MavLink and Prometheus Integration

Input and output integration overview

This plugin collects metrics from MavLink-compatible flight controllers like ArduPilot and PX4, enabling live data ingestion from unmanned systems such as drones and boats.

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

MavLink

The MavLink plugin is designed to gather metrics from MavLink-compatible flight controllers such as ArduPilot and PX4. It provides a mechanism to live ingest flight metrics from various unmanned systems, including drones, planes, and boats. By utilizing the ArduPilot-specific MavLink dialect, the plugin parses a wide range of messages as documented in the MavLink documentation. It enables seamless integration of telemetry data, allowing for detailed monitoring and analysis of flight operations. Users must be cautious, as this plugin may generate a substantial volume of data; thus, filters are available to limit the metrics collected and transmitted to output plugins. Additionally, configuration options allow customization of which messages to receive and how to connect to the flight controller.

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

MavLink

[[inputs.mavlink]]
  ## Flight controller URL supporting serial port, UDP and TCP connections.
  ## Options are documented at
  ##   https://mavsdk.mavlink.io/v1.4/en/cpp/guide/connections.html.
  ##
  ## Examples:
  ## - Serial port: serial:///dev/ttyACM0:57600
  ## - TCP client:  tcp://192.168.1.12:5760
  ## - UDP client:  udp://192.168.1.12:14550
  ## - TCP server:  tcpserver://:5760
  ## - UDP server:  udpserver://:14550
  # url = "tcp://127.0.0.1:5760"

  ## Filter to specific messages. Only the messages in this list will be parsed.
  ## If blank or unset, all messages will be accepted. Glob syntax is accepted.
  ## Each message in this list should be lowercase camel_case, with "message_"
  ## prefix removed, eg: "global_position_int", "attitude"
  # filter = []

  ## Mavlink system ID for Telegraf. Only used if the mavlink plugin is sending 
  ## messages, eg. when `stream_request_frequency` is 0 (see below.)
  # system_id = 254

  ## Determines whether the plugin sends requests to subscribe to data.
  ## In mavlink, stream rates must be configured before data is received.
  ## This config item sets the rate in Hz, with 0 disabling the request.
  ## 
  ## This frequency should be set to 0 if your software already controls the 
  ## rates using REQUEST_DATA_STREAM or MAV_CMD_SET_MESSAGE_INTERVAL
  ## (See https://mavlink.io/en/mavgen_python/howto_requestmessages.html)
  # stream_request_frequency = 4

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

MavLink

1. Real-Time Fleet Monitoring: Utilize the MavLink plugin to create a centralized dashboard for monitoring multiple drones in real-time. By ingesting metrics from various flight controllers, operators can oversee the status, health, and location of all drones, allowing for quick decision-making and enhanced situational awareness. This integration could significantly improve coordination during large-scale operations, like aerial surveys or search and rescue missions.

2. Automated Anomaly Detection: Leverage MavLink in conjunction with machine learning algorithms to detect anomalies in flight data. By continuously monitoring metrics such as altitude, speed, and battery status, the system can alert operators to deviations from normal behavior, potentially indicating technical malfunctions or safety issues. This proactive approach can enhance safety and reduce the risk of in-flight failures.

3. Data-Driven Maintenance Scheduling: Integrate the data collected through the MavLink plugin with maintenance management systems to optimize maintenance schedules based on actual flight metrics. Analyzing the collected data can highlight patterns indicating when specific components are likely to fail, thereby enabling predictive maintenance strategies that minimize downtime and repair costs.

4. Enhanced Research Analytics: For academic and commercial UAV research, MavLink can be used to gather extensive flight data for analysis. By compiling metrics over numerous flights, researchers can uncover insights related to flight patterns, environmental interactions, and the efficiency of different drone models. This can foster advancements in UAV technology and broader applications in autonomous systems.

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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