MavLink and Clickhouse Integration

Input and output integration overview

<p>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.</p>

<p>Telegraf’s SQL plugin sends collected metrics to an SQL database using a straightforward table schema and dynamic column generation. When configured for ClickHouse, it adjusts DSN formatting and type conversion settings to ensure seamless data integration.</p>

Integration details

MavLink

<p>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.</p>

Clickhouse

<p>Telegraf’s SQL plugin is engineered to write metric data into an SQL database by dynamically creating tables and columns based on incoming metrics. When configured for ClickHouse, it utilizes the clickhouse-go v1.5.4 driver, which employs a unique DSN format and a set of specialized type conversion rules to map Telegraf’s data types directly to ClickHouse’s native types. This approach ensures optimal storage and retrieval performance in high-throughput environments, making it well-suited for real-time analytics and large-scale data warehousing. The dynamic schema creation and precise type mapping enable detailed time-series data logging, crucial for monitoring modern, distributed systems.</p>

Configuration

MavLink

Clickhouse

Input and output integration examples

MavLink

<ol> <li> <p><strong>Real-Time Fleet Monitoring</strong>: 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.</p> </li> <li> <p><strong>Automated Anomaly Detection</strong>: 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.</p> </li> <li> <p><strong>Data-Driven Maintenance Scheduling</strong>: 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.</p> </li> <li> <p><strong>Enhanced Research Analytics</strong>: 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.</p> </li> </ol>

Clickhouse

<ol> <li> <p><strong>Real-Time Analytics for High-Volume Data</strong>: Use the plugin to feed streaming metrics from large-scale systems into ClickHouse. This setup supports ultra-fast query performance and near real-time analytics, ideal for monitoring high-traffic applications.</p> </li> <li> <p><strong>Time-Series Data Warehousing</strong>: Integrate the plugin with ClickHouse to create a robust time-series data warehouse. This use case allows organizations to store detailed historical metrics and perform complex queries for trend analysis and capacity planning.</p> </li> <li> <p><strong>Scalable Monitoring in Distributed Environments</strong>: Leverage the plugin to dynamically create tables per metric type in ClickHouse, making it easier to manage and query data from a multitude of distributed systems without prior schema definitions.</p> </li> <li> <p><strong>Optimized Storage for IoT Deployments</strong>: Deploy the plugin to ingest data from IoT sensors into ClickHouse. Its efficient schema creation and native type mapping facilitate the handling of massive volumes of data, enabling real-time monitoring and predictive maintenance.</p> </li> </ol>

Feedback

Thank you for being part of our community! If you have any general feedback or found any bugs on these pages, we welcome and encourage your input. Please submit your feedback in the InfluxDB community Slack.