Description
In this episode, we explore the system behind Uber's driver-matching functionality, capable of handling an incredible one million requests per second. We break down the key technologies that make it work, from H3, the hexagonal grid system for location indexing, to Ringpop, which scales services across servers. You'll hear about how GPS data is transformed into road segments, and how databases like Cassandra and Redis power this high-demand platform. Whether you're curious about large-scale systems or just fascinated by Uber's tech, this episode simplifies complex engineering into something anyone can understand.
In this episode, we'll take a look at Meta’s ambitious approach to scaling large language models. We'll explore the shift from handling many smaller models for recommendation engines to building colossal generative AI models, and the immense challenges that come with it. From hardware and...
Published 10/24/24
In this episode, let's explore how Netflix revamped their video processing pipeline, moving from a monolithic system to a microservices architecture. What drove such a major shift? You'll hear how their original platform, Reloaded, couldn’t keep up with Netflix’s rapid pace of innovation, and why...
Published 10/23/24