Kubernetes and OpenTSDB Integration

Input and output integration overview

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

The OpenTSDB plugin facilitates the integration of Telegraf with OpenTSDB, allowing users to push time-series metrics to an OpenTSDB backend seamlessly.

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.

OpenTSDB

The OpenTSDB plugin is designed to send metrics to an OpenTSDB instance using either the telnet or HTTP mode. With the introduction of OpenTSDB 2.0, the recommended method for sending metrics is via the HTTP API, which allows for batch processing of metrics by configuring the ‘http_batch_size’. The plugin supports several configuration options including metrics prefixing, server host and port specification, URI path customization for reverse proxies, and debug options for diagnosing communication issues with OpenTSDB. This plugin is particularly useful in scenarios where time series data is generated and needs to be efficiently stored in a scalable time series database like OpenTSDB, making it suitable for a wide range of monitoring and analytics applications.

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

OpenTSDB

[[outputs.opentsdb]]
  ## prefix for metrics keys
  prefix = "my.specific.prefix."

  ## DNS name of the OpenTSDB server
  ## Using "opentsdb.example.com" or "tcp://opentsdb.example.com" will use the
  ## telnet API. "http://opentsdb.example.com" will use the Http API.
  host = "opentsdb.example.com"

  ## Port of the OpenTSDB server
  port = 4242

  ## Number of data points to send to OpenTSDB in Http requests.
  ## Not used with telnet API.
  http_batch_size = 50

  ## URI Path for Http requests to OpenTSDB.
  ## Used in cases where OpenTSDB is located behind a reverse proxy.
  http_path = "/api/put"

  ## Debug true - Prints OpenTSDB communication
  debug = false

  ## Separator separates measurement name from field
  separator = "_"

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.

OpenTSDB

1. Real-time Infrastructure Monitoring: Utilize the OpenTSDB plugin to collect and store metrics from various infrastructure components. By configuring the plugin to push metrics to OpenTSDB, organizations can have a centralized view of their infrastructure health and performance over time.

2. Custom Application Metrics Tracking: Integrate the OpenTSDB plugin into custom applications to track key performance indicators (KPIs) such as response times, error rates, and user interactions. This setup allows developers and product teams to visualize application performance trends and make data-driven decisions.

3. Automated Anomaly Detection: Leverage the plugin in conjunction with machine learning algorithms to automatically detect anomalies in time-series data sent to OpenTSDB. By continuously monitoring the incoming metrics, the system can train models that alert users to potential issues before they affect application performance.

4. Historical Data Analysis: Use the OpenTSDB plugin to store and analyze historical performance data for capacity planning and trend analysis. This provides valuable insights into system behavior over time, helping teams to understand usage patterns and prepare for future growth.

Feedback

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