GoSV - Time Series in Postgres + Vitess MySQL scaling +Robot Coordination system

Details -
6pm:6:30pm : Social hour
Talk #1: Go in the Physical World - building robots coordination system at scale By Chenyun Yang, Fetch Robotics

When building a system running on robots and cloud for warehouse automation, which facilitates robot-human coordination, we identified challenges imposed by the physical world. In this talk, we discuss how we leveraged Go to address those challenges to make the system scalable and fault-tolerant.

Talk #2: Downsampling: How to manage high ingest rate metrics for long-term storage and fast querying. - Preetam Jinka from Shiftleft

Every analytics platform has to deal with ingesting high-frequency data that builds up in volume and eventually needs to be queried with low latency. A common way to address this problem is to implement downsampling or rollups, which takes large amounts of raw data and summarizes it into a lower resolution in order to reduce space requirements and improve querying performance. In this talk, I'll share how we've added downsampling in our time-series infrastructure implemented with Go and PostgreSQL, and the techniques we use to minimize ingest and query latencies at scale.

Talk #3: Vitess: First to 'Go' and still going strong by Sugu Sougoumarane from PlanetScale

The year was 2010. Vitess was the first project written in Go to go into production by serving all of YouTube's metadata. You could not watch a video without it.
Nine years later, Vitess is still going strong and is now gaining momentum as one of the few cloud-native data stores, and indefinitely scalable. We'll walk through the journey we took in vitess and talk about the difficult problems it solves today.