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Measures

You can use the measures parameter within cubes to define measures. Each measure is an aggregation over a certain column in your database table. Any measure should have the following parameters: name, sql, and type.

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Parameters

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name

The name parameter serves as the identifier of a measure. It must be unique among all measures, dimensions, and segments within a cube and follow the naming conventions. 
cubes:
  - name: orders
    # ...

    measures:
      - name: count
        sql: id
        type: count

      - name: total_amount
        sql: amount
        type: sum

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title

You can use the title parameter to change a measure’s displayed name. By default, Cube will humanize your measure key to create a display name. In order to override default behavior, please use the title parameter. 
cubes:
  - name: orders
    # ...

    measures:
      - name: orders_count
        title: Number of Orders Placed
        sql: id
        type: count

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description

This parameter provides a human-readable description of a measure. When applicable, it will be displayed in Playground and exposed to data consumers via APIs and integrations. 
cubes:
  - name: orders
    # ...

    measures:
      - name: orders_count
        description: Count of all orders
        sql: id
        type: count

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public

The public parameter is used to manage the visibility of a measure. Valid values for public are true and false. When set to false, this measure cannot be queried through the API. Defaults to true. 
cubes:
  - name: orders
    # ...

    measures:
      - name: orders_count
        sql: id
        type: count
        public: false

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meta

Custom metadata. Can be used to pass any information to the frontend. 
cubes:
  - name: orders
    # ...

    measures:
      - name: revenue
        type: sum
        sql: price
        meta:
          any: value

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sql

sql is a required parameter. It can take any valid SQL expression depending on the type of the measure. Please refer to the Measure Types Guide for detailed information on the corresponding sql parameter. 
cubes:
  - name: orders
    # ...

    measures:
      - name: users_count
        sql: "COUNT(*)"
        type: number
Depending on the measure type, the sql parameter would either:
  • Be skipped (in case of the count type).
  • Contain an aggregate function, e.g., STRING_AGG(string_dimension, ',') (in case of string, time, boolean, and number types).
  • Contain a non-aggregated expression that Cube would wrap into an aggregate function according to the measure type (in case of the avg, count_distinct, count_distinct_approx, min, max, and sum types).

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mask

The optional mask parameter defines the replacement value used when the measure is masked by a data masking access policy. The mask can be a static value (number, boolean, or string) or a SQL expression. When using a SQL expression, it should be an aggregate expression (the same way as the measure’s sql parameter for number type measures), because the mask replaces the entire measure expression including aggregation: 
cubes:
  - name: orders
    # ...

    measures:
      - name: count
        type: count
        mask: 0

      - name: total_revenue
        sql: revenue
        type: sum
        mask: -1

      - name: avg_revenue
        sql: revenue
        type: avg
        mask:
          sql: "AVG(CASE WHEN {CUBE}.is_public THEN {CUBE}.revenue END)"
If no mask is defined, the default mask value is NULL. See data masking for more details.
SQL masks on measures are not applied in ungrouped queries (e.g., SELECT * via the SQL API). If you need dynamic masking in ungrouped mode, use a masked dimension instead.

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filters

If you want to add some conditions for a metric’s calculation, you should use the filters parameter. The syntax looks like the following: 
cubes:
  - name: orders
    # ...

    measures:
      - name: orders_completed_count
        sql: id
        type: count
        filters:
          - sql: "{CUBE}.status = 'completed'"

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type

type is a required parameter. There are various types that can be assigned to a measure. Please refer to the Measure Types for the full list of measure types. 
cubes:
  - name: orders
    # ...

    measures:
      - name: orders_count
        sql: id
        type: count

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rolling_window

The rolling_window parameter is used to for rolling window calculations, e.g., to calculate a metric over a moving window of time, e.g. a week or a month.
Rolling window calculations require the query to contain a single time dimension with a provided date range. It is used to calculate the minimum and maximum values for the series of time windows.With Tesseract, the next-generation data modeling engine, rolling window calculations don’t require the date range for the time dimension. Tesseract is currently in preview. Use the CUBEJS_TESSERACT_SQL_PLANNER environment variable to enable it.

