Simple metrics are used to store simple data, like measures and logical variables.

Creating a Simple metric

To add a new Simple Metric to a Thing Definition, you should:

1. Enter the RAW Data page.
2. Select the Thing Definitions tab.
3. Select the Thing Definition to edit.
4. Press the Add Metric button.
5. Select the Simple Metric type and provide the required information.
6. Press the Save button and edit the additional information, if needed.

Editing a Simple metric

Once a Simple Metric has been saved, you can configure more information on it.

A Simple Metric is described by:  

General

• Label: the metric label shown within the widget.
• Name: the name of the metric, also used to reference it within the dashboards' templates.
• Description: the text describing the metric. The description is displayed as a tooltip within the dashboard widgets.
• Unit: the unit of measurement (e.g. Kg, °C, pieces, liters...etc.)
• Value Type: the type of the value, one of Boolean, Double, Float, Integer, Long, String.
  Pay attention to select the right type, if not, metric values may not be stored correctly.    
• Group: the group the metric belongs to (e.g. Temperatures, System).
• Privatizable by customer: allow the customer to define whether the metric is private or not. 
  For more details, refer to this Privatizable date article.

Mapping

Into a Simple metric, you can configure the value to be assigned to the metric when the thing is created or activated the first time. The value is registered by using the creation/activation timestamp.

Data

In case of numeric metrics, the Data Type allows you to define whether the metric values are DISCRETE or CONTINUOUS, and then you can also define Dictionary or Thresholds.

Range

In case the metric is of type numeric, in this section, you can specify the minimum and maximum values the metric can assume.

Minimum and maximum values can also be dynamically redefined by specifying a property or a metric for them.

Discarding invalid values

Suppose a thing produces data points with a frequency higher than the real need (e.g. ambient temperature sensor), unnecessary data points will be generated with the effect to consume DPH for Data Points Processing. 

You can define whether to discard consecutive equal values received by the IoT Connector.

With this approach, the granularity of the data will be lowered, and therefore the current data (if missing) is to be considered equal to the previous data. In a time-series-chart widget, a STEP visualization is recommended in order to highlight changes in the series of data.

Out of range data points can be discarded, avoiding unexpected behaviors, like alerts and rules triggering or wrong widget visualization (e.g. spikes on time-series-chart widget).

Note that, only constant values are used to validate the incoming metric values. In case you are using a dynamic range, remember to specify a static range that at least contains the dynamic one.