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MSUM (window)

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    Syntax

    MSUM( value, rows_1 [ , rows_2 ] )

    MSUM( value, rows_1 [ , rows_2 ]
      [ TOTAL | WITHIN ... | AMONG ... ]
      [ ORDER BY ... ]
      [ BEFORE FILTER BY ... ]
    )

    More info:

    Description

    Warning

    The sorting order is based on the fields listed in the sorting section of the chart and in the ORDER BY clause. First, ORDER BY fields are used, and then they are complemented by the fields from the chart.

    Returns the moving sum of values in a fixed-size window defined by the sort order and arguments:

    rows_1 rows_2 Window
    positive - The current row and rows_1 preceding rows.
    negative - The current row and -rows_1 following rows.
    any sign any sign rows_1 preceding rows, the current row and rows_2 following rows.

    Window functions with a similar behavior: MCOUNT, MMIN, MMAX, MAVG.

    See also SUM, RSUM.

    Argument types:

    • value — Fractional number | Integer
    • rows_1 — Integer
    • rows_2 — Integer

    Return type: Same type as (value)

    Note

    Only constant values are accepted for the arguments (rows_1, rows_2).

    Examples

    Example with two and three arguments

    Source data

    Date City Category Orders Profit
    '2019-03-01' 'London' 'Office Supplies' 8 120.80
    '2019-03-04' 'London' 'Office Supplies' 2 100.00
    '2019-03-05' 'London' 'Furniture' 1 750.00
    '2019-03-02' 'Moscow' 'Furniture' 2 1250.50
    '2019-03-03' 'Moscow' 'Office Supplies' 4 85.00
    '2019-03-01' 'San Francisco' 'Office Supplies' 23 723.00
    '2019-03-01' 'San Francisco' 'Furniture' 1 1000.00
    '2019-03-03' 'San Francisco' 'Furniture' 4 4000.00
    '2019-03-02' 'Detroit' 'Furniture' 5 3700.00
    '2019-03-04' 'Detroit' 'Office Supplies' 25 1200.00
    '2019-03-04' 'Detroit' 'Furniture' 2 3500.00

    Grouped by [City].

    Sorted by [City].

    Result

    [City] SUM([Orders]) MSUM(SUM([Orders]), 1) MSUM(SUM([Orders]), -2) MSUM(SUM([Orders]) 1, 1)
    'Detroit' 32 32 49 43
    'London' 11 43 45 49
    'Moscow' 6 17 34 45
    'San Francisco' 28 34 28 34
    Example with ORDER BY

    Source data

    Date City Category Orders Profit
    '2019-03-01' 'London' 'Office Supplies' 8 120.80
    '2019-03-04' 'London' 'Office Supplies' 2 100.00
    '2019-03-05' 'London' 'Furniture' 1 750.00
    '2019-03-02' 'Moscow' 'Furniture' 2 1250.50
    '2019-03-03' 'Moscow' 'Office Supplies' 4 85.00
    '2019-03-01' 'San Francisco' 'Office Supplies' 23 723.00
    '2019-03-01' 'San Francisco' 'Furniture' 1 1000.00
    '2019-03-03' 'San Francisco' 'Furniture' 4 4000.00
    '2019-03-02' 'Detroit' 'Furniture' 5 3700.00
    '2019-03-04' 'Detroit' 'Office Supplies' 25 1200.00
    '2019-03-04' 'Detroit' 'Furniture' 2 3500.00

    Grouped by [City].

    Sorted by [City].

    Result

    [City] SUM([Orders]) MSUM(SUM([Orders]), 1 ORDER BY [City] DESC) MSUM(SUM([Orders]), 1 ORDER BY [Order Sum])
    'Detroit' 32 43 60
    'London' 11 17 17
    'Moscow' 6 34 6
    'San Francisco' 28 28 39
    Example with grouping

    Source data

    Date City Category Orders Profit
    '2019-03-01' 'London' 'Office Supplies' 8 120.80
    '2019-03-04' 'London' 'Office Supplies' 2 100.00
    '2019-03-05' 'London' 'Furniture' 1 750.00
    '2019-03-02' 'Moscow' 'Furniture' 2 1250.50
    '2019-03-03' 'Moscow' 'Office Supplies' 4 85.00
    '2019-03-01' 'San Francisco' 'Office Supplies' 23 723.00
    '2019-03-01' 'San Francisco' 'Furniture' 1 1000.00
    '2019-03-03' 'San Francisco' 'Furniture' 4 4000.00
    '2019-03-02' 'Detroit' 'Furniture' 5 3700.00
    '2019-03-04' 'Detroit' 'Office Supplies' 25 1200.00
    '2019-03-04' 'Detroit' 'Furniture' 2 3500.00

    Grouped by [City], [Category].

    Sorted by [City], [Category].

    Result

    [City] [Category] SUM([Orders]) MSUM(SUM([Orders]), 1 TOTAL) MSUM(SUM([Orders]), 1 WITHIN [City]) MSUM(SUM([Orders]), 1 AMONG [City])
    'Detroit' 'Furniture' 7 7 7 7
    'Detroit' 'Office Supplies' 25 32 32 48
    'London' 'Furniture' 1 26 1 8
    'London' 'Office Supplies' 10 11 11 14
    'Moscow' 'Furniture' 2 12 2 3
    'Moscow' 'Office Supplies' 4 6 6 29
    'San Francisco' 'Furniture' 5 9 5 7
    'San Francisco' 'Office Supplies' 23 28 28 23

    Data source support

    Materialized Dataset, ClickHouse 19.13, Microsoft SQL Server 2017 (14.0), MySQL 5.6, Oracle Database 12c (12.1), PostgreSQL 9.3.