OPC UA and Librato Integration
Powerful performance with an easy integration, powered by Telegraf, the open source data connector built by InfluxData.
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Time series database
Source: DB Engines
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Table of Contents
Powerful Performance, Limitless Scale
Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.
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Input and output integration overview
<p>The OPC UA plugin provides an interface for retrieving data from OPC UA server devices, facilitating effective data collection and monitoring.</p>
<p>The Librato plugin for Telegraf is designed to facilitate seamless integration with the Librato Metrics API, allowing for efficient metric reporting and monitoring.</p>
Integration details
OPC UA
<p>The OPC UA Plugin retrieves data from devices that communicate using the OPC UA protocol, allowing you to collect and monitor data from your OPC UA servers.</p>
Librato
<p>The Librato plugin enables Telegraf to send metrics to the Librato Metrics API. To authenticate, users must provide an <code>api_user</code> and <code>api_token</code>, which can be acquired from the Librato account settings. This integration allows for efficient monitoring and reporting of custom metrics within the Librato platform. The plugin also utilizes a <code>source_tag</code> option that can enrich the metrics with contextual information from Point Tags; however, it does not currently support sending associated Point Tags. It is essential to note that any point value sent that cannot be converted to a float64 type will be skipped, ensuring that only valid metrics are processed and sent to Librato. The plugin also supports secret-store options for managing sensitive authentication credentials securely, facilitating best practices in credential management.</p>
Configuration
OPC UA
Librato
Input and output integration examples
OPC UA
<ol> <li> <p><strong>Basic Configuration</strong>: Set up the plugin with your OPC UA server endpoint and desired metrics. This allows Telegraf to start gathering metrics from the configured nodes.</p> </li> <li> <p><strong>Node ID Setup</strong>: Use the configuration to specify specific nodes, such as temperature sensors, to monitor their values in real-time. For example, configure node <code>ns=3;s=Temperature</code> to gather temperature data directly.</p> </li> <li> <p><strong>Group Configuration</strong>: Simplify monitoring multiple nodes by grouping them under a single configuration—this sets defaults for all nodes in that group, thereby reducing redundancy in setup.</p> </li> </ol>
Librato
<ol> <li> <p><strong>Real-time Application Monitoring</strong>: Utilize Librato to collect performance metrics from a web application in real-time. This setup involves sending response times, error rates, and user interactions to Librato, allowing developers to monitor the application’s health and performance metrics closely. By analyzing these metrics, teams can quickly identify and address performance bottlenecks or application failures before they impact end users.</p> </li> <li> <p><strong>Infrastructure Metrics Aggregation</strong>: Leverage this plugin to gather and send metrics from various infrastructure components, such as servers or containers, to Librato for centralized monitoring. Configuring the plugin to send CPU, memory usage, and disk I/O metrics enables system administrators to have a comprehensive view of infrastructure performance, assisting in capacity planning and resource optimization strategies.</p> </li> <li> <p><strong>Custom Metrics for Business Operations</strong>: Feed business-specific metrics, such as sales transactions or user sign-ups, to the Librato service using this plugin. By tracking these custom metrics, businesses can gain insights into their operational performance and make data-driven decisions to enhance their strategies, marketing efforts, or product development initiatives.</p> </li> <li> <p><strong>Anomaly Detection in Metrics</strong>: Implement monitoring tools that utilize machine learning for anomaly detection. By continuously sending real-time metrics to Librato, teams can analyze trends and automatically flag unusual behavior, such as sudden spikes in latency or unusual traffic patterns, enabling timely intervention and troubleshooting.</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.
Powerful Performance, Limitless Scale
Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.
See Ways to Get Started
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