Kubernetes and Splunk Integration

Input and output integration overview

This plugin captures metrics for Kubernetes pods and containers by communicating with the Kubelet API.

This output plugin facilitates direct streaming of Telegraf collected metrics into Splunk via the HTTP Event Collector, enabling easy integration with Splunk’s powerful analytics platform.

Integration details

Kubernetes

The Kubernetes input plugin interfaces with the Kubelet API to gather metrics for running pods and containers on a single host, ideally as part of a daemonset in a Kubernetes installation. By operating on each node within the cluster, it collects metrics from the locally running kubelet, ensuring that the data reflects the real-time state of the environment. Being a rapidly evolving project, Kubernetes sees frequent updates, and this plugin adheres to the major cloud providers’ supported versions, maintaining compatibility across multiple releases within a limited time span. Significant consideration is given to the potential high series cardinality, which can burden the database; thus, users are advised to implement filtering techniques and retention policies to manage this load effectively. Configuration options provide flexible customization of the plugin’s behavior to integrate seamlessly into different setups, enhancing its utility in monitoring Kubernetes environments.

Splunk

Use Telegraf to easily collect and aggregate metrics from many different sources and send them to Splunk. Utilizing the HTTP output plugin combined with the specialized Splunk metrics serializer, this configuration ensures efficient data ingestion into Splunk’s metrics indexes. The HEC is an advanced mechanism provided by Splunk designed to reliably collect data at scale via HTTP or HTTPS, providing critical capabilities for security, monitoring, and analytics workloads. Telegraf’s integration with Splunk HEC streamlines operations by leveraging standard HTTP protocols, built-in authentication, and structured data serialization, optimizing metrics ingestion and enabling immediate actionable insights.

Configuration

Kubernetes

[[inputs.kubernetes]]
  ## URL for the kubelet, if empty read metrics from all nodes in the cluster
  url = "http://127.0.0.1:10255"

  ## Use bearer token for authorization. ('bearer_token' takes priority)
  ## If both of these are empty, we'll use the default serviceaccount:
  ## at: /var/run/secrets/kubernetes.io/serviceaccount/token
  ##
  ## To re-read the token at each interval, please use a file with the
  ## bearer_token option. If given a string, Telegraf will always use that
  ## token.
  # bearer_token = "/var/run/secrets/kubernetes.io/serviceaccount/token"
  ## OR
  # bearer_token_string = "abc_123"

  ## Kubernetes Node Metric Name
  ## The default Kubernetes node metric name (i.e. kubernetes_node) is the same
  ## for the kubernetes and kube_inventory plugins. To avoid conflicts, set this
  ## option to a different value.
  # node_metric_name = "kubernetes_node"

  ## Pod labels to be added as tags.  An empty array for both include and
  ## exclude will include all labels.
  # label_include = []
  # label_exclude = ["*"]

  ## Set response_timeout (default 5 seconds)
  # response_timeout = "5s"

  ## Optional TLS Config
  # tls_ca = /path/to/cafile
  # tls_cert = /path/to/certfile
  # tls_key = /path/to/keyfile
  ## Use TLS but skip chain & host verification
  # insecure_skip_verify = false

Splunk

[[outputs.http]]
  ## Splunk HTTP Event Collector endpoint
  url = "https://splunk.example.com:8088/services/collector"

  ## HTTP method to use
  method = "POST"

  ## Splunk authentication token
  headers = {"Authorization" = "Splunk YOUR_SPLUNK_HEC_TOKEN"}

  ## Serializer for formatting metrics specifically for Splunk
  data_format = "splunkmetric"

  ## Optional parameters
  # timeout = "5s"
  # insecure_skip_verify = false
  # tls_ca = "/path/to/ca.pem"
  # tls_cert = "/path/to/cert.pem"
  # tls_key = "/path/to/key.pem"

Input and output integration examples

Kubernetes

1. Dynamic Resource Allocation Monitoring: By utilizing the Kubernetes plugin, teams can set up alerts for resource usage patterns across various pods and containers. This proactive monitoring approach enables automatic scaling of resources in response to specific thresholds—helping to optimize performance while minimizing costs during peak usage.

2. Multi-tenancy Resource Isolation Analysis: Organizations using Kubernetes can leverage this plugin to track resource consumption per namespace. In a multi-tenant scenario, understanding the resource allocations and usages across different teams becomes critical for ensuring fair access and performance guarantees, leading to better resource management strategies.

3. Real-time Health Dashboards: Integrate the data captured by the Kubernetes plugin into visualization tools like Grafana to create real-time dashboards. These dashboards provide insights into the overall health and performance of the Kubernetes environment, allowing teams to quickly identify and rectify issues across clusters, pods, and containers.

4. Automated Incident Response Workflows: By combining the Kubernetes plugin with alert management systems, teams can automate incident response procedures based on real-time metrics. If a pod’s resource usage exceeds predefined limits, an automated workflow can trigger remediation actions, such as restarting the pod or reallocating resources—all of which can help improve system resilience.

Splunk

1. Real-Time Security Analytics: Utilize this plugin to stream security-related metrics from various applications into Splunk in real-time. Organizations can detect threats instantly by correlating data streams across systems, significantly reducing detection and response times.

2. Multi-Cloud Infrastructure Monitoring: Integrate Telegraf to consolidate metrics from multi-cloud environments directly into Splunk, enabling comprehensive visibility and operational intelligence. This unified monitoring allows teams to detect performance issues quickly and streamline cloud resource management.

3. Dynamic Capacity Planning: Deploy the plugin to continuously push resource metrics from container orchestration platforms (like Kubernetes) into Splunk. Leveraging Splunk’s analytics capabilities, teams can automate predictive scaling and resource allocation, avoiding resource bottlenecks and minimizing costs.

4. Automated Incident Response Workflows: Combine this plugin with Splunk’s alerting system to create automated incident response workflows. Metrics collected by Telegraf trigger real-time alerts and automated remediation scripts, ensuring rapid resolution and maintaining high system availability.

Feedback

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