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Installation > Python Executor Configuration > Windows
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Trendz Python Executor Configuration

Overview

Trendz Python Executor is required to run:

  • Python Calculation fields
  • All prediction models (except Fourier Transformation)
  • Code generation for Metric Explorer

Starting from version 1.14.0, the only way to run these features is via Trendz Python Executor using Docker (or Kubernetes).

Python Executor Standalone Installation

Step 1: Create Docker Compose File

Create the Docker Compose file with the following configuration:

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version: '3.0'
services:
  mypyexecutor:
    restart: always
    image: "thingsboard/trendz-python-executor:1.14.0"
    ports:
      - "8181:8181"
    environment:
      SCRIPT_ENGINE_RUNTIME_TIMEOUT: 30000
      EXECUTOR_MANAGER: 1
      EXECUTOR_SCRIPT_ENGINE: 6
      THROTTLING_QUEUE_CAPACITY: 10
      THROTTLING_THREAD_POOL_SIZE: 6
      NETWORK_BUFFER_SIZE: 10485760
    volumes:
      - mytrendz-data/python-executor:/python-executor

Explanation of key fields:

  • 8181 - Python executor port for communication with Trendz service
  • restart: always - automatically restarts the executor on failure or system reboot
  • thingsboard/trendz-python-executor:1.14.0 - Docker image for Trendz Python Executor
  • SCRIPT_ENGINE_RUNTIME_TIMEOUT - timeout for Python script execution
  • mytrendz-data/python-executor:/python-executor - mounts the volume mytrendz-data/python-executor to Trendz Python Executor additional data directory

Step 2: Create Volumes

Windows users should use Docker-managed volumes for Trendz data. Create a Docker volume before executing the Docker run command. Open “Docker Quickstart Terminal” and run:

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docker volume create mytrendz-data-python-executor

Note: Replace the volume name mytrendz-data-python-executor with the name you plan to use in docker-compose.yaml.

Step 3: Start Python Executor

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docker compose up -d
docker compose logs -f mypyexecutor

Step 4: Connect Trendz to Python Executor

Configure Trendz to communicate with the Python Executor.

Open Notepad as Administrator and edit:

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C:\Program Files (x86)\trendz\conf\trendz.yml

Locate the script-engine block and configure:

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script-engine:
  provider: "${SCRIPT_ENGINE_PROVIDER:DOCKER_CONTAINER}"
  runtime-timeout: "${SCRIPT_ENGINE_TIMEOUT:60000}"
  callback-timeout: "${SCRIPT_ENGINE_TIMEOUT:60000}"
  docker-provider-url: "${SCRIPT_ENGINE_DOCKER_PROVIDER_URL:PYTHON_EXECUTOR_HOST:PYTHON_EXECUTOR_PORT}"
  websocket-buffer-size: "${SCRIPT_ENGINE_WEBSOCKET_BUFFER_SIZE:20971520}"
  websocket-concurrency: "${SCRIPT_ENGINE_WEBSOCKET_CONCURRENCY:5}"

Replace PYTHON_EXECUTOR_HOST and PYTHON_EXECUTOR_PORT with your Python Executor service values and ensure Trendz can reach this network destination.

How to Migrate Trendz Python Executor 1.13.3 to Trendz Python Executor 1.14.0

If you already have a Python Executor with a version earlier than 1.14.0 connected to Trendz, you should migrate it before updating Trendz to 1.14.0.

Modify Docker Compose File

Locate the docker-compose.yaml file from which the Python Executor was launched.

Change the Python Executor image tag to version 1.14.0:

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    image: "thingsboard/trendz-python-executor:1.14.0"

Add the following lines at the end of the Python Executor configuration:

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   volumes:
      - mytrendz-data/python-executor:/python-executor

Add the following lines at the end of the Docker Compose file:

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volumes:
  mytrendz-data-python-executor:
    external: true

The final docker-compose.yaml should look like:

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version: '3.0'
services:
  mypyexecutor:
    restart: always
    image: "thingsboard/trendz-python-executor:1.14.0"
    ports:
      - "8181:8181"
    environment:
      SCRIPT_ENGINE_RUNTIME_TIMEOUT: 30000
      EXECUTOR_MANAGER: 1
      EXECUTOR_SCRIPT_ENGINE: 6
      THROTTLING_QUEUE_CAPACITY: 10
      THROTTLING_THREAD_POOL_SIZE: 6
      NETWORK_BUFFER_SIZE: 10485760
    volumes:
      - mytrendz-data/python-executor:/python-executor
volumes:
  mytrendz-data-python-executor:
    external: true

Create Volumes for Python Executor

Windows users should use Docker-managed volumes for Trendz data. Create a Docker volume before executing the Docker run command. Open “Docker Quickstart Terminal” and run:

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docker volume create mytrendz-data-python-executor

Note: Replace the volume name mytrendz-data-python-executor with the name you plan to use in docker-compose.yaml.

Restart Python Executor

Restart the Python Executor to apply the changes:

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docker compose restart -d
docker compose logs -f mypyexecutor

How to Connect Additional Libraries to the Python Executor

If necessary, you can add additional Python libraries to the Python Executor and use them in your Trendz Calculation Fields or Trendz Custom Prediction Models.

For example, if you want to add the emoji library (specific version 2.2.0), follow these steps.

Step 1. Locate the Volume

The Python Executor data is mapped to a local folder via Docker volumes. By default:

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cd %USERPROFILE%\mytrendz-data\python-executor

Step 2. Add a requirements.txt File

Create a file named requirements.txt in this folder with the library you need. For example:

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echo emoji==2.2.0 > requirements.txt

Step 3. Restart the Docker Container

After updating requirements.txt, restart the Python Executor container:

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docker compose ps 
docker compose restart mypyexecutor

Step 4. Verify Installation

Check the logs to ensure the library installed successfully:

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docker compose logs mypyexecutor

You should see something like:

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Installing custom Python requirements...
Requirement already satisfied: emoji==2.2.0 in /usr/local/lib/python3.9/site-packages

Next steps

  • Getting started guide - These guide provide quick overview of main Trendz features.

  • Metric Explorer - Learn how to explore and create new metrics with Trendz Metric Explorer.

  • Anomaly Detection - Learn how to identify anomalies in the data.

  • Calculated Fields - Learn about Calculated fields and how to use them.

  • States - Learn how to define and analyse states for assets based on raw telemetry.

  • Prediction - Learn how to make forecasts and predict telemetry behavior.

  • Filters - Learn how filter dataset during analysis.

  • Available Visualizations - Learn about visualization widgets available in Trendz and how to configure them.

  • Share and embed Visualizations - Learn how to add Trendz visualizations on ThingsBoard dashboard or 3rd party web pages.

  • AI Assistant - Learn how to utilize Trendz AI capabilities.