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Expressions

Learn how to define field expressions using SQL, lookups, and arrays.

Learning Objectives​

After completing this guide, you will be able to:

  • Write SQL expressions for dimensions and measures
  • Use lookup expressions for dimension fields
  • Configure primary key expressions
  • Work with array expressions

Expression Types​

SQL Expressions​

SQL expressions are the most common type. They define how to query the field from the database.

For Dimensions:

- type: dimension
  name: Customer Name
  data_type: string
  expression:
    sql: customer_name

For Measures:

- type: measure
  name: Total Revenue
  data_type: decimal
  expression:
    sql: sum(amount)

Complex SQL:

- type: dimension
  name: Full Name
  data_type: string
  expression:
    sql: CONCAT(first_name, ' ', last_name)

With CASE statements:

- type: dimension
  name: Order Status
  data_type: string
  expression:
    sql: |
      CASE
        WHEN status = 'P' THEN 'Pending'
        WHEN status = 'C' THEN 'Completed'
        WHEN status = 'X' THEN 'Cancelled'
        ELSE 'Unknown'
      END

Shorthand​

For simple SQL only, you can use a string instead of a nested mapping (same as expression: { sql: ... }):

- type: dimension
  name: Order ID
  data_type: integer
  expression: order_id

- type: measure
  name: Total Revenue
  data_type: decimal
  expression: sum(amount)

Use the mapping form when you need lookup, primary_key, or array.

Primary Keys​

Mark a dimension as a primary key. This helps with query optimization and ensures uniqueness.

- type: dimension
  name: Order ID
  data_type: integer
  expression:
    primary_key: true
    sql: order_id

When to use:

  • Unique identifiers (IDs, codes)
  • One row per entity
  • Helps planner optimize joins

Lookup Expressions​

Lookup expressions indicate that a dimension field is a lookup/dimension field. This is optional but can help with query planning.

- type: dimension
  name: Product Category
  data_type: string
  expression:
    lookup: true
    sql: category_name

When to use:

  • Dimension fields (not measures)
  • Fields used for grouping/filtering
  • Optional - SQL expressions work without it

Array Expressions​

Array expressions indicate that a field contains array values.

- type: dimension
  name: Tags
  data_type: string
  expression:
    array: true
    sql: tag_array

When to use:

  • Database columns that are arrays
  • JSON array fields
  • Multi-value dimensions

Measure Expressions​

Measures must include aggregation functions:

Sum:

- type: measure
  name: Total Revenue
  data_type: decimal
  expression:
    sql: sum(amount)

Average:

- type: measure
  name: Average Order Value
  data_type: decimal
  expression:
    sql: avg(amount)

Count:

- type: measure
  name: Order Count
  data_type: integer
  expression:
    sql: count(*)

Count Distinct:

- type: measure
  name: Unique Customers
  data_type: integer
  expression:
    sql: count(distinct customer_id)

Min/Max:

- type: measure
  name: First Order Date
  data_type: date
  expression:
    sql: min(order_date)

- type: measure
  name: Last Order Date
  data_type: date
  expression:
    sql: max(order_date)

Expression Best Practices​

  1. Use column names directly for simple fields
  2. Include aggregation for all measures (sum, avg, count, etc.)
  3. Mark primary keys for unique identifiers
  4. Use SQL functions for transformations (CONCAT, CASE, etc.)
  5. Keep expressions readable - complex logic should be in views or calculated columns

Common Patterns​

Concatenation​

- type: dimension
  name: Full Address
  data_type: string
  expression:
    sql: CONCAT(street, ', ', city, ', ', state, ' ', zip)

Date Formatting​

- type: dimension
  name: Order Month
  data_type: string
  expression:
    sql: TO_CHAR(order_date, 'YYYY-MM')

Null Handling​

- type: dimension
  name: Customer Name
  data_type: string
  expression:
    sql: COALESCE(customer_name, 'Unknown')

Conditional Logic​

- type: dimension
  name: Customer Segment
  data_type: string
  expression:
    sql: |
      CASE
        WHEN total_orders > 100 THEN 'VIP'
        WHEN total_orders > 50 THEN 'Regular'
        ELSE 'New'
      END

Next Steps​