Kubeflow Pipelines and Prometheus Alertmanager: Filtering Transient Component Spikes

Monitoring Pipeline Component Stability

Kubeflow Pipelines rely on Prometheus to scrape metrics from the underlying Kubernetes pods executing individual components. While basic pod health is tracked, the integration between the KFP API server and Prometheus Alertmanager often triggers notifications for transient resource spikes that do not actually result in pipeline failure.

The goal is to implement a monitoring strategy that distinguishes between a temporary memory burst during a data-heavy transformation and a sustained leak that requires intervention, without introducing excessive noise into the alerting channel.

Given that alerting rules are managed via external Kubernetes manifests rather than the Kubeflow UI, there is uncertainty regarding the optimal configuration for the for duration in Prometheus rules to account for varying component execution times.

• What is the recommended approach for aligning Prometheus alert durations with the expected lifecycle of short-lived KFP component pods?
• How can custom metrics exported via the Prometheus client library be prioritized over generic pod restart counts to reduce false positives?