostJob frequently triggered requests to Azure services.
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.
