ClickHouse/docs/en/sql-reference/aggregate-functions/reference/summap.md

slug	sidebar_position
/en/sql-reference/aggregate-functions/reference/summap	141

sumMap

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 without overflow.

Syntax

• sumMap(key <Array>, value <Array>) Array type.
• sumMap(Tuple(key <Array>, value <Array>)) Tuple type.

Alias: sumMappedArrays.

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 UInt16,
        requests UInt64
    ),
    statusMapTuple Tuple(Array(Int32), Array(Int32))
) 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]));

Next, we query the table using the sumMap function, making use of both array and tuple type syntaxes:

Query:

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

Result:

┌────────────timeslot─┬─sumMap(statusMap.status, statusMap.requests)─┬─sumMap(statusMapTuple)─────────┐
│ 2000-01-01 00:00:00 │ ([1,2,3,4,5],[10,10,20,10,10])               │ ([1,2,3,4,5],[10,10,20,10,10]) │
│ 2000-01-01 00:01:00 │ ([4,5,6,7,8],[10,10,20,10,10])               │ ([4,5,6,7,8],[10,10,20,10,10]) │
└─────────────────────┴──────────────────────────────────────────────┴────────────────────────────────┘

See Also

• Map combinator for Map datatype
• sumMapWithOverflow