
Observability Lessons NVIDIA and CoreWeave
NVIDIA now ships VictoriaMetrics as the default metrics backed in their self-managed Cloud Functions, and CoreWeave uses VictoriaMetrics for infrastructure monitoring and AI capabilities.

NVIDIA now ships VictoriaMetrics as the default metrics backed in their self-managed Cloud Functions, and CoreWeave uses VictoriaMetrics for infrastructure monitoring and AI capabilities.
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Read how to use vmalert’s replay mode to retroactively evaluate recording and alerting rules with SLO objective as example.

This ‘VictoriaMetrics 2022 Momentum Milestones’ blog post provides a summary of this year’s main achievements with our top features, blogs and talks.
One of the latest benchmarks we made was ‘VictoriaMetrics: scaling to 100 million metrics per second’. While the fact of such scale for VictoriaMetrics is noteworthy on its own, the benchmark tool used to generate that load is usually overlooked. In this blog post I’ll explain in more details the challenge of running such benchmarks.

An overview of the latest features added to VictoriaMetrics backup components.

We’re participating in three conferences until the end of the year as sponsors and speakers: KubeCon NA, OSMC & OSA Con 2022. This blog outlines our participation at each one of them with details on how to meet us or listen to our talks.

In monitoring, the term cardinality defines the number of unique time series stored in TSDB. The higher is cardinality, the more resources are usually required for metrics processing and querying. Let’s see how Cardinality explorer can help us here.

VictoriaMetrics is a monitoring solution. It was designed to collect and process telemetry from many systems, provide a retrospective view, and forecast metrics for capacity planning. But what about monitoring VictoriaMetrics itself?

Benchmarking VictoriaMetrics and Grafana Mimir on the same hardware

An article about how to remove a storage node from a VictoriaMetrics cluster gracefully
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