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Calculating filtered aggregates

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Use case

Sometimes, there’s a need to calculate an aggregation over facts in a joined cube that is filtered by a dimension from another cube. For example, you might want to calculate the total sales for a retailer. Each retailer has multiple stores, and each store does its own sales. If you set sales goals at the retailer level and are only interested in the sales that happened after a certain date, you would need to calculate the total sales in a way that only includes sales that happened after that date.

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Data modeling

We can model this scenario by creating a cube for each entity: retailer, store, and sales. The retailer cube has a one-to-many relationship with the store cube, and the store cube has a one-to-many relationship with the sales cube: 
cubes:
  - name: retailer
    sql: |
      SELECT 101 AS id, 'Retailer 1' AS name, 10 AS sales_goal, '2025-02-01Z'::TIMESTAMP AS goal_start UNION ALL
      SELECT 102 AS id, 'Retailer 2' AS name, 10 AS sales_goal, '2025-02-01Z'::TIMESTAMP AS goal_start UNION ALL
      SELECT 103 AS id, 'Retailer 3' AS name, 10 AS sales_goal, '2025-02-01Z'::TIMESTAMP AS goal_start

    joins:
      - name: store
        sql: '{CUBE.id} = {store.retailer_id}'
        relationship: one_to_many

    dimensions:
      - name: id
        sql: "{CUBE}.id"
        type: number
        primary_key: true
      
      - name: name
        sql: "{CUBE}.name"
        type: string
      
      - name: goal_start
        sql: "{CUBE}.goal_start"
        type: time

      - name: sales
        sql: "{store.total_sales}"
        type: number
        sub_query: true

      - name: sales_for_goal
        sql: "{store.total_sales_for_goal}"
        type: number
        sub_query: true
    
    measures:
      - name: sales_goal
        sql: "{CUBE}.sales_goal"
        type: sum

      - name: sales_goal_achieved
        type: number
        sql: "({CUBE.sales_for_goal} / NULLIF({CUBE.sales_goal}, 0))"

  - name: store
    sql: |
      SELECT 201 AS id, 'Store 1' AS name, 101 AS retailer_id UNION ALL
      SELECT 202 AS id, 'Store 2' AS name, 101 AS retailer_id UNION ALL
      SELECT 203 AS id, 'Store 3' AS name, 101 AS retailer_id UNION ALL
      SELECT 204 AS id, 'Store 4' AS name, 102 AS retailer_id UNION ALL
      SELECT 205 AS id, 'Store 5' AS name, 102 AS retailer_id UNION ALL
      SELECT 206 AS id, 'Store 6' AS name, 102 AS retailer_id UNION ALL
      SELECT 207 AS id, 'Store 7' AS name, 103 AS retailer_id UNION ALL
      SELECT 208 AS id, 'Store 8' AS name, 103 AS retailer_id UNION ALL
      SELECT 209 AS id, 'Store 9' AS name, 103 AS retailer_id

    joins:
      - name: sales
        sql: '{CUBE.id} = {sales.store_id}'
        relationship: one_to_many

    dimensions:
      - name: id
        sql: "{CUBE}.id"
        type: number
        primary_key: true
      
      - name: name
        sql: "{CUBE}.name"
        type: string
      
      - name: retailer_id
        sql: "{CUBE}.retailer_id"
        type: number

      - name: goal_start
        sql: "{retailer.goal_start}"
        type: time

      - name: sales
        sql: "{sales.total_sales}"
        type: number
        sub_query: true

      - name: sales_for_goal
        sql: "{sales.total_sales_for_goal}"
        type: number
        sub_query: true

    measures:
      - name: total_sales
        sql: "{CUBE.sales}"
        type: sum

      - name: total_sales_for_goal
        sql: "{CUBE.sales_for_goal}"
        type: sum

