Drizzle Cube Definition

This skill helps you create semantic layer cubes using Drizzle Cube's defineCube function. Cubes provide a business-friendly abstraction over database tables with type-safe dimensions, measures, and built-in security.

Core Concept

A cube in Drizzle Cube is:

• A semantic layer over one or more database tables
• Defined using Drizzle ORM table references
• Always filtered by security context (mandatory for multi-tenant isolation)
• Type-safe with full TypeScript support

Basic Cube Structure

import { defineCube } from 'drizzle-cube'
import { eq } from 'drizzle-orm'
import { employees } from './schema' // Your Drizzle schema

export const employeesCube = defineCube('Employees', {
  title: 'Employee Analytics', // Optional human-readable title
  description: 'Analytics for employee data', // Optional description

  // MANDATORY: Security context filtering for multi-tenant isolation
  sql: (ctx) => ({
    from: employees,
    where: eq(employees.organisationId, ctx.securityContext.organisationId)
  }),

  // Define dimensions (categorical/time fields for grouping/filtering)
  dimensions: {
    id: {
      title: 'Employee ID',
      type: 'number',
      sql: () => employees.id,
      primaryKey: true // Mark the primary key
    },
    name: {
      title: 'Employee Name',
      type: 'string',
      sql: () => employees.name
    },
    email: {
      title: 'Email Address',
      type: 'string',
      sql: () => employees.email
    },
    departmentId: {
      title: 'Department',
      type: 'number',
      sql: () => employees.departmentId
    },
    isActive: {
      title: 'Active Status',
      type: 'boolean',
      sql: () => employees.isActive
    },
    createdAt: {
      title: 'Created Date',
      type: 'time',
      sql: () => employees.createdAt
    }
  },

  // Define measures (aggregated numeric values)
  measures: {
    count: {
      title: 'Total Employees',
      type: 'count',
      sql: () => employees.id
    },
    totalSalary: {
      title: 'Total Salary',
      type: 'sum',
      sql: () => employees.salary
    },
    avgSalary: {
      title: 'Average Salary',
      type: 'avg',
      sql: () => employees.salary
    },
    minSalary: {
      title: 'Minimum Salary',
      type: 'min',
      sql: () => employees.salary
    },
    maxSalary: {
      title: 'Maximum Salary',
      type: 'max',
      sql: () => employees.salary
    }
  }
})

Important: The defineCube function takes two parameters:

1. name (string) - The cube name (e.g., 'Employees')
2. definition (object) - The cube configuration (sql, dimensions, measures, etc.)

Dimension Types

Drizzle Cube supports four dimension types:

1. String Dimensions

dimensions: {
  name: {
    title: 'Full Name',
    type: 'string',
    sql: () => employees.name
  },
  email: {
    type: 'string',
    sql: () => employees.email
  }
}

2. Number Dimensions

dimensions: {
  id: {
    type: 'number',
    sql: () => employees.id,
    primaryKey: true
  },
  departmentId: {
    type: 'number',
    sql: () => employees.departmentId
  }
}

3. Time Dimensions

dimensions: {
  createdAt: {
    title: 'Created Date',
    type: 'time',
    sql: () => employees.createdAt
  },
  updatedAt: {
    type: 'time',
    sql: () => employees.updatedAt
  }
}

4. Boolean Dimensions

dimensions: {
  isActive: {
    title: 'Active',
    type: 'boolean',
    sql: () => employees.isActive
  },
  isRemote: {
    title: 'Remote Worker',
    type: 'boolean',
    sql: () => employees.isRemote
  }
}

Measure Types

Drizzle Cube supports several aggregation types:

1. Count Measures

measures: {
  count: {
    title: 'Total Count',
    type: 'count',
    sql: () => employees.id // Column to count
  },
  activeCount: {
    title: 'Active Employees',
    type: 'count',
    sql: () => employees.id,
    filters: [(ctx) => eq(employees.isActive, true)] // Filtered count
  }
}

