This talk will focus on the self-managed nature of Uber's database monitoring and how we've leveraged our open source time series database M3DB to support massive multi-region scale and high cardinality monitoring.
We'll cover how we monitor applications, databases, and their interactions and how we automatically setup application specific dashboards and alerts. This includes things like the ability to alert on metrics like P99 latency and slow queries for a given application at the per-table and per-query level.
Of course, automated and fine grained monitoring requires the ability to ingest, persist, and query massive amounts of high cardinality time series data. We'll talk about the architecture of M3DB and how we've leveraged it at Uber to scale our monitoring systems to billion of unique time series and 10s of millions of data points per second.
We'll conclude the talk with an overview of our Prometheus and Kubernetes integrations, explaining how you can start leveraging M3DB for your own workloads. Finally, we'll give a brief overview of our plans to evolve M3DB into a general purpose, horizontally scalable event store.
Rob works at Uber on the Observability team and is the lead of M3, an open source distributed monitoring system that integrates with Prometheus.
Richard works at Uber on the Observability team and is a Senior Software Engineer on M3DB, an open source distributed time series database that integrates with Prometheus.