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offset

The offset parameter is used to specify the starting point of the time window. You can set the window offset parameter to either start or end, which will match the start or end of the window. By default, the offset parameter is set to end.

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trailing and leading

The trailing and leading parameters define the size of the time window. The trailing parameter defines the size of the window part before the offset point, and the leading parameter defines the size of the window part after the offset point. These parameters have a format defined as (-?\d+) (minute|hour|day|week|month|year). It means that you can define these parameters using both positive and negative integers. The trailing and leading parameters can also be set to unbounded, which means infinite size for the corresponding window part. By default, the leading and trailing parameters are set to zero. 
cubes:
  - name: orders
    # ...

    measures:
      - name: rolling_count_month
        sql: id
        type: count
        rolling_window:
          trailing: 1 month
Here’s an example of an unbounded window that’s used for cumulative counts: 
cubes:
  - name: orders
    # ...

    measures:
      - name: cumulative_count
        type: count
        rolling_window:
          trailing: unbounded

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multi_stage

The multi_stage parameter is used to define measures that are used with multi-stage calculations, e.g., time-shift measures. 
cubes:
  - name: time_shift
    sql: >
      SELECT '2024-01-01'::TIMESTAMP AS time, 100 AS revenue UNION ALL
      SELECT '2024-02-01'::TIMESTAMP AS time, 200 AS revenue UNION ALL
      SELECT '2024-03-01'::TIMESTAMP AS time, 300 AS revenue UNION ALL

      SELECT '2025-01-01'::TIMESTAMP AS time, 400 AS revenue UNION ALL
      SELECT '2025-02-01'::TIMESTAMP AS time, 500 AS revenue UNION ALL
      SELECT '2025-03-01'::TIMESTAMP AS time, 600 AS revenue

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

    measures:
      - name: revenue
        sql: revenue
        type: sum

      - name: revenue_prior_year
        multi_stage: true
        sql: "{revenue}"
        type: number
        time_shift:
          - time_dimension: time
            interval: 1 year
            type: prior

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group_by

The group_by parameter is used with multi-stage measures to specify dimensions that should be used for the GROUP BY of the inner aggregation stage, ignoring any dimensions present in the query. This is commonly used for fixed dimension calculations — computing a measure at a fixed granularity regardless of the query’s dimensions. For example, calculating percent of total or comparing individual items to a broader dataset. 
measures:
  - name: country_revenue
    multi_stage: true
    sql: "{revenue}"
    type: sum
    group_by:
      - country
group_by accepts a list of dimension names from the same cube. The inner stage will group by only these dimensions, while the outer aggregation will group by the query’s dimensions.
ParameterInner GROUP BYOuter GROUP BY
group_byOnly the listed dimensionsQuery dimensions
reduce_byQuery dimensions minus listedQuery dimensions
add_group_byQuery dimensions plus listedQuery dimensions

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reduce_by

The reduce_by parameter is used with multi-stage measures to specify dimensions that should be removed from the GROUP BY of the inner aggregation stage. This is commonly used for ranking calculations — computing a rank across a dimension while still allowing grouping by other dimensions in the query. 
measures:
  - name: product_rank
    multi_stage: true
    order_by:
      - sql: "{revenue}"
        dir: asc
    reduce_by:
      - product
    type: rank
reduce_by accepts a list of dimension names. The inner stage will group by the query’s dimensions minus the listed dimensions, while the outer aggregation will group by the query’s dimensions.

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add_group_by

The add_group_by parameter is used with multi-stage measures to specify dimensions that should be added to the GROUP BY of the inner aggregation stage, in addition to any dimensions present in the query. This is commonly used for nested aggregate patterns — computing an aggregate of an aggregate. For example, averaging per-user metrics or counting how many groups exceed a threshold. 
measures:
  - name: avg_user_score
    multi_stage: true
    sql: "{avg_score}"
    type: avg
    add_group_by:
      - user_id
add_group_by accepts a list of dimension names from the same cube. The listed dimensions will be included in the inner stage’s GROUP BY but will not appear in the outer aggregation — they are used only to define the granularity at which the base measure is computed before the outer aggregation is applied.

