Agenda

In today's fast-paced world of software development, new changes (both in code and infrastructure) are being released at breakneck pace. At Uber, we roll out ~11,000 changes every week and it's important for us to have a way to quickly be able to identify and resolve issues caused by these changes. A delay in detecting issues can create a number of issues including impacts to: user experience, our ability to facilitate transactions on the platform, company revenue, and the overall trust and confidence of our users. At Uber, we have built a system called "Healthline" to help with our Mean Time To Detect (MTTD) and Mean Time To Resolve (MTTR) issues and to avoid potential outages and large-scale user impacts. Due to our ability to detect the issues in real time, this has become the go-to tool for release managers to observe the impact of canary release and decide whether to proceed further or to rollback.

In this talk we will be sharing details on how we are leveraging Apache Pinot™ to achieve this in real time at Uber scale.