Supervisor and Prometheus Integration

Input and output integration overview

This plugin gathers information about processes running under Supervisor using the XML-RPC API.

The Prometheus Output Plugin enables Telegraf to expose metrics at an HTTP endpoint for scraping by a Prometheus server. This integration allows users to collect and aggregate metrics from various sources in a format that Prometheus can process efficiently.

Integration details

Supervisor

The Supervisor plugin for Telegraf is designed to collect metrics about processes managed by the Supervisor process control system using its XML-RPC API. The plugin is able to track various metrics, including process states and uptime, and provides options for configuring which metrics to collect through include or exclude lists. This integration is particularly useful for monitoring applications running under Supervisor, providing insights into their operational status and performance metrics. A minimum tested Supervisor version is 3.3.2, and it is recommended to secure the HTTP server with basic authentication for better security.

Prometheus

This plugin for facilitates the integration with Prometheus, a well-known open-source monitoring and alerting toolkit designed for reliability and efficiency in large-scale environments. By working as a Prometheus client, it allows users to expose a defined set of metrics via an HTTP server that Prometheus can scrape at specified intervals. This plugin plays a crucial role in monitoring diverse systems by allowing them to publish performance metrics in a standardized format, enabling extensive visibility into system health and behavior. Key features include support for configuring various endpoints, enabling TLS for secure communication, and options for HTTP basic authentication. The plugin also integrates seamlessly with global Telegraf configuration settings, supporting extensive customization to fit specific monitoring needs. This promotes interoperability in environments where different systems must communicate performance data effectively. Leveraging Prometheus’s metric format, it allows for flexible metric management through advanced configurations such as metric expiration and collectors control, offering a sophisticated solution for monitoring and alerting workflows.

Configuration

Supervisor

[[inputs.supervisor]]
  ## Url of supervisor's XML-RPC endpoint if basic auth enabled in supervisor http server,
  ## than you have to add credentials to url (ex. http://login:pass@localhost:9001/RPC2)
  # url="http://localhost:9001/RPC2"
  ## With settings below you can manage gathering additional information about processes
  ## If both of them empty, then all additional information will be collected.
  ## Currently supported supported additional metrics are: pid, rc
  # metrics_include = []
  # metrics_exclude = ["pid", "rc"]

Prometheus

[[outputs.prometheus_client]]
  ## Address to listen on.
  ##   ex:
  ##     listen = ":9273"
  ##     listen = "vsock://:9273"
  listen = ":9273"

  ## Maximum duration before timing out read of the request
  # read_timeout = "10s"
  ## Maximum duration before timing out write of the response
  # write_timeout = "10s"

  ## Metric version controls the mapping from Prometheus metrics into Telegraf metrics.
  ## See "Metric Format Configuration" in plugins/inputs/prometheus/README.md for details.
  ## Valid options: 1, 2
  # metric_version = 1

  ## Use HTTP Basic Authentication.
  # basic_username = "Foo"
  # basic_password = "Bar"

  ## If set, the IP Ranges which are allowed to access metrics.
  ##   ex: ip_range = ["192.168.0.0/24", "192.168.1.0/30"]
  # ip_range = []

  ## Path to publish the metrics on.
  # path = "/metrics"

  ## Expiration interval for each metric. 0 == no expiration
  # expiration_interval = "60s"

  ## Collectors to enable, valid entries are "gocollector" and "process".
  ## If unset, both are enabled.
  # collectors_exclude = ["gocollector", "process"]

  ## Send string metrics as Prometheus labels.
  ## Unless set to false all string metrics will be sent as labels.
  # string_as_label = true

  ## If set, enable TLS with the given certificate.
  # tls_cert = "/etc/ssl/telegraf.crt"
  # tls_key = "/etc/ssl/telegraf.key"

  ## Set one or more allowed client CA certificate file names to
  ## enable mutually authenticated TLS connections
  # tls_allowed_cacerts = ["/etc/telegraf/clientca.pem"]

  ## Export metric collection time.
  # export_timestamp = false

  ## Specify the metric type explicitly.
  ## This overrides the metric-type of the Telegraf metric. Globbing is allowed.
  # [outputs.prometheus_client.metric_types]
  #   counter = []
  #   gauge = []

Input and output integration examples

Supervisor

1. Centralized Monitoring Dashboard: Implement this plugin to feed Supervisor metrics directly into a centralized monitoring dashboard, allowing teams to visualize the health and performance of their applications in real-time. This integration enables quick identification of issues, helps track service performance over time, and aids in capacity planning based on observed trends.

2. Alerting for Process Failures: Utilize the metrics gathered by the Supervisor plugin to create an alerting mechanism that notifies engineers when critical processes go down or enter a fatal state. By setting thresholds in your monitoring system, teams can respond proactively to potential problems, minimizing downtime and ensuring system reliability.

3. Historical Analysis of Process States: Store the metrics collected over time to analyze process state changes and patterns. By examining historical data, teams can identify recurring issues, track the impact of deployment changes, and optimize resource allocation based on process trends, leading to improved overall system performance.

4. Integration with Incident Management Systems: Configure the Supervisor plugin to automatically send alerts to incident management systems like PagerDuty or OpsGenie when a process reaches a critical state. This integration streamlines the incident response process, ensuring that the right team members are notified promptly and can take action without delay.

Prometheus

1. Monitoring Multi-cloud Deployments: Utilize the Prometheus plugin to collect metrics from applications running across multiple cloud providers. This scenario allows teams to centralize monitoring through a single Prometheus instance that scrapes metrics from different environments, providing a unified view of performance metrics across hybrid infrastructures. It streamlines reporting and alerting, enhancing operational efficiency without needing complex integrations.

2. Enhancing Microservices Visibility: Implement the plugin to expose metrics from various microservices within a Kubernetes cluster. Using Prometheus, teams can visualize service metrics in real time, identify bottlenecks, and maintain system health checks. This setup supports adaptive scaling and resource utilization optimization based on insights generated from the collected metrics. It enhances the ability to troubleshoot service interactions, significantly improving the resilience of the microservice architecture.

3. Real-time Anomaly Detection in E-commerce: By leveraging this plugin alongside Prometheus, an e-commerce platform can monitor key performance indicators such as response times and error rates. Integrating anomaly detection algorithms with scraped metrics allows the identification of unexpected patterns indicating potential issues, such as sudden traffic spikes or backend service failure. This proactive monitoring empowers business continuity and operational efficiency, minimizing potential downtimes while ensuring service reliability.

4. Performance Metrics Reporting for APIs: Utilize the Prometheus Output Plugin to gather and report API performance metrics, which can then be visualized in Grafana dashboards. This use case enables detailed analysis of API response times, throughput, and error rates, promoting continuous improvement of API services. By closely monitoring these metrics, teams can quickly react to degradation, ensuring optimal API performance and maintaining a high level of service availability.

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