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time_shift

The time_shift parameter is used to configure a time shift for a measure. It accepts an array of time shift configurations that consist of time_dimension, type, interval, and name parameters.

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type and interval

These parameters define the time shift direction and size. The type can be either prior (shifting time backwards) or next (shifting time forwards). The interval parameter defines the size of the time shift and has the following format: quantity unit, e.g., 1 year or 7 days. 
    measures:
      - name: revenue
        sql: revenue
        type: sum

      - name: revenue_7d_ago
        multi_stage: true
        sql: "{revenue}"
        type: number
        time_shift:
          - interval: 7 days
            type: prior

      - name: revenue_1y_ago
        multi_stage: true
        sql: "{revenue}"
        type: number
        time_shift:
          - interval: 1 year
            type: prior

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time_dimension

The time_dimension parameter is used to specify the time dimension for the time shift. If it’s omitted, Cube will apply the time shift to all time dimensions in the query. In this case, only single time shift configuration is allowed in time_shift. If time_dimension is specified, the time shift will only happen if the query contains this very time dimension. This is useful if you’d like to apply different time shifts to different time dimensions or if you want to apply a time shift only when a specific time dimension is present in the query. 
    measures:
      - name: revenue
        sql: revenue
        type: sum

      - name: lagging_revenue
        multi_stage: true
        sql: "{revenue}"
        type: number
        time_shift:
          - time_dimension: purchase_date
            interval: 3 months
            type: prior

          - time_dimension: shipping_date
            interval: 2 months
            type: prior

          - time_dimension: delivery_date
            interval: 1 month
            type: prior

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name

The name parameter is used to reference a named time shift that is defined on a time dimension from a calendar cube. Named time shifts are used in cases when different measures use the same time shift configuration (e.g., prior + 1 year) but have to be shifted differently depending on the custom calendar. 
cubes:
  - name: sales_calendar
    calendar: true
    sql: >
      SELECT '2025-06-02Z' AS date, '2024-06-01Z' AS mapped_date, '2024-06-03Z' AS mapped_date_alt UNION ALL
      SELECT '2025-06-03Z' AS date, '2024-06-02Z' AS mapped_date, '2024-06-04Z' AS mapped_date_alt UNION ALL
      SELECT '2025-06-04Z' AS date, '2024-06-03Z' AS mapped_date, '2024-06-05Z' AS mapped_date_alt UNION ALL
      SELECT '2025-06-05Z' AS date, '2024-06-04Z' AS mapped_date, '2024-06-06Z' AS mapped_date_alt UNION ALL
      SELECT '2025-06-06Z' AS date, '2024-06-05Z' AS mapped_date, '2024-06-07Z' AS mapped_date_alt UNION ALL
      SELECT '2025-06-07Z' AS date, '2024-06-06Z' AS mapped_date, '2024-06-08Z' AS mapped_date_alt UNION ALL
      SELECT '2025-06-08Z' AS date, '2024-06-07Z' AS mapped_date, '2024-06-09Z' AS mapped_date_alt

    dimensions:
      - name: date_key
        sql: "{CUBE}.date::TIMESTAMP"
        type: time
        primary_key: true

      - name: date
        sql: "{CUBE}.date::TIMESTAMP"
        type: time
        time_shift:
          - name: 1_year_prior
            sql: "{CUBE}.mapped_date::TIMESTAMP"

          - name: 1_year_prior_alternative
            sql: "{CUBE}.mapped_date_alt::TIMESTAMP"

