IPMI Sensor and Prometheus Integration

Input and output integration overview

The IPMI Sensor Plugin facilitates the collection of server health metrics directly from hardware via the IPMI protocol, querying sensor data from either local or remote systems.

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

IPMI Sensor

The IPMI Sensor plugin is designed to gather bare metal metrics via the command line utility ipmitool, which interfaces with the Intelligent Platform Management Interface (IPMI). This protocol provides management and monitoring capabilities for hardware components in server systems, allowing for the retrieval of critical system health metrics such as temperature, fan speeds, and power supply status from both local and remote servers. When configured without specified servers, the plugin defaults to querying the local machine’s sensor statistics using the ipmitool sdr command. In scenarios covering remote hosts, authentication is supported through username and password using the command format ipmitool -I lan -H SERVER -U USERID -P PASSW0RD sdr. This flexibility allows users to monitor systems effectively across various environments. The plugin also supports multiple sensor types, including chassis power status and DCMI power readings, catering to administrators needing real-time insight into server operations.

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

IPMI Sensor

[[inputs.ipmi_sensor]]
  ## Specify the path to the ipmitool executable
  # path = "/usr/bin/ipmitool"

  ## Use sudo
  ## Setting 'use_sudo' to true will make use of sudo to run ipmitool.
  ## Sudo must be configured to allow the telegraf user to run ipmitool
  ## without a password.
  # use_sudo = false

  ## Servers
  ## Specify one or more servers via a url. If no servers are specified, local
  ## machine sensor stats will be queried. Uses the format:
  ##  [username[:password]@][protocol[(address)]]
  ##  e.g. root:passwd@lan(127.0.0.1)
  # servers = ["USERID:PASSW0RD@lan(192.168.1.1)"]

  ## Session privilege level
  ## Choose from: CALLBACK, USER, OPERATOR, ADMINISTRATOR
  # privilege = "ADMINISTRATOR"

  ## Timeout
  ## Timeout for the ipmitool command to complete.
  # timeout = "20s"

  ## Metric schema version
  ## See the plugin readme for more information on schema versioning.
  # metric_version = 1

  ## Sensors to collect
  ## Choose from:
  ##   * sdr: default, collects sensor data records
  ##   * chassis_power_status: collects the power status of the chassis
  ##   * dcmi_power_reading: collects the power readings from the Data Center Management Interface
  # sensors = ["sdr"]

  ## Hex key
  ## Optionally provide the hex key for the IMPI connection.
  # hex_key = ""

  ## Cache
  ## If ipmitool should use a cache
  ## Using a cache can speed up collection times depending on your device.
  # use_cache = false

  ## Path to the ipmitools cache file (defaults to OS temp dir)
  ## The provided path must exist and must be writable
  # cache_path = ""

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

IPMI Sensor

1. Centralized Monitoring Dashboard: Utilize the IPMI Sensor plugin to gather metrics from multiple servers and compile them into a centralized monitoring dashboard. This enables real-time visibility into server health across data centers. Administrators can track metrics like temperature and power usage, helping them make data-driven decisions about resource allocation, potential failures, and maintenance schedules.

2. Automated Power Alerts: Incorporate the plugin into an alerting system that monitors chassis power status and triggers alerts when anomalies are detected. For instance, if the power status indicates a failure or if watt values exceed expected thresholds, automated notifications can be sent to operations teams, ensuring prompt attention to hardware issues.

3. Energy Consumption Analysis: Leverage the DCMI power readings collected via the plugin to analyze energy consumption patterns of hardware over time. By integrating these readings with analytics platforms, organizations can identify opportunities to reduce power usage, optimize efficiency, and potentially decrease operational costs in large server farms or cloud infrastructures.

4. Health Check Automation: Schedule regular health checks by using the IPMI Sensor Plugin to collect data from a fleet of servers. This data can be logged and compared against historical performance metrics to identify trends, outliers, or signs of impending hardware failure, allowing IT teams to take proactive measures and reduce downtime.

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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