IPMI Sensor and VictoriaMetrics 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.

This plugin enables Telegraf to efficiently write metrics directly into VictoriaMetrics using the InfluxDB line protocol, leveraging the performance and scalability features of VictoriaMetrics for large-scale time-series data.

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.

VictoriaMetrics

VictoriaMetrics supports direct ingestion of metrics in the InfluxDB line protocol, making this plugin ideal for efficient real-time metric storage and retrieval. The integration combines Telegraf’s extensive metric collection capabilities with VictoriaMetrics’ optimized storage and querying features, including compression, fast ingestion rates, and efficient disk utilization. Ideal for cloud-native and large-scale monitoring scenarios, this plugin offers simplicity, robust performance, and high reliability, enabling advanced operational insights and long-term storage solutions for large volumes of metrics.

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 = ""

VictoriaMetrics

[[outputs.influxdb]]
  ## URL of the VictoriaMetrics write endpoint
  urls = ["http://localhost:8428"]

  ## VictoriaMetrics accepts InfluxDB line protocol directly
  database = "db_name"

  ## Optional authentication
  # username = "username"
  # password = "password"
  # skip_database_creation = true
  # exclude_retention_policy_tag = true
  # content_encoding = "gzip"

  ## Timeout for HTTP requests
  timeout = "5s"

  ## Optional TLS configuration
  # tls_ca = "/path/to/ca.pem"
  # tls_cert = "/path/to/cert.pem"
  # tls_key = "/path/to/key.pem"
  # insecure_skip_verify = false

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.

VictoriaMetrics

1. Cloud-Native Application Monitoring: Stream metrics from microservices deployed on Kubernetes directly into VictoriaMetrics. By centralizing metrics, organizations can perform real-time monitoring, rapid anomaly detection, and seamless scalability across dynamically evolving cloud environments.

2. Scalable IoT Data Management: Use the plugin to ingest sensor data from IoT deployments into VictoriaMetrics. This approach facilitates real-time analytics, predictive maintenance, and efficient management of massive volumes of sensor data with minimal storage overhead.

3. Financial Systems Performance Tracking: Leverage VictoriaMetrics via this plugin to store and analyze metrics from financial systems, capturing latency, transaction volume, and error rates. Organizations can rapidly identify and resolve performance bottlenecks, ensuring high availability and regulatory compliance.

4. Cross-Environment Performance Dashboards: Integrate metrics from diverse infrastructure components—such as cloud instances, containers, and physical servers into VictoriaMetrics. Using visualization tools, teams can build comprehensive dashboards for end-to-end performance visibility, proactive troubleshooting, and infrastructure optimization.

Feedback

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