Case Study: Uber

Challenge: Maintain metrics at scale

Application: M3 platform, a turnkey, scalable, configurable store for Prometheus metrics

Solution: Prometheus and a homegrown metrics store

Storage is now 8.53x more cost effective per metric per replica

4x faster to set up monitoring systems in data centers

Combined high/low urgency notifications per week went from 25 to 1.5

Almost zero time to onboard systems that already had native Prometheus support

6.6 billion time series stored, 500 million metrics-per-second aggregated, 20 million resulting metrics-per-second persisted to storage globally

“Prometheus added a ton of very high quality libraries and common monitoring metrics exporters, and the way in which it exported its metrics made it very easy for us to continue to pull in existing software and use that at scale.” —Rob Skillington, Technical Lead for Metrics and Systems Monitoring, Uber

“The open governance and wide industry participation helped us feel at ease that Prometheus would be compatible with almost any popular open source software that we would need to monitor now and in the future.” —Rob Skillington, Technical Lead for Metrics and Systems Monitoring, Uber

Read the Case Study