As a Python Developer at IntelliCode Solutions, I was often tasked with optimizing our Python application's performance. In one particular instance, we had a database querying function that was running significantly slower than expected. This function was critical in generating reports, so it was crucial to optimize its performance.
Initially, to diagnose the issue, I utilized Python's built-in 'cProfile' module to profile the function. It quickly became clear that our database queries were the bottleneck. We were making several separate database calls within a loop, resulting in a latency issue.
To resolve this, I batched the SQL queries together, reducing the overall calls to the database. Additionally, I optimized our code further by using Python's List Comprehension feature instead of traditional loops, which are faster due to Python's internal implementation. After these measures were implemented, the function execution time was reduced by an impressive 75%, drastically improving the efficiency of our reporting feature.