Validation & Examples

Complete Example: E-Commerce Domain

Here's a complete domain template combining all concepts -- 3 datasets, 3 relationships, nested calculated metrics, calculated dimensions, and a grain mapping:

schemaVersion: "2.0"
 
datasets:
  - name: orders
    label: "Orders"
    type: physical
    connectionId: "conn_1"
    connectionType: PostgreSQL
    schema: public
    table: orders
    primaryKey: [order_id]
    fields:
      identifiers:
        - name: order_id
          dataType: number
          type: primary
        - name: customer_id
          dataType: number
          type: foreign
      dimensions:
        - name: order_date
          label: "Order Date"
          dataType: date
          granularity: day
        - name: status
          label: "Status"
          dataType: string
      metrics:
        - name: amount
          label: "Revenue"
          dataType: number
          sourceField: amount
        - name: cost
          label: "Cost"
          dataType: number
          sourceField: cost
 
  - name: customers
    label: "Customers"
    type: physical
    connectionId: "conn_1"
    connectionType: PostgreSQL
    schema: public
    table: customers
    primaryKey: [id]
    fields:
      identifiers:
        - name: id
          dataType: number
          type: primary
      dimensions:
        - name: segment
          label: "Customer Segment"
          dataType: string
        - name: region
          label: "Region"
          dataType: string
 
  - name: returns
    label: "Returns"
    type: physical
    connectionId: "conn_1"
    connectionType: PostgreSQL
    schema: public
    table: returns
    fields:
      identifiers: []
      dimensions:
        - name: return_date
          label: "Return Date"
          dataType: date
      metrics:
        - name: amount
          label: "Return Amount"
          dataType: number
          sourceField: amount
 
relationships:
  - name: orders_to_customers
    sourceDataset: orders
    sourceFields: [customer_id]
    targetDataset: customers
    targetFields: [id]
    cardinality: many_to_one
    defaultJoinType: LEFT
    isAutoJoin: true
    discoveredBy: user
    isActive: true
 
calculatedMetrics:
  revenue:
    label: "Total Revenue"
    expression: "SUM({amount})"
    inputs:
      amount: { dataset: orders, field: amount }
 
  cost:
    label: "Total Cost"
    expression: "SUM({cost_amount})"
    inputs:
      cost_amount: { dataset: orders, field: cost }
 
  profit:
    label: "Profit"
    expression: "{revenue} - {cost}"
    inputs:
      revenue: { metric: revenue }
      cost: { metric: cost }
 
  profit_margin:
    label: "Profit Margin %"
    expression: "{profit} / NULLIF({revenue}, 0) * 100"
    inputs:
      profit: { metric: profit }
      revenue: { metric: revenue }
    format:
      type: percentage
      decimals: 1
 
  net_revenue:
    label: "Net Revenue"
    description: "Revenue minus returns"
    expression: "{total_sales} - COALESCE({total_returns}, 0)"
    inputs:
      total_sales: { dataset: orders, field: amount, aggregate: SUM }
      total_returns: { dataset: returns, field: amount, aggregate: SUM }
 
calculatedDimensions:
  order_month:
    label: "Order Month"
    expression: "DATE_TRUNC('month', {order_date})"
    inputs:
      order_date: { dataset: orders, field: order_date }
    dataType: date
 
  order_size:
    label: "Order Size"
    expression: |
      CASE
        WHEN {amount} > 1000 THEN 'Large'
        WHEN {amount} > 100 THEN 'Medium'
        ELSE 'Small'
      END
    inputs:
      amount: { dataset: orders, field: amount }
    dataType: string
 
grainMappings:
  - sourceDimension: order_month
    targetDataset: returns
    targetColumn: return_date

Validation Errors

When you save a domain template, Semaphor validates it and reports errors. Here are the validation codes you may encounter:

Errors

Code	Meaning	Fix
E001	Circular dependency detected (e.g., metric A references B which references A)	Break the cycle by removing one of the references
E002	Input references an unknown metric name	Check that the referenced metric exists in `calculatedMetrics`
E003	Input references an unknown field in a dataset	Verify the `dataset` has that `field` in its field lists
E004	Input references an unknown dataset name	Ensure the dataset is defined in the `datasets` array
E005	Token `{name}` in expression not found in inputs	Add the missing token name to the `inputs` map
E007	Calculated dimension references a metric	Dimensions can only reference columns or other dimensions
E008	Invalid filter on a metric	Check filter syntax (field + operator + value)
E009	Invalid expression syntax	Remove braces inside string literals
E010	Input requires an aggregate but none specified	Add `aggregate` to the input or wrap token in aggregate function
E011	Input references an unknown calculated dimension	Check that the dimension exists in `calculatedDimensions`
E012	Unknown field reference in metric expression	Check spelling of field names in the `expression`; ensure the referenced field is defined in the dataset
E013	Invalid metric expression	Review expression for circular dependencies, remove AS aliases, and check SQL syntax

Warnings

Code	Meaning	Action
E006	Input is defined but not used in expression	Remove the unused input or add it to the expression
W001	Dataset has no primary key signal	Add `primaryKey` or mark identifier as `type: primary`
W002	Relationship may cause metric inflation	Consider using `aggregate_then_join` strategy or review cardinality

Next Steps

Agent Guide -- Build templates programmatically with validation rules
User Guide -- How end users interact with your domain in dashboards