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lookml-fields

lookml-fields

github.com/lkrdevData & databases1656 installsUpdated 22w ago
Repository

About lookml-fields

lookml-fields is a reference skill explaining how LookML field types and their sql parameters shape generated SQL. It is intended for people authoring data models and distinguishes dimensions, measures, filters, parameters, and dimension groups.

Key features

  1. 01Explain five LookML field types
  2. 02Describe type-specific sql behavior
  3. 03Clarify aggregation and measure references
  4. 04Show templated filter usage
  5. 05Explain parameters and Liquid variables
  6. 06Cover timeframes in dimension groups

Use cases

  1. 01Choose a field type for a model definition
  2. 02Review SQL generated from a LookML field
  3. 03Build dynamic logic with parameters
  4. 04Define time-based dimensions

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/lkrdev/lookml_skills --skill lookml-fields

After installing, type/lookml-fieldsin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

references/
dimension_group.md
dimension.md
filter_parameter.md
measure.md
value_format.md

Details

From the project README

Instructions

1. Field Types Overview

LookML fields are the building blocks of your data model. Each type serves a specific purpose in generating SQL.

Field TypePurposeSQL Generation Phase
DimensionDescribes data (attributes). Groups results.SELECT and GROUP BY clause.
MeasureAggregates data (metrics). Calculates results.SELECT clause (with aggregation).
FilterRestricts data based on conditions.WHERE or HAVING clause (via templated filters).
ParameterCaptures user input for dynamic logic.None directly (injects values into other fields).
Dimension GroupGenerates a set of time-based dimensions.SELECT and GROUP BY clause (multiple columns).

2. The Role of sql Parameter

The sql parameter behaves differently strictly based on the field type.

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:
    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:
    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):
    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:
    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:
    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

Typesql references...Can reference Measures?Aggregated?
DimensionColumns, Other DimensionsNONo
Measure (Agg)Columns, DimensionsNOYes
Measure (Num)Other MeasuresYESYes (already agg)
Filter(Rarely used)NoN/A
Parameter(None)NoN/A
Value Format(None)NoN/A

Reference Skills

For detailed standards on specific field types, refer to:

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