1. Scalable Prometheus Monitoring with VictoriaMetrics on Kubernetes

    Python

    Prometheus is a powerful monitoring tool commonly used to collect and process telemetry data (metrics) from various sources, such as Kubernetes clusters. VictoriaMetrics is a fast, cost-effective and scalable monitoring solution and time series database that is fully compatible with Prometheus and Grafana. When deployed in a Kubernetes cluster, VictoriaMetrics can be used as a drop-in replacement for Prometheus to handle high loads of monitoring data more efficiently.

    To create a scalable Prometheus monitoring system using VictoriaMetrics on Kubernetes, we need to deploy several components:

    1. VictoriaMetrics Operator: This Kubernetes operator manages VictoriaMetrics clusters and components such as VMInsert, VMStorage, VMSelect, andVMAlert.

    2. VMStorage: This is the storage backend for VictoriaMetrics which is responsible for storing time series data.

    3. VMSelect: This component performs queries on the data that resides in VMStorage.

    4. VMInsert: The VMInsert component accepts incoming data on the Prometheus remote_write interface and stores it in VMStorage.

    5. VMAlert: It evaluates alerting rules and records new time series.

    6. HorizontalPodAutoscaler (HPA): Kubernetes Horizontal Pod Autoscaler can automatically scale the number of pods in a deployment based on observed CPU utilization or custom metrics such as those provided by Prometheus.

    With Pulumi, you can use the pulumi_kubernetes package to provision these resources onto your Kubernetes cluster.

    Below is a Pulumi program in Python that sets up VictoriaMetrics cluster components using the Kubernetes operator. This program assumes that you have Pulumi installed, a Kubernetes cluster configured, and the necessary permissions to deploy resources to it.

    import pulumi import pulumi_kubernetes as kubernetes # Provision the VictoriaMetrics Operator. # The Operator is responsible for deploying and managing the VictoriaMetrics cluster components. # It can be installed via a Helm chart or YAML manifests. vm_operator = kubernetes.yaml.ConfigFile('vm-operator', config='https://...') # Replace with the actual URL to VictoriaMetrics Operator YAML or Helm chart # Create a VMStorage resource for VictoriaMetrics, managed by the Operator. # This resource will take care of persisting the monitoring data. vm_storage = kubernetes.apiextensions.CustomResource( "vmstorage", api_version="operator.victoriametrics.com/v1beta1", kind="VMStorage", metadata={"name": "prometheus-vmstorage"} # Additional options can be configured as per the needs such as storage class, volume size, etc. ) # Create a VMSelect resource for querying the stored metrics. vm_select = kubernetes.apiextensions.CustomResource( "vmselect", api_version="operator.victoriametrics.com/v1beta1", kind="VMSelect", metadata={"name": "prometheus-vmselect"}, spec={ "replicaCount": 2, # Adjust the number of replicas based on the expected query load } ) # Create a VMInsert resource for accepting incoming metric data. vm_insert = kubernetes.apiextensions.CustomResource( "vminsert", api_version="operator.victoriametrics.com/v1beta1", kind="VMInsert", metadata={"name": "prometheus-vminsert"}, spec={ "replicaCount": 2, # Can be adjusted according to the expected write load } ) # Create a VMAlert resource for evaluating alerting rules. vm_alert = kubernetes.apiextensions.CustomResource( "vmalert", api_version="operator.victoriametrics.com/v1beta1", kind="VMAlert", metadata={"name": "prometheus-vmalert"}, spec={ # Configure your alerting rules here "ruleSelector": { "matchLabels": { "app": "prometheus", "role": "alert-rules", }, }, # Define other settings like the alertmanager URL, evaluation interval, etc. } ) # Export the URLs for accessing the VictoriaMetrics components pulumi.export('VMSelect URL', vm_select.metadata.apply(lambda metadata: f"http://{metadata.name}.svc:8481/select/")) pulumi.export('VMInsert URL', vm_insert.metadata.apply(lambda metadata: f"http://{metadata.name}.svc:8480/insert/")) pulumi.export('VMAlert URL', vm_alert.metadata.apply(lambda metadata: f"http://{metadata.name}.svc:8880/")) # Optionally, create a Horizontal Pod Autoscaler (HPA) to scale the VMSelect based on CPU utilization hpa_vmselect = kubernetes.autoscaling.v2beta1.HorizontalPodAutoscaler( "hpa-vmselect", metadata={"name": "hpa-vmselect"}, spec={ "scaleTargetRef": { "apiVersion": "apps/v1", "kind": "Deployment", "name": "vmselect-deployment" # Replace with the correct deployment name of your VMSelect }, "minReplicas": 2, # Minimum number of replicas "maxReplicas": 5, # Maximum number of replicas "metrics": [{ "type": "Resource", "resource": { "name": "cpu", "target": { "type": "Utilization", "averageUtilization": 80, # Targeted CPU utilization percentage for scaling }, }, }], } )

    Please update URLs and names appropriately based on your own deployments and configurations.

    Notes:

    • The VMStorage, VMSelect, VMInsert, and VMAlert resources are created using pulumi_kubernetes.apiextensions.CustomResource. This enables custom resources provided by VictoriaMetrics Operator to be managed by Pulumi as first-class citizens, in a similar manner to built-in Kubernetes resources.

    • When scaling with the HorizontalPodAutoscaler, the exact deployment names used in your setup need to be specified in the scaleTargetRef configuration.

    • The pulumi.export statements at the end of the program make the URLs used to access the VictoriaMetrics components available as Pulumi stack outputs for easy access.

    This setup can be considered a starting point. Depending on the volume of metrics, retention policies, computation resources, and other factors, you might need to tweak resource specifications to ensure optimal performance and cost-efficiency.

    Make sure to apply all necessary configurations that suit the requirements of your Prometheus monitoring setup, including setting up authentication, storage classes, and alerting rules.


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