Metrics

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Introduction to Metrics

Metrics provide quantifiable measurements to gauge performance, behavior, and other essential information. They facilitate in-depth analysis by offering insights not readily apparent from raw data.

Defining Metrics

Defining metrics involves several key parameters:

Name

The reference name for the metric, which must be unique across all metrics.

Description

A detailed overview of the metric.

Type

Type of metric (simple, ratio, cumulative, derived).

Type Parameters

Parameters specific to each metric type.

How to create and edit a metric

In the Catalog section, click the Manage button on the desired data source. Then, navigate to the Metrics tab to create or edit a metric.

To better understand the structure, here’s an illustrative example of a metrics specification:

metrics:
  - name: order_total
    description: Total orders processed.
    type: simple
    type_params:
      measure: order_count
    label: Total Orders

Supported Metric Types

Darling supports various metric types:

Simple Metrics
Cumulative Metrics
Derived Metrics
Ratio Metrics

Simple metrics directly reference a measure. They can be thought of as a function accepting a single measure as input.Example:

metrics:
  - name: cancellations
    type: simple
    type_params:
      measure: cancellations_count
    filter: |
      {{ Dimension('order__value') }} > 100 and {{ Dimension('user__acquisition') }}

Cumulative metrics aggregate a measure over a defined time window. If no window is specified, the aggregation occurs over all time.Example:

metrics:
  - name: wau_rolling_7
    type: cumulative
    type_params:
      measures:
        - distinct_users
      window: 7 days

Derived metrics use expressions involving other metrics, enabling calculations based on existing metric values.Example:

metrics:
  - name: order_gross_profit
    type: derived
    type_params:
      expr: revenue - cost
      metrics:
        - name: order_total
          alias: revenue
        - name: order_cost
          alias: cost

Ratio metrics compute a ratio between two metrics. Constraints can be applied to the numerator, denominator, or both.Example:

metrics:
  - name: cancellation_rate
    type: ratio
    type_params:
      numerator: cancellations
      denominator: transaction_amount

When creating metrics, maintain consistency in definitions. Avoid overlapping names and ambiguity to ensure clarity.

Conclusion

Metrics are crucial for deriving actionable insights. Understanding the various metric types and their definitions will enable you to build comprehensive data models.

FAQ: Can the same metric name be used across different semantic models?

No, metric names should be unique across all semantic models to prevent ambiguity and ensure clarity in analysis.

Darling App

Darling API

Introduction to Metrics

Defining Metrics

Name

Description

Type

Type Parameters

How to create and edit a metric

Supported Metric Types

Conclusion

​Introduction to Metrics

​Defining Metrics

Name

Description

Type

Type Parameters

​How to create and edit a metric

​Supported Metric Types

​Conclusion

Introduction to Metrics

Defining Metrics

How to create and edit a metric

Supported Metric Types

Conclusion