Architecture Inversion: Scale By Moving Computation Not Data

In the blog post, Jon Bratseth discusses how large-scale data systems, like those used by TikTok for personalized video recommendations, face challenges in efficiently processing vast amounts of data to deliver high-quality results. Traditional methods of data comparison are inefficient for the scale of billions of videos and users, prompting the use of indexing techniques to streamline data retrieval. However, even with optimized indexing and rescoring strategies, the data movement required for detailed scoring remains a bottleneck. The concept of "Architecture Inversion," where computation is integrated directly into data storage systems rather than moving data to separate compute nodes, is presented as a solution to this problem. This approach, initially implemented by major companies like Yahoo, is becoming increasingly relevant for a wider range of applications due to advances in machine learning algorithms and the rising demand for delivering high-quality data to large language models (LLMs). Vespa.ai is highlighted as a platform that facilitates this architectural shift, allowing for local computation on stored data, which enhances efficiency and performance.