Kubernetes-Based Anomaly Detection with Prometheus

Question

Pulumi · Accepted Answer

To set up Kubernetes-based anomaly detection with Prometheus, you'll need to deploy Prometheus into your Kubernetes cluster and configure it to collect metrics from your workloads. These metrics can then be analyzed to identify anomalous behavior, which might indicate issues such as unexpected resource consumption or errors.

In this program, I'm demonstrating how to deploy Prometheus into a Kubernetes cluster using Pulumi's Python SDK. We will use the alicloud.arms.Prometheus resource from the Alibaba Cloud provider (pulumi_alicloud) to create a managed Prometheus instance. This managed service helps you monitor and alert on metrics without manually managing the Prometheus server.

Here's a step-by-step Python program that demonstrates how to use Pulumi to configure a Kubernetes-based anomaly detection system with Prometheus:

We start by importing the required packages for Pulumi, including Kubernetes and the Alibaba Cloud provider.
Then, we create a Prometheus resource, specifying the necessary configurations for the Prometheus instance, such as the Kubernetes cluster ID and the cluster type.
We'll also create a monitoring configuration and an alert rule to detect anomalies in our Kubernetes cluster.

This program assumes you already have a Kubernetes cluster running on Alibaba Cloud and that you have configured your Pulumi environment with the necessary access to interact with the Alibaba Cloud services.

import pulumi
import pulumi_alicloud as alicloud

# Create an Alibaba Cloud Resource Monitoring Service (ARMS) Prometheus instance
prometheus_instance = alicloud.arms.Prometheus("prometheusInstance",
    cluster_id="<your_cluster_id>",  # Replace <your_cluster_id> with the ID of your cluster
    cluster_name="<your_cluster_name>",  # Replace <your_cluster_name> with the name of your cluster
    cluster_type="Kubernetes",  # For a Kubernetes cluster type
    resource_group_id="<your_resource_group_id>",  # Replace <your_resource_group_id> with your resource group ID
    grafana_instance_id="<your_grafana_instance_id>",  # Replace <your_grafana_instance_id> with the ID of your Grafana instance for visualization if available
)

# Configure a Prometheus monitoring configuration for the Kubernetes cluster
monitoring_config = alicloud.arms.PrometheusMonitoring("monitoringConfig",
    type="prometheus",
    status="ENABLE",
    cluster_id=prometheus_instance.cluster_id,
    config_yaml="""global:
  scrape_interval: 15s
  evaluation_interval: 15s
scrape_configs:
  - job_name: 'kubernetes-pods'
    kubernetes_sd_configs:
      - role: pod
    relabel_configs:
      - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
        action: keep
        regex: true""",  # Provide your own scrape configuration YAML
)

# Define an alert rule to detect anomalies such as high CPU usage or memory leaks
alert_rule = alicloud.arms.PrometheusAlertRule("alertRule",
    type="prometheus",
    message="High CPU usage detected",
    duration="5m",  # How long the condition must be true before triggering the alert
    cluster_id=prometheus_instance.cluster_id,
    expression="sum(rate(container_cpu_usage_seconds_total{name!~\"^$\"}[3m])) by (pod) > 0.7",  # An example expression to monitor CPU usage
    prometheus_alert_rule_name="high-cpu-usage",
)

# Export the instance URL of Prometheus for easy access
pulumi.export('prometheus_instance_url', pulumi.Output.concat("http://", prometheus_instance.vpc_id, "/", prometheus_instance.id))

In this code:

cluster_id should be replaced with the actual ID of your Kubernetes cluster.
cluster_name is the name of your Kubernetes cluster.
resource_group_id is your resource group ID in Alibaba Cloud.
grafana_instance_id links with a Grafana instance if you have one, which is useful for visualizing the metrics.
In the monitoring_config, you provide a YAML configuration for Prometheus scraping rules.
The alert_rule specifies an alert condition using Prometheus query language (PromQL).
The expression in alert_rule is an example of monitoring high CPU usage. You would replace it with your own conditions that signify anomalies in your environment.

After deploying this configuration with Pulumi, Prometheus will start collecting metrics based on the specified scrape configuration, and the alert rule will notify when anomalies are detected based on the conditions you have set.