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4.1 KiB
Markdown
93 lines
4.1 KiB
Markdown
---
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slug: /en/sql-reference/aggregate-functions/reference/summapwithoverflow
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sidebar_position: 141
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---
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# sumMapWithOverflow
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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.
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It differs from the [sumMap](../reference/summap.md) function in that it does summation with overflow - i.e. returns the same data type for the summation as the argument data type.
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**Syntax**
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- `sumMapWithOverflow(key <Array>, value <Array>)` [Array type](../../data-types/array.md).
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- `sumMapWithOverflow(Tuple(key <Array>, value <Array>))` [Tuple type](../../data-types/tuple.md).
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**Arguments**
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- `key`: [Array](../../data-types/array.md) of keys.
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- `value`: [Array](../../data-types/array.md) of values.
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Passing a tuple of key and value arrays is a synonym to passing separately an array of keys and an array of values.
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:::note
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The number of elements in `key` and `value` must be the same for each row that is totaled.
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:::
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**Returned Value**
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- Returns a tuple of two arrays: keys in sorted order, and values summed for the corresponding keys.
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**Example**
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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](../../data-types/nested-data-structures/index.md) type, and together as a column called `statusMapTuple` of [tuple](../../data-types/tuple.md) type to illustrate the use of the two different syntaxes of this function described above.
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Query:
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``` sql
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CREATE TABLE sum_map(
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date Date,
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timeslot DateTime,
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statusMap Nested(
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status UInt8,
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requests UInt8
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),
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statusMapTuple Tuple(Array(Int8), Array(Int8))
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) ENGINE = Log;
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```
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```sql
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INSERT INTO sum_map VALUES
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('2000-01-01', '2000-01-01 00:00:00', [1, 2, 3], [10, 10, 10], ([1, 2, 3], [10, 10, 10])),
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('2000-01-01', '2000-01-01 00:00:00', [3, 4, 5], [10, 10, 10], ([3, 4, 5], [10, 10, 10])),
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('2000-01-01', '2000-01-01 00:01:00', [4, 5, 6], [10, 10, 10], ([4, 5, 6], [10, 10, 10])),
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('2000-01-01', '2000-01-01 00:01:00', [6, 7, 8], [10, 10, 10], ([6, 7, 8], [10, 10, 10]));
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```
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If we query the table using the `sumMap`, `sumMapWithOverflow` with the array type syntax, and `toTypeName` functions then we can see that
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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.
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Query:
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``` sql
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SELECT
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timeslot,
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toTypeName(sumMap(statusMap.status, statusMap.requests)),
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toTypeName(sumMapWithOverflow(statusMap.status, statusMap.requests)),
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FROM sum_map
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GROUP BY timeslot
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```
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Equivalently we could have used the tuple syntax with for the same result.
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``` sql
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SELECT
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timeslot,
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toTypeName(sumMap(statusMapTuple)),
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toTypeName(sumMapWithOverflow(statusMapTuple)),
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FROM sum_map
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GROUP BY timeslot
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```
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Result:
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``` text
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┌────────────timeslot─┬─toTypeName(sumMap(statusMap.status, statusMap.requests))─┬─toTypeName(sumMapWithOverflow(statusMap.status, statusMap.requests))─┐
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1. │ 2000-01-01 00:01:00 │ Tuple(Array(UInt8), Array(UInt64)) │ Tuple(Array(UInt8), Array(UInt8)) │
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2. │ 2000-01-01 00:00:00 │ Tuple(Array(UInt8), Array(UInt64)) │ Tuple(Array(UInt8), Array(UInt8)) │
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└─────────────────────┴──────────────────────────────────────────────────────────┴──────────────────────────────────────────────────────────────────────┘
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```
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**See Also**
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- [sumMap](../reference/summap.md)
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