Optimizing M3: How Uber Halved Our Metrics Ingestion Latency by Forking the Go Compiler

In Uber’s New York engineering office, our Observability team maintains a robust, scalable metrics and alerting pipeline responsible for detecting, mitigating, and notifying engineers of issues with their services as soon as they occur. Monitoring the health of our thousands of microservices helps us ensure that our platform runs smoothly and efficiently for our millions of users across the globe, from riders and driver-partners to eaters and restaurant-partners. A few months ago, a routine deployment in a core service of M3, our open source metrics and monitoring platform, caused a doubling in overall latency for collecting and persisting metrics to storage, elevating the metrics’ P99 from approximately 10 seconds to over 20 seconds.
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How Uber Monitors 4,000 Microservices

With 4,000 proprietary microservices and a growing number of open source systems that needed to be monitored, by late 2014 Uber was outgrowing its usage of Graphite and Nagios for metrics. They evaluated several technologies, including Atlas and OpenTSDB, but the fact that a growing number of open source systems were adding native support for the Prometheus Metrics Exporter format tipped the scales in that direction. Uber found with its use of Prometheus and M3, Uber’s storage costs for ingesting metrics became 8.
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The Billion Data Point Challenge: Building a Query Engine for High Cardinality Time Series Data

Uber, like most large technology companies, relies extensively on metrics to effectively monitor its entire stack. From low-level system metrics, such as memory utilization of a host, to high-level business metrics, including the number of Uber Eats orders in a particular city, they allow our engineers to gain insight into how our services are operating on a daily basis. As our dimensionality and usage of metrics increases, common solutions like Prometheus and Graphite become difficult to manage and sometimes cease to work.
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