The podcast discusses building a scalable spam fighting system using real-time analytics and AI. It also explores the evolution of face detection and recognition, tactics to combat comment and sign up spam, handling asynchronous rebuilds for indexing, and challenges in recommendations for live streams of buying and selling.
Incremental indexing is a major challenge in vector search algorithms, especially in high-dimensional spaces, but Rockset has made significant efforts to address it in their real-time analytics platform.
Metadata filtering is a critical issue in vector search, and it should be integrated seamlessly into the system design to optimize results efficiently, which is exactly what Rockset has done.
Deep dives
Rockset VP of Engineering discusses real-time analytics and spam fighting
Lewis Brandy, the VP of Engineering at Rockset, joins the Stack Overflow podcast to discuss the intersection of real-time analytics and spam fighting. Lewis shares his background in software engineering and his experience working on infrastructure for fighting spam at Facebook. He highlights the importance of building scalable spam fighting systems and explains how real-time analytics and AI can be combined with spam fighting to enhance effectiveness. Lewis also dives into the challenges of incremental indexing in vector search algorithms and the importance of metadata filtering in hybrid search. He concludes by showcasing a case study from Whatnot, a company that leverages live streaming and vector recommendations for buying and selling.
The challenges of incremental indexing in vector search algorithms
Lewis Brandy explains one of the major hurdles in vector search algorithms, which is the problem of incremental indexing. He compares it to the challenges of inserting new elements into a balanced binary search tree, causing the tree to become unbalanced and resulting in a linear lookup time instead of logarithmic. Lewis emphasizes that incremental indexing is even more difficult in high-dimensional spaces due to the curse of dimensionality. However, he mentions that Rockset has invested significant effort in addressing this problem, striving to make incremental indexing as efficient as possible in their real-time analytics platform.
The significance of metadata filtering in vector search
Lewis Brandy discusses another critical issue in vector search, which is metadata filtering. He explains how metadata filtering combines semantic and lexical search capabilities, allowing users to filter their queries based on specific metadata criteria alongside vector-based recommendations. Lewis highlights the complexity of optimizing the use of different types of indexes to achieve the desired results efficiently. He emphasizes that metadata filtering should be considered as a fundamental aspect of the system design rather than an afterthought, and Rockset has integrated it seamlessly into their platform.
A case study: Real-time data analytics and vector recommendations in buying and selling
Lewis Brandy presents a case study involving Rockset's collaboration with Whatnot, a platform for live streaming buying and selling. Whatnot faced the challenge of recommending relevant streamers to potential buyers in real-time while considering the online status of the streamers. This scenario required a combination of vector search for recommendations and metadata filtering to ensure real-time updates and availability. Lewis highlights that this use case exemplifies the perfect use of Rockset's capabilities, addressing the complex challenges of real-time analytics, vector-based recommendations, and metadata filtering for optimal user experience.