M3 - search results
Uber’s observability engineers present their work on distributed tracing (Jaeger), sampling (XYS), and metrics processing (M3).
Noticing increased latency in our metrics platform, Uber engineers track down a bug related to stack growth in a goroutine, resulting in a fix elevated to the Go open source GitHub repository.
Celina Ward discusses her journey to engineering, what it was like to present at Kubecon 2018, and what’s next for M3, Uber's open source metrics platform.
M3, Uber's open source metrics platform for Prometheus, facilitates scalable and configurable multi-tenant storage for large-scale metrics.
At the Uber Open Summit Sofia 2019, we showcased how open source technologies are driving the future of artificial intelligence, site reliability, and other domains.
Uber Engineering Manager Marin Dimitrov shares his best practices for motivating an engineering team, keeping connected when your headquarters office is over 6,000 miles and 10 hours away, and leveraging open source software.
The next Uber Open Summit, where we present our latest and most impactful open source projects, will be in Sofia, Bulgaria on April 20, 2019.
Uber leveraged machine learning to design our capacity safety forecasting tooling with a special emphasis on calculating a quality of reliability score.
Developed by Uber, Kraken is an open source peer-to-peer Docker registry capable of distributing terabytes of data in seconds.
In this article, we discuss Uber's journey toward a unified, multi-tenant, and scalable data workflow management system.
Ever wondered what it’s like to work in tech at Uber New York City? Just blocks from Times Square and Bryant Park, Uber’s new office in midtown Manhattan is home to more than a dozen teams, hundreds of employees (and growing), and a wide variety of engineering roles.
Brian Hsieh, Uber's Open Source program lead, reflects on open source accomplishments, project launches, and collaborations in 2018.
Part of Uber's open source M3 metrics system, our query engine can support real-time, large-scale computation and multiple query languages.
Uber’s Observability team built a robust, scalable metrics and alerting pipeline to detect, mitigate, and notify engineers of issues as they occur.
Uber developed Peloton to help us balance resource use, elastically share resources, and plan for future capacity needs.
Uber open source projects leads give updates on seven of our projects, all of which will be showcased at the upcoming Uber Open Summit 2018.
Keynote speakers include Jim Zemlin, executive director of the Linux Foundation, and Zoubin Ghahramani, chief scientist at Uber AI Labs.
In this article, we provide a general overview of how our teams leverage forecasting to build better products and maintain the health of the Uber marketplace.
In this article, we discuss how Uber Engineering uses Locality Sensitive Hashing on Apache Spark to reliably detect fraudulent trips at scale.
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