M3: Open Source Metrics Engine
Trusted by startups and the world’s largest companies
Monitorama 2018
Putting billions of timeseries to work at Uber with autonomous monitoring
"At a scale of 1.5 million datapoints ingested per second, it started getting very expensive to monitor our metrics and we had to turn down our replication factor (RF) to 2 on Cassandra. With M3DB, we were able to bring RF back to 3 while also cutting down significantly on hardware / storage costs."
Prateek Rungta
Uber
7x reduction in hardware footprint
Learn more about this caseM3 meetup
June 2020
"We have 5000 domestic Walmart stores that need monitoring, so we wanted a centralized solution that was cheaper than the obvious alternatives and scalable...With M3, the advantages are very good performance on writes and queries, it’s cost effective versus alternatives, and it scales linearly."
Ron Murphy
System Telemetry Architect, Walmart Labs
50k to millions of samples per second
Learn more about this caseM3 meetup
June 2020
"We needed a metrics stack to visualize, detect, and alert on the growing machine learning infrastructure that LinkedIn relies on to provide a best in class experience for its users."
Brian McQueen
SRE, LinkedIn
100k quorum writes per second for each lightweight client
Learn more about this caseFOSDEM 2020
Querying millions to billions of metrics with M3DB's inverted index
"When querying millions or billions of metrics, you want something flexible and sublinear in speed as the queries become longer and longer the more distinct values you have. This led us to the creation of M3DB’s inverted index."
Rob Skillington
CTO and Co-Founder of Chronosphere, Former Tech Lead at Uber
3 billion+ datapoints queried per second
Learn more about this case
Monitorama 2018
Putting billions of timeseries to work at Uber with autonomous monitoring
"At a scale of 1.5 million datapoints ingested per second, it started getting very expensive to monitor our metrics and we had to turn down our replication factor (RF) to 2 on Cassandra. With M3DB, we were able to bring RF back to 3 while also cutting down significantly on hardware / storage costs."
Prateek Rungta
Uber
7x reduction in hardware footprint
Learn more about this caseM3 meetup
June 2020
"We have 5000 domestic Walmart stores that need monitoring, so we wanted a centralized solution that was cheaper than the obvious alternatives and scalable...With M3, the advantages are very good performance on writes and queries, it’s cost effective versus alternatives, and it scales linearly."
Ron Murphy
System Telemetry Architect, Walmart Labs
50k to millions of samples per second
Learn more about this caseM3 meetup
June 2020
"We needed a metrics stack to visualize, detect, and alert on the growing machine learning infrastructure that LinkedIn relies on to provide a best in class experience for its users."
Brian McQueen
SRE, LinkedIn
100k quorum writes per second for each lightweight client
Learn more about this caseFOSDEM 2020
Querying millions to billions of metrics with M3DB's inverted index
"When querying millions or billions of metrics, you want something flexible and sublinear in speed as the query’s become longer and longer the more distinct values you have. This led us to the creation of M3DB’s inverted index."
Rob Skillington
CTO and Co-Founder of Chraonosphere, Former Tech Lead at Uber
3 billion+ datapoints queried per second
Learn more about this case
What is M3
M3 is the obvious choice for Cloud Native companies looking to scale up their Prometheus based monitoring systems. M3 can be used as Prometheus Remote Storage and has 100% PromQL compatibility.
M3 was originally developed at Uber in order to provide visibility into Uber’s business operations, microservices and infrastructure. With its ability to horizontally scale with ease, M3 provides a single centralized storage solution for all monitoring use cases.
Global Scale
Proven at the largest scales in the world by storing 10s of billions of active metric time series.
Reliable
Three replicas of data with quorum writes and reads for consistency.
Highly Efficient
Optimized compression algorithm resulting in 11X compression ratio.
Performant
Proven in production to ingest more than one billion datapoints per second while serving more than two billion datapoint reads per second.
Compatible
Compatible with Prometheus, StatsD, and Carbon ingestion formats as well as PromQL and Graphite query languages.
Open & Community Focused
Open sourced under the Apache 2 license with a highly active community. Contributions welcomed.