RCOUNT (window)

    Syntax

    RCOUNT( value [ , direction ] )
    
    RCOUNT( value [ , direction ]
            [ 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 count of all values in a growing (or shrinking) window defined by the sort order and the value of direction:

    direction Window
    "asc" Starts from the first row and ends at the current row.
    "desc" Starts from the current row and ends at the last row.

    By default "asc" is used.

    Window functions with a similar behavior: RSUM, RMIN, RMAX, RAVG.

    See also COUNT, MCOUNT.

    Argument types:

    • valueAny
    • directionString

    Return type: Integer

    Note

    Only constant values are accepted for the arguments (direction).

    Examples

    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]) RCOUNT(SUM([Orders]) TOTAL) RCOUNT(SUM([Orders]) WITHIN [City]) RCOUNT(SUM([Orders]) AMONG [City])
    'Detroit' 'Furniture' 7 1 1 1
    'Detroit' 'Office Supplies' 25 2 2 2
    'London' 'Furniture' 1 3 1 2
    'London' 'Office Supplies' 10 4 2 4
    'Moscow' 'Furniture' 2 5 1 3
    'Moscow' 'Office Supplies' 4 6 2 3
    'San Francisco' 'Furniture' 5 7 1 4
    'San Francisco' 'Office Supplies' 23 8 2 1
    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]) RCOUNT(SUM([Orders]), "desc") RCOUNT(SUM([Orders]), "asc" ORDER BY [City] DESC) RCOUNT(SUM([Orders]) ORDER BY [Order Sum])
    'Detroit' 32 4 4 4
    'London' 11 3 3 2
    'Moscow' 6 2 2 1
    'San Francisco' 28 1 1 3

    Data source support

    ClickHouse 19.13, Microsoft SQL Server 2017 (14.0), MySQL 5.6, PostgreSQL 9.3.