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ogamma Visual Logger for OPC

ogamma Visual Logger for OPC is an integration tool to:

  • Collect data from OPC UA Servers, as well as from classic OPC DA servers using additional wrapper application;

  • Store that data in time-series database:

    • TimescaleDB (PostgreSQL database optimized to store time-series data);
    • PostgreSQL regular version;
    • InfluxDB (versions 1.7, 1.8 or 2.x);
    • Confluent: Cloud and Enterprise;
    • Apache Kafka;
    • Microsoft SQL Server;
    • MySQL;
    • SQLite;
    • MQTT Brokers. Using MQTT protocol, data can be published to any Cloud/IoT platform providing access over MQTT such as:
      • Generic MQTT broker (for example, Eclipse Mosquito).
      • Microsoft Azure IoT Hub;
      • AWS IoT Broker;
      • Google Cloud IoT Core MQTT Bridge;

  • Visualize data using Grafana, acting as a REST back-end for its SimpleJson data source plugin. It can fetch real time or historical data directly from OPC UA Servers, or from PostgreSQL.

  • Analyze data using query language (specific to type of the used TSDB).

Each instance of ogamma Visual Logger can implement only one role or all of them.

Multiple instances can run in parallel in different roles, making horizontal scaling easy.

Note that to enable all the features third party components are required. The easiest way to setup and run ogamma Visual Logger and dependency services is to use Docker images, which can be easily pulled and then started with single command docker-compose up, by using of the docker-compose.yml file which is available at this repository: https://github.com/onewayautomation/ogamma-logger/blob/master/docker/docker-compose.yml Note that .yml files have specific version numbers of images. If you have containers running older versions of images, after updating of .yml files they might stop working due to issues with upgrading of data volumes.

Context Diagram

ogamma Visual Logger - Context Diagram

Getting Started with Docker image.

Docker image is available at: https://hub.docker.com/r/ogamma/logger.

File docker/docker-compose.yml allows to pull ogamma Visual Logger image. The folder docker has also .yml files for other images (time-series databases), that can be started independently, by passing file name in the command docker compose -f <file-name> :

  • Databases (TimescaleDB, InfluxDB versions 1.x and 2.x, Apache Kafka);
  • PgAdmin, to manage database PostgreSQL;
  • Grafana, optional, to visualize data;
  • Sample OPC UA Server from Microsoft.
  • MQTT brocker.

Prerequisites.

There is only one: Docker Desktop, available to download here https://www.docker.com/products/docker-desktop. It can be installed in Windows as well as in Linux machines.

To setup all service components and run them:

  • Pull this repository to some local folder;
  • Open shell terminal (PowerShell on Windows, or bash in Linux);
  • Navigate to the folder docker where file docker-compose.yml is located;
  • Run command docker-compose up -d.
  • To start other containers, pass yml file name. For example, to start InfluxDB version 1.8, run command: docker compose -f influxdb.yml up -d.

The container image from Docker Hub will be pulled (this might take few or more minutes, but this step happens only once, when you run this command at the very first time), and service(s) defined in the yml file will be started.

After that:

To log data from OPC UA Servers to the specific database, the instance of the ogamma Visual Logger for OPC application needs to be configured. Refer to the User Manual for details.

Distribution packages for Windows and Linux.

For links to download installation packages please refer to section Deploy of the online User Manual: https://onewayautomation.com/visual-logger-docs/html/deploy.html

User Manual

For more information, please refer to online User Manual available at https://onewayautomation.com/visual-logger-docs/html/index.html

You can see Visual Logger in action in youtube video at https://www.youtube.com/watch?v=mY9Wh8wAKZg&t=215s, where it is used to explain OPC UA terms such as publishing interval, sampling interval and queue size for monitored items.

Introduction video with more detailed instructions is available on youtube at https://youtu.be/yZdsyVz7hw0 (for older version, needs update).