Instructions

1. Field Types Overview

SELECT

GROUP BY

SELECT

WHERE

HAVING

SELECT

GROUP BY

2. The Role of

sql

Parameter

sql

Dimensions: The "What"

• Role: Defines the raw transformation of the column before any aggregation.
• SQL Context: The expression is placed directly into the

  GROUP BY

  clause.
• Input: Can reference table columns (

  ${TABLE}.col

  ), other dimensions (

  ${dim}

  ), or raw SQL functions.
• Example:
  lookml

  dimension: full_name {
    sql: CONCAT(${first_name}, ' ', ${last_name}) ;;
  }
  -- Generates: CONCAT(table.first_name, ' ', table.last_name)

Measures: The "How Much"

• Role: Defines the value to be aggregated or the calculation involving other aggregates.
• SQL Context: Puts the expression inside the aggregation function (e.g.,

  SUM(sql)

  ), or as a standalone calculation for

  type: number

  .
• Input:
  • For

    type: sum/avg/min/max

    : References dimensions or columns.
  • For

    type: number

    : References other measures.
  • For

    type: count

    :

    sql

    is ignored (always

    COUNT(*)

    or

    COUNT(primary_key)

    ).
• Example:
  lookml

  measure: total_profit {
    type: sum
    sql: ${sale_price} - ${cost} ;; 
  }
  -- Generates: SUM(sale_price - cost)

Filters: The "Which"

• Role: Defines the condition logic, usually for Templated Filters used in Derived Tables or

  sql_always_where

  .
• SQL Context: The

  sql

  parameter in a

  filter

  field is rarely used directly in modern LookML. Instead, the input to the filter is used in

  {% condition %}

  tags.
• Best Practice: Identify if you need a

  filter

  field or just a

  parameter

  +

  dimension

  .
• Example (Templated Filter):
  lookml

  filter: date_filter { type: date }
  -- Usage in Derived Table SQL:
  -- WHERE {% condition date_filter %} created_at {% endcondition %}

Parameters: The "User Input"

• Role: Does NOT generate SQL itself. It holds a user-selected value to be injected into other fields.
• SQL Context: Accessed via Liquid variables (

  {% parameter name %}

  ) inside Dimensions, Measures, or Derived Tables.
• Input: User selects from a UI list or types a value.
• Example:
  lookml

  parameter: timeframe_selector {
    type: unquoted
    allowed_value: { value: "month" }
    allowed_value: { value: "year" }
  }
  dimension: dynamic_date {
    sql: DATE_TRUNC({% parameter timeframe_selector %}, ${created_raw}) ;;
  }

Dimension Groups: The "Time Generator"

• Role: Defines the source timestamp or date column. Looker then generates multiple dimension fields based on the

  timeframes

  list.
• SQL Context: Casts and truncates the source column for each timeframe.
• Input: Must be a standardized timestamp or date expression.
• Example:
  lookml

  dimension_group: created {
    type: time
    timeframes: [date, month]
    sql: ${TABLE}.created_at ;;
  }
  -- Generates:
  -- created_date -> CAST(table.created_at AS DATE)
  -- created_month -> DATE_TRUNC(table.created_at, MONTH)

3. Summary of Differences

sql

Reference Skills

• Dimensions: Naming, labels, and type-specific rules.
• Measures: Aggregation types, filters, and formats.
• Filters & Parameters: Templated filters and user input.
• Dimension Groups: Timeframes and intervals.
• Value Formats: Named and custom currency/number formats.