  - name: sales
    sql: >
      SELECT 101 AS id, '2024-06-01Z' AS date, 101 AS amount UNION ALL
      SELECT 102 AS id, '2024-06-02Z' AS date, 102 AS amount UNION ALL
      SELECT 103 AS id, '2024-06-03Z' AS date, 103 AS amount UNION ALL
      SELECT 104 AS id, '2024-06-04Z' AS date, 104 AS amount UNION ALL
      SELECT 105 AS id, '2024-06-05Z' AS date, 105 AS amount UNION ALL
      SELECT 106 AS id, '2024-06-06Z' AS date, 106 AS amount UNION ALL
      SELECT 107 AS id, '2024-06-07Z' AS date, 107 AS amount UNION ALL
      SELECT 108 AS id, '2024-06-08Z' AS date, 108 AS amount UNION ALL
      SELECT 109 AS id, '2024-06-09Z' AS date, 109 AS amount UNION ALL

      SELECT 202 AS id, '2025-06-02Z' AS date, 202 AS amount UNION ALL
      SELECT 203 AS id, '2025-06-03Z' AS date, 203 AS amount UNION ALL
      SELECT 204 AS id, '2025-06-04Z' AS date, 204 AS amount UNION ALL
      SELECT 205 AS id, '2025-06-05Z' AS date, 205 AS amount UNION ALL
      SELECT 206 AS id, '2025-06-06Z' AS date, 206 AS amount UNION ALL
      SELECT 207 AS id, '2025-06-07Z' AS date, 207 AS amount UNION ALL
      SELECT 208 AS id, '2025-06-08Z' AS date, 208 AS amount

    joins:
      - name: sales_calendar
        sql: "{sales.date} = {sales_calendar.date_key}"
        relationship: many_to_one

    dimensions:
      - name: id
        sql: id
        type: number
        primary_key: true

      - name: date
        sql: "{CUBE}.date::TIMESTAMP"
        type: time
        public: false

    measures:
      - name: total_amount
        sql: amount
        type: sum

      - name: total_amount_1y_prior
        multi_stage: true
        sql: "{total_amount}"
        type: number
        time_shift:
          - name: 1_year_prior

      - name: total_amount_1y_prior_alternative
        multi_stage: true
        sql: "{total_amount}"
        type: number
        time_shift:
          - name: 1_year_prior_alternative
Named time shifts also allow to reuse the same time shift configuration across multiple measures and cubes where they are defined.

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case

The case parameter is used to define conditional measures, i.e., measures that are calculated based on the value of a switch dimension.
case measures are powered by Tesseract, the next-generation data modeling engine. Tesseract is currently in preview. Use the CUBEJS_TESSERACT_SQL_PLANNER environment variable to enable it.
You do not need to include the sql parameter if the case parameter is used. However, the multi_stage parameter must be set to true for case measures. 
cubes:
  - name: orders
    # ...

    dimensions:
      - name: currency
        type: switch
        values:
          - USD
          - EUR
          - GBP
    
    measures:
      - name: amount_usd
        sql: amount_usd
        type: sum

      - name: amount_eur
        sql: amount_eur
        type: sum

      - name: amount_gbp
        sql: amount_gbp
        type: sum

      - name: amount_in_currency
        multi_stage: true
        case:
          switch: "{CUBE.currency}"
          when:
            - value: EUR
              sql: "{CUBE.amount_eur}"
            - value: GBP
              sql: "{CUBE.amount_gbp}"
          else:
            sql: "{CUBE.amount_usd}"
        type: number

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format

format is an optional parameter. It is used to format the output of measures in different ways, for example, as currency for revenue. Please refer to the Measure Formats for the full list of supported formats. 
cubes:
  - name: orders
    # ...

    measures:
      - name: total
        sql: amount
        type: sum
        format: currency

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drill_members

Using the drill_members parameter, you can define a set of drill down fields for the measure. drill_members is defined as an array of dimensions. Cube automatically injects dimensions’ names and other cubes’ names with dimensions in the context, so you can reference these variables in the drill_members array. Learn more about how to define and use drill downs. 
cubes:
  - name: orders
    # ...

    measures:
      - name: revenue
        type: sum
        sql: price
        drill_members:
          - id
          - price
          - status
          - products.name
          - products.id