Uber
Presentations
Peloton - Uber's Webscale Unified Scheduler on Mesos & Kubernetes
With the increasing scale of Uber’s business, efficient use of cluster resources is important to reduce the cost per trip. As we have learned when operating Mesos clusters in production, it is a challenge to overcommit resources for latency-sensitive services due to their large spread of...
Time Predictions in Uber Eats
Uber Eats has been one of the fastest-growing meal delivery services since its initial launch in Toronto in December 2015. Currently, it’s available in over 40 countries and 400 cities. The ability to accurately predict delivery times is paramount to customer satisfaction and retention....
Conquering Microservices Complexity @Uber With Distributed Tracing
Microservices bestow many benefits on the organizations adopting them, but they come with a steep price: complexity of the resulting architecture. Distributed tracing is a recognized way of dealing with that complexity and getting back the visibility into our systems. At Uber we discovered that...
Interviews
Time Predictions in Uber Eats
What is that focus of your work today?
I’m currently leading the time prediction area for UberEats. As you can imagine, precise predictions are keys for the system’s efficiency and reliability. The features I’m working on are like predicting how long it will take to deliver the food to the eater, how long will the restaurant spend to prepare the food, etc....
Read Full InterviewConquering Microservices Complexity @Uber With Distributed Tracing
What is the focus of your work today?
I mostly work on distributed tracing, in the larger scope of overall observability.
Read Full Interview