Docker and MariaDB Integration

Input and output integration overview

<p>The Docker input plugin allows you to collect metrics from your Docker containers using the Docker Engine API, facilitating enhanced visibility and monitoring of containerized applications.</p>

<p>This plugin writes metrics from Telegraf directly into MariaDB using parameterized SQL INSERT statements, offering a flexible way to store metrics in structured, relational tables.</p>

Integration details

Docker

<p>The Docker input plugin for Telegraf gathers valuable metrics from the Docker Engine API, providing insights into running containers. This plugin utilizes the Official Docker Client to interface with the Engine API, allowing users to monitor various container states, resource allocations, and performance metrics. With options for filtering containers by names and states, along with customizable tags and labels, this plugin supports flexibility in monitoring containerized applications in diverse environments, whether on local systems or within orchestration platforms like Kubernetes. Additionally, it addresses security considerations by requiring permissions for accessing Docker’s daemon and emphasizes proper configuration when deploying within containerized environments.</p>

MariaDB

<p>The SQL output plugin in Telegraf enables direct writing of metrics into SQL-compatible databases like MariaDB by executing parameterized SQL statements. With support for the MySQL driver, the plugin seamlessly integrates with MariaDB for reliable, structured metric storage. This setup is ideal for users who prefer SQL-based analytics or want to store metrics alongside business data for unified querying. MariaDB is a community-developed, enterprise-grade fork of MySQL that emphasizes performance, security, and openness. The plugin supports inserting time series metrics into custom schemas, enabling flexible analytics and integrations with BI tools like Metabase or Grafana using SQL connectors.</p>

Configuration

Docker

MariaDB

Input and output integration examples

Docker

<ol> <li> <p><strong>Monitoring the Performance of Containerized Applications</strong>: Use the Docker input plugin in order to track the CPU, memory, disk I/O, and network activity of applications running in Docker containers. By collecting these metrics, DevOps teams can proactively manage resource allocation, troubleshoot performance bottlenecks, and ensure optimal application performance across different environments.</p> </li> <li> <p><strong>Integrating with Kubernetes</strong>: Leverage this plugin to gather metrics from Docker containers orchestrated by Kubernetes. By filtering out unnecessary Kubernetes labels and focusing on key metrics, teams can streamline their monitoring solutions and create dashboards that provide insights into the overall health of microservices running within the Kubernetes cluster.</p> </li> <li> <p><strong>Capacity Planning and Resource Optimization</strong>: Use the metrics collected by the Docker input plugin to perform capacity planning for Docker deployments. Analyzing usage patterns helps identify underutilized resources and over-provisioned containers, guiding decisions on scaling up or down based on actual usage trends.</p> </li> <li> <p><strong>Automated Alerting for Container Anomalies</strong>: Set up alerting rules based on the metrics collected through the Docker plugin to notify teams of unusual spikes in resource usage or service disruptions. This proactive monitoring approach helps maintain service reliability and optimize the performance of containerized applications.</p> </li> </ol>

MariaDB

<ol> <li> <p><strong>Business Intelligence Integration</strong>: Store application performance metrics directly into MariaDB and connect it to BI tools like Metabase or Apache Superset. This setup allows blending of operational data with business KPIs for unified dashboards, enhancing visibility across departments.</p> </li> <li> <p><strong>Compliance Reporting with Historical Metrics</strong>: Use this plugin to log metrics into MariaDB for audit and compliance use cases. The relational model enables precise querying of past performance indicators with timestamped entries, supporting regulatory documentation.</p> </li> <li> <p><strong>Custom Alerting Based on SQL Logic</strong>: Insert metrics into MariaDB and use custom SQL queries to define alert thresholds or conditions. Combined with cron jobs or scheduled scripts, this enables advanced alerting workflows not possible with traditional metric platforms.</p> </li> <li> <p><strong>IoT Sensor Metrics Storage</strong>: Collect sensor data from IoT devices via Telegraf and store it in MariaDB using a normalized schema. This approach is cost-effective and integrates well with existing SQL-based systems for real-time or historical analysis.</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.