Data blending

When time is the only practical link between cubes, explains the union-style blending pattern to combine metrics without a traditional multi-cube join.

In case you want to plot two measures from different cubes on a single chart, or create a calculated measure based on it, you need to create a join between these two cubes. If there’s no way to join two cubes other than by time dimension, you can consider using the data blending approach.

Data blending is a pattern that allows creating a cube based on two or more existing cubes, and contains a union of the underlying cubes’ date to query it together.

Data blending could be faster than joining on date when the record count is very large, because with this pattern, aggregation happens before joining, which can be more efficient for large volumes of data. The other situation in which data blending could be a better approach than joining on date is when the two tables have mostly the same columns, such as in the example below.

For an example, consider an omnichannel store which has both online and offline sales. Let’s calculate summary metrics for revenue, customer count, etc. We have a retail_orders cube for offline sales:

YAML

cubes:
  - name: retail_orders
    sql_table: retail_orders

    measures:
      - name: customer_count
        sql: customer_id
        type: count_distinct

      - name: revenue
        sql: amount
        type: sum

    dimensions:
      - name: created_at
        sql: created_at
        type: time

An online_orders cube for online sales:

YAML

cubes:
  - name: online_orders
    sql_table: online_orders

    measures:
      - name: customer_count
        sql: user_id
        type: count_distinct

      - name: revenue
        sql: amount
        type: sum

    dimensions:
      - name: created_at
        sql: created_at
        type: time

Given the above cubes, a data blending cube can be introduced as follows:

YAML

cubes:
  - name: all_sales
    sql: |
      SELECT
        amount,
        user_id AS customer_id,
        created_at,
        'online' AS row_type
      FROM {online_orders.sql()} AS online
      UNION ALL
      SELECT
        amount,
        customer_id,
        created_at,
        'retail' AS row_type
      FROM {retail_orders.sql()} AS retail

    measures:
      - name: customer_count
        sql: customer_id
        type: count_distinct

      - name: revenue
        sql: amount
        type: sum

      - name: online_revenue
        sql: amount
        type: sum
        filters:
          - sql: "{CUBE}.row_type = 'online'"

      - name: offline_revenue
        sql: amount
        type: sum
        filters:
          - sql: "{CUBE}.row_type = 'retail'"

      - name: online_revenue_percentage
        sql: |
          {online_revenue} /
          NULLIF({online_revenue} + {offline_revenue}, 0)
        type: number
        format: percent

    dimensions:
      - name: created_at
        sql: created_at
        type: time

      - name: revenue_type
        sql: row_type
        type: string

Another use case of the Data Blending approach would be when you want to chart some measures (business related) together and see how they correlate.

Provided we have the aforementioned tables online_orders and retail_orders let’s assume that we want to chart those measures together and see how they correlate. You can simply pass the queries to the Cube client, and it will merge the results which will let you easily display it on the chart.

import cube from "@cubejs-client/core"

const API_URL = "http://localhost:4000"
const CUBE_TOKEN = "YOUR_TOKEN"

const cubeApi = cube(CUBE_TOKEN, {
  apiUrl: `${API_URL}/cubejs-api/v1`
})

const queries = [
  {
    measures: ["online_orders.revenue"],
    timeDimensions: [
      {
        dimension: "online_orders.created_at",
        granularity: "day",
        dateRange: ["2020-08-01", "2020-08-07"]
      }
    ]
  },
  {
    measures: ["retail_orders.revenue"],
    timeDimensions: [
      {
        dimension: "retail_orders.created_at",
        granularity: "day",
        dateRange: ["2020-08-01", "2020-08-07"]
      }
    ]
  }
]

const resultSet = await cubeApi.load(queries)