Trendz Python Executor Configuration

• Overview
• Python Executor Standalone Installation
  • Step 1: Create Docker Compose File
  • Step 2: Create Volumes
  • Step 3: Start Python Executor
  • Step 4: Connect Trendz to Python Executor
• How to Migrate Trendz Python Executor 1.13.3 to Trendz Python Executor 1.14.0
  • Modify Docker Compose File
  • Create Volumes for Python Executor
  • Restart Python Executor
• How to Connect Additional Libraries to the Python Executor
  • Step 1. Locate the Volume
  • Step 2. Add a requirements.txt File
  • Step 3. Restart the Docker Container
  • Step 4. Verify Installation
• Next steps

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

Run the following commands before starting the Docker container. These commands also change the owner of the newly created folders to the Docker container user. The chown command requires sudo permissions and will prompt for your password:

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mkdir -p ~/.mytrendz-data && sudo chown -R 799:799 ~/.mytrendz-data
mkdir -p ~/.mytrendz-data/python-executor && sudo chown -R 799:799 ~/.mytrendz-data/python-executor

Note: Replace the directory ~/.mytrendz-data with the directories 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.

Edit /usr/share/trendz/conf/trendz.conf and add:

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export SCRIPT_ENGINE_TIMEOUT=30000
export SCRIPT_ENGINE_PROVIDER=DOCKER_CONTAINER
export SCRIPT_ENGINE_DOCKER_PROVIDER_URL=PYTHON_EXECUTOR_HOST:PYTHON_EXECUTOR_PORT

Replace PYTHON_EXECUTOR_HOST and PYTHON_EXECUTOR_PORT with actual host and port values.

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

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

Create Volumes for Python Executor

Run the following commands before starting the Docker container. These commands also change the owner of the newly created folders to the Docker container user. The chown command requires sudo permissions and will prompt for your password:

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mkdir -p ~/.mytrendz-data && sudo chown -R 799:799 ~/.mytrendz-data
mkdir -p ~/.mytrendz-data/python-executor && sudo chown -R 799:799 ~/.mytrendz-data/python-executor

Note: Replace the directory ~/.mytrendz-data with the directories 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 volume is usually at:

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cd ~/.mytrendz-data/python-executor

Step 2. Add a requirements.txt File

Create the file with the library:

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echo 'emoji==2.2.0' > requirements.txt

Step 3. Restart the Docker Container

Find the container name and restart it:

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docker compose ps 
docker compose restart mypyexecutor

Step 4. Verify Installation

Check the logs:

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docker compose logs mypyexecutor

You should see:

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

After the container restarts, you can import and use the custom libraries in your Trendz Calculation Fields or Trendz Custom Prediction Models.

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.