> ## Documentation Index
> Fetch the complete documentation index at: https://private-7c7dfe99-mintlify-8c05c8a2.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

> Totals one or more `value` arrays according to the keys specified in the `key` array. Returns a tuple of arrays: keys in sorted order, followed by values summed for the corresponding keys without overflow.

# sumMap

<h2 id="sumMappedArrays">
  sumMappedArrays
</h2>

Introduced in: v1.1.0

Totals one or more `value` arrays according to the keys specified in the `key` array. Returns a tuple of arrays: keys in sorted order, followed by values summed for the corresponding keys without overflow.

<Note>
  * Passing a tuple of keys and value arrays is identical to passing an array of keys and an array of values.
  * The number of elements in `key` and all `value` arrays must be the same for each row that is totaled.
</Note>

**Syntax**

```sql theme={null}
sumMappedArrays(key, value1 [, value2, ...])
sumMappedArrays(Tuple(key, value1 [, value2, ...]))
```

**Arguments**

* `key` — Array of keys. [`Array`](/reference/data-types/array)
* `value1, value2, ...` — Arrays of values to sum for each key. [`Array`](/reference/data-types/array)

**Returned value**

Returns a tuple of arrays: the first array contains keys in sorted order, followed by arrays containing values summed for the corresponding keys. [`Tuple`](/reference/data-types/tuple)

**Examples**

**Basic usage with Nested type**

```sql title=Query theme={null}
CREATE TABLE sum_map(
    date Date,
    timeslot DateTime,
    statusMap Nested(
        status UInt16,
        requests UInt64
    ),
    statusMapTuple Tuple(Array(Int32), Array(Int32))
) ENGINE = Memory;

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]));

SELECT
    timeslot,
    sumMappedArrays(statusMap.status, statusMap.requests),
    sumMappedArrays(statusMapTuple)
FROM sum_map
GROUP BY timeslot;
```

```response title=Response theme={null}
┌────────────timeslot─┬─sumMappedArrays(statusMap.status, statusMap.requests)─┬─sumMappedArrays(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])          │
└─────────────────────┴───────────────────────────────────────────────────────┴─────────────────────────────────────────┘
```

**Multiple value arrays example**

```sql title=Query theme={null}
CREATE TABLE multi_metrics(
    date Date,
    browser_metrics Nested(
        browser String,
        impressions UInt32,
        clicks UInt32
    )
)
ENGINE = Memory;

INSERT INTO multi_metrics VALUES
    ('2000-01-01', ['Firefox', 'Chrome'], [100, 200], [10, 25]),
    ('2000-01-01', ['Chrome', 'Safari'], [150, 50], [20, 5]),
    ('2000-01-01', ['Firefox', 'Edge'], [80, 40], [8, 4]);

SELECT
    sumMappedArrays(browser_metrics.browser, browser_metrics.impressions, browser_metrics.clicks) AS result
FROM multi_metrics;
```

```response title=Response theme={null}
┌─result────────────────────────────────────────────────────────────────────────┐
│ (['Chrome', 'Edge', 'Firefox', 'Safari'], [350, 40, 180, 50], [45, 4, 18, 5]) │
└───────────────────────────────────────────────────────────────────────────────┘
-- In this example:
-- The result tuple contains three arrays
-- First array: keys (browser names) in sorted order
-- Second array: total impressions for each browser
-- Third array: total clicks for each browser
```

**See Also**

* [Map combinator for Map datatype](/reference/functions/aggregate-functions/combinators#-map)
* [sumMapWithOverflow](/reference/functions/aggregate-functions/sumMapWithOverflow)