video1.0<iframe src="https://www.loom.com/embed/2153ccc341d3420c8a13867d108553c7" frameborder="0" width="1818" height="1363" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>13631818Loomhttps://www.loom.com13631818https://cdn.loom.com/sessions/thumbnails/2153ccc341d3420c8a13867d108553c7-293fc00aa5af3d54.gif393.522In this video, I discuss the significant performance issues we encountered when processing 10 million candidate records in memory, leading to inefficiencies and slowdowns. I outline a series of optimization steps, including moving filtering logic to the database layer, utilizing hash sets for holiday lookups, and implementing parallel processing with PLINQ to leverage multiple CPUs. These changes resulted in a remarkable 60% reduction in execution time, improving our processing speed from 11 ms to 4000 ms. I encourage you to review these optimization strategies and consider how we can implement them in our workflows to enhance efficiency. Your feedback and suggestions on these approaches would be greatly appreciated.