ReadMeFeed / Community knowledge
Real questions. Useful conversations. Find the people who know your stack.
DevOps & observability · #datadog
Understanding application performance often requires linking host‑level metrics gathered by the Datadog Agent with fine‑grained container resource usage from Kubernetes. Teams want to identify whether CPU or memory throttling at the pod level correlates with spikes in request latency or error rates observed in APM traces. However, it is unclear which Agent c
Diagnosing intermittent connection pool exhaustion in Java applications requires correlating JVM metrics with specific trace spans. While the Datadog Agent captures database request spans and JVM metrics, there is a potential gap when dealing with transient spikes in connection acquisition time. If a connection pool exhausts rapidly due to a specific outlier
Goal: Determine how Datadog's adaptive sampling algorithm decides which traces to retain or discard so that bottleneck analysis remains reliable before optimization. Constraint: Public documentation describes adaptive sampling as traffic- and error-driven but does not disclose the exact weighting of latency, error, and throughput metrics, creating uncertaint
Datadog monitors configured for Multi Alerts evaluate thresholds against each unique combination of tags independently. While this allows for granular visibility, high-cardinality tags can result in a significant volume of individual notifications when multiple groups cross the threshold simultaneously. When utilizing complex boolean logic in monitor queries
kubernetes
java