InsightFinder offers comprehensive integration capabilities, enabling the collection of metrics, logs, and event data directly from pods within a Kubernetes cluster.
Leveraging the advanced capabilities of the InsightFinder Intelligence Engine, it provides in-depth root cause analysis for pod crash events. Additionally, integration with our Autoscaler feature allows for cost-efficient resource management and proactive scaling in response to workload surges.
InsightFinder’s platform support extends to a wide range of providers, including AWS EKS, Google Cloud GKE, and on-premise solutions.
We provided a Kubernetes collector to collect the following data from the cluster:
InsightFinder also supports the autoscaling of memory and CPU resources based on our predictions.
InsightFinder predicts future memory requirements, enabling proactive reduction of memory allocations. When lower usage is forecasted, the autoscaler decreases memory requests, aligning resource allocation with predicted needs.
Conversely, InsightFinder can preemptively increase resource limits and requests. By forecasting higher resource demands, it automatically scales up memory allocations, preventing issues related to resource scarcity.
InsightFinder actively predicts and detects incidents in realtime. This functionality permits tailored scaling actions in response to specific incidents, such as automatically increasing resources during an OutOfMemory event. This proactive approach ensures system stability and reliability.
helm repo add insightfinder https://insightfinder.github.io/charts
helm install -f ./values.yaml if-kubernetes-agent insightfinder/if-kubernetes-agent --version 0.0.6