sumMapWithOverflow

Totals a value array according to the keys specified in the key array. Returns a tuple of two arrays: keys in sorted order, and values summed for the corresponding keys. It differs from the sumMap function in that it does summation with overflow - i.e. returns the same data type for the summation as the argument data type.

Syntax

sumMapWithOverflow(key <Array>, value <Array>) Array type.
sumMapWithOverflow(Tuple(key <Array>, value <Array>)) Tuple type.

Arguments

key: Array of keys.
value: Array of values.

Passing a tuple of key and value arrays is a synonym to passing separately an array of keys and an array of values.

Note

The number of elements in key and value must be the same for each row that is totaled.

Returned Value

Returns a tuple of two arrays: keys in sorted order, and values summed for the corresponding keys.

Example

First we create a table called sum_map, and insert some data into it. Arrays of keys and values are stored separately as a column called statusMap of Nested type, and together as a column called statusMapTuple of tuple type to illustrate the use of the two different syntaxes of this function described above.

Query:

CREATE TABLE sum_map(
    date Date,
    timeslot DateTime,
    statusMap Nested(
        status UInt8,
        requests UInt8
    ),
    statusMapTuple Tuple(Array(Int8), Array(Int8))
) ENGINE = Log;

INSERT INTO sum_map VALUES
    ('2000-01-01', '2000-01-01 00:00:00', [1, 2, 3], [10, 10, 10], ([1, 2, 3], [10, 10, 10])),
    ('2000-01-01', '2000-01-01 00:00:00', [3, 4, 5], [10, 10, 10], ([3, 4, 5], [10, 10, 10])),
    ('2000-01-01', '2000-01-01 00:01:00', [4, 5, 6], [10, 10, 10], ([4, 5, 6], [10, 10, 10])),
    ('2000-01-01', '2000-01-01 00:01:00', [6, 7, 8], [10, 10, 10], ([6, 7, 8], [10, 10, 10]));

If we query the table using the sumMap, sumMapWithOverflow with the array type syntax, and toTypeName functions then we can see that for the sumMapWithOverflow function, the data type of the summed values array is the same as the argument type, both UInt8 (i.e. summation was done with overflow). For sumMap the data type of the summed values arrays has changed from UInt8 to UInt64 such that overflow does not occur.

Query:

SELECT
    timeslot,
    toTypeName(sumMap(statusMap.status, statusMap.requests)),
    toTypeName(sumMapWithOverflow(statusMap.status, statusMap.requests)),
FROM sum_map
GROUP BY timeslot

Equivalently we could have used the tuple syntax with for the same result.

SELECT
    timeslot,
    toTypeName(sumMap(statusMapTuple)),
    toTypeName(sumMapWithOverflow(statusMapTuple)),
FROM sum_map
GROUP BY timeslot

Result:

   ┌────────────timeslot─┬─toTypeName(sumMap(statusMap.status, statusMap.requests))─┬─toTypeName(sumMapWithOverflow(statusMap.status, statusMap.requests))─┐
1. │ 2000-01-01 00:01:00 │ Tuple(Array(UInt8), Array(UInt64))                       │ Tuple(Array(UInt8), Array(UInt8))                                    │
2. │ 2000-01-01 00:00:00 │ Tuple(Array(UInt8), Array(UInt64))                       │ Tuple(Array(UInt8), Array(UInt8))                                    │
   └─────────────────────┴──────────────────────────────────────────────────────────┴──────────────────────────────────────────────────────────────────────┘

See Also

sumMap