ActiveMQ and MariaDB Integration

Input and output integration overview

<p>The ActiveMQ Input Plugin collects metrics from the ActiveMQ message broker through its Console API, providing insights into the performance and status of message queues, topics, and subscribers.</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

ActiveMQ

<p>The ActiveMQ Input Plugin interfaces with the ActiveMQ Console API to gather metrics related to queues, topics, and subscribers. ActiveMQ, a widely-used open-source message broker, supports various messaging protocols and provides a robust Web Console for management and monitoring. This plugin allows users to track essential metrics including queue sizes, consumer counts, and message counts across different ActiveMQ entities, thereby enhancing observability within messaging systems. Users can configure various parameters such as the WebConsole URL and basic authentication credentials to tailor the plugin to their environment. The metrics collected can be used for monitoring the health and performance of messaging applications, facilitating proactive management and troubleshooting.</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

ActiveMQ

MariaDB

Input and output integration examples

ActiveMQ

<ol> <li> <p><strong>Proactive Queue Monitoring</strong>: Use the ActiveMQ plugin to monitor queue sizes in real-time for a high-volume trading application. This implementation allows teams to receive alerts when queue sizes exceed a certain threshold, enabling rapid response to potential downtime caused by backlogs, thereby ensuring continuous availability of trading operations.</p> </li> <li> <p><strong>Performance Baselines and Anomaly Detection</strong>: Integrate this plugin with machine learning frameworks to establish performance baselines for message throughput. By analyzing historical data collected through this plugin, teams can flag anomalies in processing rates, leading to quicker identification of issues impacting service reliability and performance.</p> </li> <li> <p><strong>Cross-Messaging System Analytics</strong>: Combine metrics from ActiveMQ with those from other messaging systems in a centralized dashboard. Users can visualize and compare performance data, such as enqueue and dequeue rates, providing valuable insights into the overall messaging architecture and assisting in optimizing the message flow between different brokers.</p> </li> <li> <p><strong>Subscriber Performance Insights</strong>: Leverage the subscriber metrics collected by this plugin to analyze behavior patterns and optimize configuration for consumer applications. Understanding metrics such as dispatched queue size and counter values can guide adjustments to improve processing efficiency and resource allocation.</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

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