2. Count Distinct Measures

measures: {
  uniqueDepartments: {
    title: 'Unique Departments',
    type: 'countDistinct',
    sql: () => employees.departmentId
  }
}

3. Sum Measures

measures: {
  totalSalary: {
    title: 'Total Salary',
    type: 'sum',
    sql: () => employees.salary
  }
}

4. Average Measures

measures: {
  avgSalary: {
    title: 'Average Salary',
    type: 'avg',
    sql: () => employees.salary
  }
}

5. Min/Max Measures

measures: {
  minSalary: {
    title: 'Minimum Salary',
    type: 'min',
    sql: () => employees.salary
  },
  maxSalary: {
    title: 'Maximum Salary',
    type: 'max',
    sql: () => employees.salary
  }
}

6. Calculated Measures

measures: {
  salaryPercentage: {
    title: 'Salary as Percentage',
    type: 'calculated',
    calculatedSql: '{totalSalary} / NULLIF({departmentBudget}, 0) * 100'
  }
}

SQL Property Patterns

The sql property in dimensions and measures can be defined in two ways:

1. Direct Column Reference (Recommended)

dimensions: {
  name: {
    type: 'string',
    sql: () => employees.name  // Function returning column
  }
}

2. Direct Column (Also Valid)

dimensions: {
  name: {
    type: 'string',
    sql: employees.name  // Direct column reference
  }
}

Best Practice: Use the function form () => employees.column for consistency and to access the QueryContext if needed.

Advanced Patterns

Filtered Measures

Add filters to measures for conditional aggregation:

measures: {
  activeEmployees: {
    title: 'Active Employees',
    type: 'count',
    sql: () => employees.id,
    filters: [
      (ctx) => eq(employees.isActive, true)
    ]
  },
  seniorEmployees: {
    title: 'Senior Employees',
    type: 'count',
    sql: () => employees.id,
    filters: [
      (ctx) => {
        const { gte } = ctx.imports
        return gte(employees.yearsOfService, 5)
      }
    ]
  },
  highEarners: {
    title: 'High Earners',
    type: 'count',
    sql: () => employees.id,
    filters: [
      (ctx) => {
        const { gt } = ctx.imports
        return gt(employees.salary, 100000)
      }
    ]
  }
}

Computed Dimensions

Use SQL expressions for computed values:

import { sql } from 'drizzle-orm'

dimensions: {
  fullName: {
    title: 'Full Name',
    type: 'string',
    sql: () => sql`${employees.firstName} || ' ' || ${employees.lastName}`
  },
  seniorityLevel: {
    title: 'Seniority',
    type: 'string',
    sql: () => sql`CASE
      WHEN ${employees.yearsOfService} < 2 THEN 'Junior'
      WHEN ${employees.yearsOfService} < 5 THEN 'Mid-level'
      ELSE 'Senior'
    END`
  }
}

Security Context (MANDATORY)

Every cube MUST filter by security context to ensure multi-tenant data isolation:

// ✅ CORRECT - Security context filtering
sql: (ctx) => ({
  from: employees,
  where: eq(employees.organisationId, ctx.securityContext.organisationId)
})

// ✅ CORRECT - Multiple security conditions
sql: (ctx) => ({
  from: employees,
  where: and(
    eq(employees.organisationId, ctx.securityContext.organisationId),
    eq(employees.tenantId, ctx.securityContext.tenantId)
  )
})

// ❌ WRONG - No security filtering (data leak!)
sql: (ctx) => ({
  from: employees
  // Missing where clause - SECURITY VIOLATION
})

Note: The sql function receives a QueryContext object (abbreviated as ctx), which contains:

• ctx.securityContext - The security context with tenant/organization information
• ctx.imports - Drizzle ORM operators and functions

Complete Example

import { defineCube } from 'drizzle-cube'
import { eq, sql, and, gte } from 'drizzle-orm'
import { employees } from './schema'

export const employeesCube = defineCube('Employees', {
  title: 'Employee Analytics',
  description: 'Comprehensive employee data and metrics',

