OPC UA and Google Cloud Monitoring Integration
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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 Stackdriver plugin allows users to send metrics directly to a specified project in Google Cloud Monitoring, facilitating robust monitoring capabilities across their cloud resources.</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>
Google Cloud Monitoring
<p>This plugin writes metrics to a project in Google Cloud Monitoring, which used to be known as Stackdriver. Authentication is a prerequisite and can be achieved via service accounts or user credentials. The plugin is designed to group metrics by a <code>namespace</code> variable and metric key, facilitating organized data management. However, users are encouraged to use the <code>official</code> naming format for enhanced query efficiency. The plugin supports additional configurations for managing metric representation and allows tags to be treated as resource labels. Notably, it imposes certain restrictions on the data it can accept, such as not allowing string values or points that are out of chronological order.</p>
Configuration
OPC UA
Google Cloud Monitoring
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>
Google Cloud Monitoring
<ol> <li> <p><strong>Multi-Project Metric Aggregation</strong>: Use this plugin to send aggregated metrics from various applications across different projects into a single Google Cloud Monitoring project. This use case helps centralize metrics for teams managing multiple applications, providing a unified view for performance monitoring and enhancing decision-making. By configuring different quota projects for billing, organizations can ensure proper cost management while benefiting from a consolidated monitoring strategy.</p> </li> <li> <p><strong>Anomaly Detection Setup</strong>: Integrate the plugin with a machine learning-based analytics tool that identifies anomalies in the collected metrics. Using the historical data provided by the plugin, the tool can learn normal baseline behavior and promptly alert the operations team when unusual patterns arise, enabling proactive troubleshooting and minimizing service disruptions.</p> </li> <li> <p><strong>Dynamic Resource Labeling</strong>: Implement dynamic tagging by utilizing the tags_as_resource_label option to adaptively attach resource labels based on runtime conditions. This setup allows metrics to provide context-sensitive information, such as varying environmental parameters or operational states, enhancing the granularity of monitoring and reporting without changing the fundamental metric structure.</p> </li> <li> <p><strong>Custom Metric Visualization Dashboards</strong>: Leverage the data collected by the Google Cloud Monitoring output plugin to feed a custom metrics visualization dashboard using a third-party framework. By visualizing metrics in real-time, teams can achieve better situational awareness, notably by correlating different metrics, improving operational decision-making, and streamlining performance management workflows.</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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