Defining Metrics

Define Metrics using the console, API, or Terraform.

Key concepts

• Metric type: Defines how the data is aggregated.
• Data Pool: The source of the data for the Metric.
• Dimensions: A set of columns used to categorize and segment the Metric data.
• Filters: A set of filters that define a subset of the Data Pool records to include in the Metric calculations.

Defining a Metric

Console

This example shows how to create a Sum Metric to define a “Revenue” metric by summing the “total_price” column in the TacoSoft sample dataset.

API

This example shows the createSumMetric API mutation to define a “Revenue” metric by summing the “total_price” column in the TacoSoft sample dataset.

Query

mutation {
  createSumMetric(input: {
    dataPool: "DPOXXXXXXXXXXXXXX",
    uniqueName: "Revenue",
    dimensions: [
      {
        columnName: "restaurant_name"
      }
    ],
    measure: {
        columnName: "taco_total_price"
      }
  }) {
    metric {
      id
      uniqueName
    }
  }
}

Terraform

resource "propel_metric" "revenue_metric" {
  unique_name = "Revenue"
  data_pool   = "DPOXXXXXXXXXXXXXX"

  type    = "SUM"
  measure = "taco_total_price"

  dimensions = ["restaurant_name"]
}

---

Examples

Example 1: Metrics with filters

This example shows how to create a Sum Metric with filters to define an “Al Pastor Revenue” metric by summing the “total_price” column for Al Pastor tacos in the TacoSoft sample dataset.

Console

API

This example shows the createSumMetric API mutation to define a “Revenue” metric by summing the “total_price” column in the TacoSoft sample dataset with filters.

Query

mutation {
  createSumMetric(input: {
    dataPool: "DPOXXXXXXXXXXXXXX",
    uniqueName: "AlPastorRevenue",
    dimensions: [
      {
        columnName: "restaurant_name"
      }
    ],
    measure: {
      columnName: "taco_total_price"
    }
    filterSql: "taco_name = 'Al Pastor'"
  }) {
    metric {
      id
      uniqueName
    }
  }
}

Terraform

resource "propel_metric" "al_pastor_revenue" {
  unique_name = "AlPastorRevenue"
  data_pool   = "DPOXXXXXXXXXXXXXX"

  type    = "SUM"
  dimensions = ["restaurant_name"]
  measure = "taco_total_price"
  filter_sql = "taco_name = 'Al Pastor'"
}

---

Example 2: Metrics with JSON fields

You can use JSON values in the metric definition, either as a measure or as filters.

This example shows how to create a Sum Metric using a measure from a JSON column.

Console

API

The following example demonstrates how to use the createSumMetric API mutation to define a “Revenue” metric. This metric sums the values located in a JSON column at the path: “OBJECT.order.total_price”.

Query

mutation {
  createSumMetric(input: {
    dataPool: "DPOXXXXXXXXXXXXXX",
    uniqueName: "Revenue",
    dimensions: [
      {
        columnName: "restaurant_name"
      }
    ],
    measure: {
        columnName: "OBJECT.order.total_price"
      }
  }) {
    metric {
      id
      uniqueName
    }
  }
}

Terraform

resource "propel_metric" "revenue_metric" {
  unique_name = "Revenue"
  data_pool   = "DPOXXX"

  type    = "SUM"
  measure = "OBJECT.order.total_price"

  dimensions = ["Restaurant"]
}

---

Example 3: Custom Metrics

Custom Metrics use SQL expressions to aggregate data from your Data Pool, enabling more complex business logic.

This example shows how to create a Custom Metric for “Average revenue per order”.

Console

API

The following example shows the query and input variables for the createCustomMetric API mutation to define an “Average revenue per order” metric with a custom expression.

Query

mutation {
  createCustomMetric(input: {
    dataPool: "DPOXXX",
    uniqueName: "Average revenue per order",
    filters: [
      {
        column: "ORDER_ID",
        operator: "IS_NOT_NULL"
      }
    ],
    dimensions: [
      {
        columnName: "Restaurant"
      }
    ],
    expression: "SUM(total_price) / COUNT_DISTINCT(ORDER_ID)"
  }) {
    metric {
      id
      uniqueName
    }
  }
}

Terraform

resource "propel_metric" "average_revenue_per_order" {
  unique_name = "Average revenue per order"
  data_pool   = "DPOXXX"
  dimensions = ["Restaurant"]
  expression = "SUM(total_price) / COUNT_DISTINCT(ORDER_ID)"
}

---