  - name: sales
    sql: |
      SELECT 301 AS id, 201 AS store_id, '2025-01-01Z'::TIMESTAMP AS order_date, 1 AS sales UNION ALL
      SELECT 302 AS id, 202 AS store_id, '2025-01-01Z'::TIMESTAMP AS order_date, 1 AS sales UNION ALL
      SELECT 303 AS id, 203 AS store_id, '2025-01-01Z'::TIMESTAMP AS order_date, 1 AS sales UNION ALL
      SELECT 304 AS id, 204 AS store_id, '2025-01-01Z'::TIMESTAMP AS order_date, 3 AS sales UNION ALL
      SELECT 305 AS id, 205 AS store_id, '2025-01-01Z'::TIMESTAMP AS order_date, 3 AS sales UNION ALL
      SELECT 306 AS id, 206 AS store_id, '2025-01-01Z'::TIMESTAMP AS order_date, 3 AS sales UNION ALL
      SELECT 307 AS id, 207 AS store_id, '2025-01-01Z'::TIMESTAMP AS order_date, 5 AS sales UNION ALL
      SELECT 308 AS id, 208 AS store_id, '2025-01-01Z'::TIMESTAMP AS order_date, 5 AS sales UNION ALL
      SELECT 309 AS id, 209 AS store_id, '2025-01-01Z'::TIMESTAMP AS order_date, 5 AS sales UNION ALL
      SELECT 310 AS id, 201 AS store_id, '2025-02-01Z'::TIMESTAMP AS order_date, 1 AS sales UNION ALL
      SELECT 311 AS id, 202 AS store_id, '2025-02-01Z'::TIMESTAMP AS order_date, 1 AS sales UNION ALL
      SELECT 312 AS id, 203 AS store_id, '2025-02-01Z'::TIMESTAMP AS order_date, 1 AS sales UNION ALL
      SELECT 313 AS id, 204 AS store_id, '2025-02-01Z'::TIMESTAMP AS order_date, 3 AS sales UNION ALL
      SELECT 314 AS id, 205 AS store_id, '2025-02-01Z'::TIMESTAMP AS order_date, 3 AS sales UNION ALL
      SELECT 315 AS id, 206 AS store_id, '2025-02-01Z'::TIMESTAMP AS order_date, 3 AS sales UNION ALL
      SELECT 316 AS id, 207 AS store_id, '2025-02-01Z'::TIMESTAMP AS order_date, 5 AS sales UNION ALL
      SELECT 317 AS id, 208 AS store_id, '2025-02-01Z'::TIMESTAMP AS order_date, 5 AS sales UNION ALL
      SELECT 318 AS id, 209 AS store_id, '2025-02-01Z'::TIMESTAMP AS order_date, 5 AS sales

    dimensions:
      - name: id
        sql: "{CUBE}.id"
        type: number
        primary_key: true 

      - name: store_id
        sql: "{CUBE}.store_id"
        type: number
      
      - name: order_date
        sql: "{CUBE}.order_date"
        type: time

      - name: goal_start
        sql: "{store.goal_start}"
        type: time
      
      - name: sales
        sql: "{CUBE}.sales"
        type: number
      
    measures:
      - name: total_sales
        sql: "{CUBE.sales}"
        type: sum

      - name: total_sales_for_goal
        sql: "{CUBE.sales}"
        type: sum
        filters:
          - sql: "{CUBE.order_date} >= {CUBE.goal_start}"
The total sales for a store and total sales for a retailer are calculated via subquery dimensions. If you look at the join path (retailer.store.sales), you would see the upstream flow of data: the aggregation over sales is passed from the sales cube to the store cube and then to the retailer cube. At the same time, the goal_start date is passed from the retailer cube to the store cube and then to the sales cube, creating the downstream flow of data. This way, the total_sales_for_goal measure in the sales cube can be filtered by the goal_start date. This pattern of passing measures (aggregates) upstream by the join path and passing dimensions downstream is an effective way to solve many data modeling tasks, including calculating filtered aggregates.

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Result

Querying the retailer cube will return the total sales for each retailer and the total sales for each retailer after the goal_start date. The sales_goal_achieved measure will show the ratio of the sales goal that has been achieved: