Kubernetes and Azure Data Explorer Integration

Input and output integration overview

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

The Azure Data Explorer plugin allows integration of metrics collection with Azure Data Explorer, enabling users to analyze and query their telemetry data efficiently. With this plugin, users can configure ingestion settings to suit their needs and leverage Azure’s powerful analytical capabilities.

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.

Azure Data Explorer

The Azure Data Explorer plugin allows users to write metrics, logs, and time series data collected from various Telegraf input plugins into Azure Data Explorer, Azure Synapse, and Real-Time Analytics in Fabric. This integration serves as a bridge, allowing applications and services to monitor their performance metrics or logs efficiently. Azure Data Explorer is optimized for analytics over large volumes of diverse data types, making it an excellent choice for real-time analytics and monitoring solutions in cloud environments. The plugin empowers users to configure metrics ingestion based on their requirements, define table schemas dynamically, and set various ingestion methods while retaining flexibility regarding roles and permissions needed for database operations. This supports scalable and secure monitoring setups for modern applications that utilize cloud services.

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

Azure Data Explorer

[[outputs.azure_data_explorer]]
  ## The URI property of the Azure Data Explorer resource on Azure
  ## ex: endpoint_url = https://myadxresource.australiasoutheast.kusto.windows.net
  endpoint_url = ""

  ## The Azure Data Explorer database that the metrics will be ingested into.
  ## The plugin will NOT generate this database automatically, it's expected that this database already exists before ingestion.
  ## ex: "exampledatabase"
  database = ""

  ## Timeout for Azure Data Explorer operations
  # timeout = "20s"

  ## Type of metrics grouping used when pushing to Azure Data Explorer.
  ## Default is "TablePerMetric" for one table per different metric.
  ## For more information, please check the plugin README.
  # metrics_grouping_type = "TablePerMetric"

  ## Name of the single table to store all the metrics (Only needed if metrics_grouping_type is "SingleTable").
  # table_name = ""

  ## Creates tables and relevant mapping if set to true(default).
  ## Skips table and mapping creation if set to false, this is useful for running Telegraf with the lowest possible permissions i.e. table ingestor role.
  # create_tables = true

  ##  Ingestion method to use.
  ##  Available options are
  ##    - managed  --  streaming ingestion with fallback to batched ingestion or the "queued" method below
  ##    - queued   --  queue up metrics data and process sequentially
  # ingestion_type = "queued"

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.

Azure Data Explorer

1. Real-Time Monitoring Dashboard: By integrating metrics from various services into Azure Data Explorer using this plugin, organizations can build comprehensive dashboards that reflect real-time performance metrics. This allows teams to respond proactively to performance issues and optimize system health without delay.

2. Centralized Log Management: Utilize Azure Data Explorer to consolidate logs from multiple applications and services. By utilizing the plugin, organizations can streamline their log analysis processes, making it easier to search, filter, and derive insights from historical data accumulated over time.

3. Data-Driven Alerting Systems: Enhance monitoring capabilities by configuring alerts based on metrics sent via this plugin. Organizations can set thresholds and automate incident responses, significantly reducing downtime and improving the reliability of critical operations.

4. Machine Learning Model Training: By leveraging the data sent to Azure Data Explorer, organizations can perform large-scale analytics and prepare the data for feeding into machine learning models. This plugin enables the structuring of data that can subsequently be used for predictive analytics, leading to enhanced decision-making capabilities.

Feedback

Thank you for being part of our community! If you have any general feedback or found any bugs on these pages, we welcome and encourage your input. Please submit your feedback in the InfluxDB community Slack.