Jenkins and Azure Application Insights Integration

Input and output integration overview

The Jenkins plugin collects vital information regarding jobs and nodes from a Jenkins instance through its API, facilitating comprehensive monitoring and analysis.

This plugin writes Telegraf metrics to Azure Application Insights, enabling powerful monitoring and diagnostics.

Integration details

Jenkins

The Jenkins Telegraf plugin allows users to gather metrics from a Jenkins instance without needing to install any additional plugins on Jenkins itself. By utilizing the Jenkins API, the plugin retrieves information about nodes and jobs running in the Jenkins environment. This integration provides a comprehensive overview of the Jenkins infrastructure, including real-time metrics that can be used for monitoring and analysis. Key features include configurable filters for job and node selection, optional TLS security settings, and the ability to manage request timeouts and connection limits effectively. This makes it an essential tool for teams that rely on Jenkins for continuous integration and delivery, ensuring they have the insights they need to maintain optimal performance and reliability.

Azure Application Insights

The Azure Application Insights plugin integrates Telegraf with Azure’s Application Insights service, facilitating the seamless transmission of metrics from various sources to a centralized monitoring platform. This plugin empowers users to harness the capabilities of Azure Application Insights, a powerful application performance management tool, allowing developers and IT operations teams to gain valuable insights into the performance, availability, and usage of their applications. By employing this plugin, users can monitor application telemetry and operational data efficiently, contributing to better diagnostics and improved application performance.

Key features of this plugin include the ability to specify an instrumentation key for the Application Insights resource, configure the endpoint URL for tracking, and enable additional diagnostic logging for a more comprehensive analysis. Furthermore, the plugin provides context tagging capabilities, allowing the addition of specific Application Insights context tags to enhance the contextual information associated with metrics being sent. These features collectively make the Azure Application Insights Output Plugin a vital tool for organizations looking to optimize their monitoring capabilities within Azure.

Configuration

Jenkins

[[inputs.jenkins]]
  ## The Jenkins URL in the format "schema://host:port"
  url = "http://my-jenkins-instance:8080"
  # username = "admin"
  # password = "admin"

  ## Set response_timeout
  response_timeout = "5s"

  ## Optional TLS Config
  # tls_ca = "/etc/telegraf/ca.pem"
  # tls_cert = "/etc/telegraf/cert.pem"
  # tls_key = "/etc/telegraf/key.pem"
  ## Use SSL but skip chain & host verification
  # insecure_skip_verify = false

  ## Optional Max Job Build Age filter
  ## Default 1 hour, ignore builds older than max_build_age
  # max_build_age = "1h"

  ## Optional Sub Job Depth filter
  ## Jenkins can have unlimited layer of sub jobs
  ## This config will limit the layers of pulling, default value 0 means
  ## unlimited pulling until no more sub jobs
  # max_subjob_depth = 0

  ## Optional Sub Job Per Layer
  ## In workflow-multibranch-plugin, each branch will be created as a sub job.
  ## This config will limit to call only the lasted branches in each layer,
  ## empty will use default value 10
  # max_subjob_per_layer = 10

  ## Jobs to include or exclude from gathering
  ## When using both lists, job_exclude has priority.
  ## Wildcards are supported: [ "jobA/*", "jobB/subjob1/*"]
  # job_include = [ "*" ]
  # job_exclude = [ ]

  ## Nodes to include or exclude from gathering
  ## When using both lists, node_exclude has priority.
  # node_include = [ "*" ]
  # node_exclude = [ ]

  ## Worker pool for jenkins plugin only
  ## Empty this field will use default value 5
  # max_connections = 5

  ## When set to true will add node labels as a comma-separated tag. If none,
  ## are found, then a tag with the value of 'none' is used. Finally, if a
  ## label contains a comma it is replaced with an underscore.
  # node_labels_as_tag = false

Azure Application Insights

[[outputs.application_insights]]
  ## Instrumentation key of the Application Insights resource.
  instrumentation_key = "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxx"

  ## Regions that require endpoint modification https://docs.microsoft.com/en-us/azure/azure-monitor/app/custom-endpoints
  # endpoint_url = "https://dc.services.visualstudio.com/v2/track"

  ## Timeout for closing (default: 5s).
  # timeout = "5s"

  ## Enable additional diagnostic logging.
  # enable_diagnostic_logging = false

  ## NOTE: Due to the way TOML is parsed, tables must be at the END of the
  ## plugin definition, otherwise additional config options are read as part of
  ## the table

  ## Context Tag Sources add Application Insights context tags to a tag value.
  ##
  ## For list of allowed context tag keys see:
  ## https://github.com/microsoft/ApplicationInsights-Go/blob/master/appinsights/contracts/contexttagkeys.go
  # [outputs.application_insights.context_tag_sources]
  #   "ai.cloud.role" = "kubernetes_container_name"
  #   "ai.cloud.roleInstance" = "kubernetes_pod_name"

Input and output integration examples

Jenkins

1. Continuous Integration Monitoring: Use the Jenkins plugin to monitor the performance of continuous integration pipelines by collecting metrics on job durations and failure rates. This can help teams identify bottlenecks in the pipeline and improve overall build efficiency.

2. Resource Allocation Analysis: Leverage Jenkins node metrics to assess resource usage across different agents. By understanding how resources are allocated, teams can optimize their Jenkins architecture, potentially reallocating agents or adjusting job configurations for better performance.

3. Job Execution Trends: Analyze historical job performance metrics to identify trends in job execution over time. With this data, teams can proactively address potential issues before they grow, making adjustments to the jobs or their configurations as needed.

4. Alerting for Job Failures: Implement alerts that leverage the Jenkins job metrics to notify team members in case of job failures. This proactive approach can enhance operational awareness and speed up response times to failures, ensuring that critical jobs are monitored effectively.

Azure Application Insights

1. Application Performance Monitoring: Utilize the Azure Application Insights plugin to continuously monitor the performance of your web applications or microservices. By sending Telegraf metrics directly to Application Insights, teams can visualize real-time application performance data, enabling proactive tuning and optimization of application resources. This setup not only enhances the reliability of applications but also ensures user satisfaction through consistent performance monitoring.

2. Integrated Logging and Telemetry: Combine this plugin with centralized logging solutions to provide a comprehensive observability stack. By sending telecom data to Azure Application Insights, teams can correlate performance metrics with log data and gain deeper insights into application behavior, allowing for more efficient troubleshooting and root cause analysis.

3. Contextual Monitoring of Cloud Resources: Use the context tagging feature to enrich your application metrics with specific contextual information related to your cloud environment. This enhanced context can be invaluable for understanding the performance of cloud-native applications, enabling better scaling decisions and resource management based on real usage patterns.

4. Real-time Alerts Setup: Configure Application Insights to trigger alerts based on specific metrics received via this plugin. This allows teams to be notified of performance degradation or anomalies in real-time, enabling immediate action to mitigate issues and maintain high availability of applications.

Feedback

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