# Deploy with Custom UDF

This guide provides instructions for setting up custom UDF deployment package (UDFs, TICKscripts, models) and `config.json` in **Time Series Analytics Microservice**.

## Configuration

- **`config.json`**:
   - Understand the configuration documented at [link](../get-started.md#configjson) and update
     as per the need to configure the custom UDF deployment package

- **`UDF Deployment package`**:

  1. **`udfs/`**:
     - Contains Python scripts for UDFs.
     - If additional Python packages are required, list them in `requirements.txt` using pinned versions.
     - For detailed instructions on writing UDFs, see the [How to Write a UDF](./write-user-defined-function.md) guide.

  2. **`tick_scripts/`**:
     - Contains TICKscripts for data processing, analytics, and alerts.
     - Mode detail on writing TICKscript is available at <https://docs.influxdata.com/kapacitor/v1/reference/tick/introduction/>

     - Example TICKscript:

      ```bash
      dbrp "datain"."autogen"

      var data0 = stream
          |from()
              .database('datain')
              .retentionPolicy('autogen')
              .measurement('opcua')
          @windturbine_anomaly_detector()
          |alert()
              .crit(lambda: "anomaly_status" > 0)
              .message('Anomaly detected: Wind Speed: {{ index .Fields "wind_speed" }}, Grid Active Power: {{ index .Fields "grid_active_power" }}, Anomaly Status: {{ index .Fields "anomaly_status" }}')
              .mqtt('my_mqtt_broker')
              .topic('alerts/wind_turbine')
              .qos(1)
          |log()
              .level('INFO')
          |influxDBOut()
              .buffer(0)
              .database('datain')
              .measurement('opcua')
              .retentionPolicy('autogen')
      ```
       - Key sections:
         - **Input**: Fetch data from Telegraf (stream).
         - **Processing**: Apply UDFs for analytics.
         - **Alerts**: Configuration for publishing alerts (e.g., MQTT). Refer to the [document](./configure-alerts.md#helm---publish-mqtt-alerts)
         - **Logging**: Set log levels (`INFO`, `DEBUG`, `WARN`, `ERROR`).
         - **Output**: Publish processed data.

          For more details, refer to the [Kapacitor TICK Script Documentation](https://docs.influxdata.com/kapacitor/v1/reference/tick/introduction/).

  3. **`models/`**:
     - Contains model files (e.g., `.pkl`) used by UDF Python scripts.

### Docker compose deployment

> **Note:** Follow the [Get started](../get-started.md) guide to deploy the `Wind Turbine Anomaly Detection` and `Weld Anomaly Detection` sample apps.

The files at `edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-time-series/apps/wind-turbine-anomaly-detection/time-series-analytics-config` for `Wind Turbine Anomaly Detection` sample app OR `edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-time-series/apps/weld-anomaly-detection/time-series-analytics-config` for `Weld Anomaly Detection` representing the UDF deployment package (UDFs, TICKscripts, models)
and `config.json` have been volume mounted for the Time Series Analytics Microservice service in `edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-time-series/docker-compose.yml`. If anything needs to be updated in the custom UDF deployment package and `config.json`, it has to be done at this location and the time series analytics microservice container needs to be restarted manually.

### Helm Deployment

> **Note:** This method does not use a volume mount. Instead, the `kubectl cp` command is used to copy the UDF deployment package into the container, which serves the same purpose.

1. Update the UDF deployment package by following the instructions in [Configure Time Series Analytics Microservice with Custom UDF Deployment Package](./configure-custom-udf.md#configuration).

2. Copy the updated UDF deployment package using the [steps](../get-started/deploy-with-helm.md#step-4-copy-the-udf-package-for-helm-deployment-to-time-series-analytics-microservice).

3. Make the following REST API call to the Time Series Analytics microservice for the updated custom UDF:

   ```sh
   curl -k -X 'POST' \
   'https://<HOST_IP>:30001/ts-api/config' \
   -H 'accept: application/json' \
   -H 'Content-Type: application/json' \
   -d '{
     "udfs": {
         "name": "<custom_UDF>",
         "models": "<custom_UDF>.pkl|<custom_UDF>.cb",
         "device"": "cpu|gpu"
     },
     "alerts": {
         "mqtt": {
             "mqtt_broker_host": "ia-mqtt-broker",
             "mqtt_broker_port": 1883,
             "name": "my_mqtt_broker"
         }
     }
   }'
   ```

4. Verify the logs of the Time Series Analytics Microservice:

   ```sh
   POD_NAME=$(kubectl get pods -n ts-sample-app -o jsonpath='{.items[*].metadata.name}' | tr ' ' '\n' | grep deployment-time-series-analytics-microservice | head -n 1)
   kubectl logs -f -n ts-sample-app $POD_NAME
   ```

For more details, refer to `Time Series Analytics` microservice API documents on [updating the config](./update-config.md).