MavLink and New Relic 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.

This plugin allows the sending of metrics to New Relic Insights using the Metrics API, enabling effective monitoring and analysis of application performance.

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.

New Relic

This plugin writes metrics to New Relic Insights utilizing the Metrics API, which provides a robust mechanism for sending time series data to the New Relic platform. Users must first obtain an Insights API Key to authenticate and authorize their data submissions. The plugin is designed to facilitate easy integration with New Relic’s monitoring and analytics capabilities, supporting a variety of metric types and allowing for efficient data handling. Core features include the ability to add prefixes to metrics for better identification, customizable timeouts for API requests, and support for proxy settings to enhance connectivity. It is essential for users to configure these options according to their requirements, enabling seamless data flow into New Relic for comprehensive real-time analytics and insights.

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

New Relic

[[outputs.newrelic]]
  ## The 'insights_key' parameter requires a NR license key.
  ## New Relic recommends you create one
  ## with a convenient name such as TELEGRAF_INSERT_KEY.
  ## reference: https://docs.newrelic.com/docs/apis/intro-apis/new-relic-api-keys/#ingest-license-key
  # insights_key = "New Relic License Key Here"

  ## Prefix to add to add to metric name for easy identification.
  ## This is very useful if your metric names are ambiguous.
  # metric_prefix = ""

  ## Timeout for writes to the New Relic API.
  # timeout = "15s"

  ## HTTP Proxy override. If unset use values from the standard
  ## proxy environment variables to determine proxy, if any.
  # http_proxy = "http://corporate.proxy:3128"

  ## Metric URL override to enable geographic location endpoints.
  # If not set use values from the standard
  # metric_url = "https://metric-api.newrelic.com/metric/v1"

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.

New Relic

1. Application Performance Monitoring: Use the New Relic Telegraf plugin to send application performance metrics from a web service to New Relic Insights. By integrating this plugin, developers can collect data such as response times, error rates, and throughput, enabling teams to monitor application health in real-time and resolve issues quickly before they impact users. This setup promotes proactive management of application performance and user experience.

2. Infrastructure Metrics Aggregation: Leverage this plugin to aggregate and send system-level metrics (CPU usage, memory consumption, etc.) from various servers to New Relic. This helps system administrators maintain an comprehensive view of infrastructure performance, facilitating capacity planning and identifying potential bottlenecks. By centralizing metrics in New Relic, teams can visualize trends over time and make informed decisions regarding resource allocation.

3. Dynamic Metric Naming for Multi-tenant Applications: Implement dynamic prefixing with the metric_prefix option to differentiate between different tenants in a multi-tenant application. By configuring the plugin to include a unique identifier per tenant in the metric names, teams can analyze usage patterns and performance metrics per tenant. This provides valuable insights into tenant behavior, supporting tailored optimizations and enhancing service quality across different customer segments.

4. Real-time Anomaly Detection: Combine the New Relic plugin with alerting mechanisms to trigger notifications based on unusual metric patterns. By sending metrics such as request counts and response times, teams can set thresholds in New Relic that, when breached, will automatically alert responsible parties. This user-driven approach supports immediate responses to potential issues before they escalate into larger incidents.

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