  // Security context filtering (MANDATORY)
  sql: (ctx) => ({
    from: employees,
    where: eq(employees.organisationId, ctx.securityContext.organisationId)
  }),

  dimensions: {
    id: {
      title: 'Employee ID',
      type: 'number',
      sql: () => employees.id,
      primaryKey: true
    },
    name: {
      title: 'Name',
      type: 'string',
      sql: () => employees.name
    },
    email: {
      title: 'Email',
      type: 'string',
      sql: () => employees.email
    },
    department: {
      title: 'Department',
      type: 'string',
      sql: () => employees.departmentName
    },
    isActive: {
      title: 'Active',
      type: 'boolean',
      sql: () => employees.isActive
    },
    createdAt: {
      title: 'Hire Date',
      type: 'time',
      sql: () => employees.createdAt
    },
    // Computed dimension
    seniorityLevel: {
      title: 'Seniority Level',
      type: 'string',
      sql: () => sql`CASE
        WHEN ${employees.yearsOfService} < 2 THEN 'Junior'
        WHEN ${employees.yearsOfService} < 5 THEN 'Mid-level'
        ELSE 'Senior'
      END`
    }
  },

  measures: {
    count: {
      title: 'Total Employees',
      type: 'count',
      sql: () => employees.id
    },
    activeCount: {
      title: 'Active Employees',
      type: 'count',
      sql: () => employees.id,
      filters: [(ctx) => eq(employees.isActive, true)]
    },
    totalSalary: {
      title: 'Total Salary',
      type: 'sum',
      sql: () => employees.salary
    },
    avgSalary: {
      title: 'Average Salary',
      type: 'avg',
      sql: () => employees.salary
    },
    minSalary: {
      title: 'Minimum Salary',
      type: 'min',
      sql: () => employees.salary
    },
    maxSalary: {
      title: 'Maximum Salary',
      type: 'max',
      sql: () => employees.salary
    },
    uniqueDepartments: {
      title: 'Unique Departments',
      type: 'countDistinct',
      sql: () => employees.departmentId
    },
    // Filtered measure
    seniorEmployees: {
      title: 'Senior Employees',
      type: 'count',
      sql: () => employees.id,
      filters: [(ctx) => gte(employees.yearsOfService, 5)]
    }
  }
})

Registering Cubes

Once defined, register cubes with the semantic layer compiler:

import { SemanticLayerCompiler } from 'drizzle-cube'
import { drizzle } from 'drizzle-orm/postgres-js'
import { employeesCube } from './cubes/employees'

const db = drizzle(process.env.DATABASE_URL)

const compiler = new SemanticLayerCompiler({
  drizzle: db,
  schema: schema
})

// Register your cube
compiler.registerCube(employeesCube)

Best Practices

1. Always include security context filtering - This is mandatory for multi-tenant isolation
2. Use meaningful names - Cube names and dimension/measure keys should be clear and descriptive
3. Add titles - Provide human-readable titles for UI display
4. Mark primary keys - Set primaryKey: true on ID dimensions
5. Type safety - Use Drizzle ORM table references for compile-time validation
6. Filtered measures - Use filters for conditional aggregations instead of creating separate cubes
7. Use function form for sql - Prefer sql: () => column over direct sql: column for consistency

Common Pitfalls

• Wrong defineCube signature - Remember: name is first parameter, NOT inside the object

  // ❌ WRONG
  defineCube({ name: 'Employees', sql: ... })
  
  // ✅ CORRECT
  defineCube('Employees', { sql: ... })
  

• Missing security context - Every cube must filter by security context
• Wrong SQL syntax - Use Drizzle ORM operators (eq, and, or), not raw SQL strings
• Incorrect types - Ensure dimension/measure types match the actual data types
• Missing imports - Import necessary operators from drizzle-orm
• Redundant name fields - Don't add name: property to dimensions/measures (the key IS the name)

Next Steps

• Learn about cube joins with the cube-joins skill
• Build queries using your cubes with the queries skill
• Set up server APIs with the server-setup skill