mirror of
https://github.com/ClickHouse/ClickHouse.git
synced 2024-12-02 04:22:03 +00:00
83 lines
3.1 KiB
Markdown
83 lines
3.1 KiB
Markdown
---
|
||
slug: /en/sql-reference/aggregate-functions/reference/summap
|
||
sidebar_position: 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](../../data-types/array.md).
|
||
- `sumMap(Tuple(key <Array>, value <Array>))` [Tuple type](../../data-types/tuple.md).
|
||
|
||
Alias: `sumMappedArrays`.
|
||
|
||
**Arguments**
|
||
|
||
- `key`: [Array](../../data-types/array.md) of keys.
|
||
- `value`: [Array](../../data-types/array.md) 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](../../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.
|
||
|
||
Query:
|
||
|
||
``` sql
|
||
CREATE TABLE sum_map(
|
||
date Date,
|
||
timeslot DateTime,
|
||
statusMap Nested(
|
||
status UInt16,
|
||
requests UInt64
|
||
),
|
||
statusMapTuple Tuple(Array(Int32), Array(Int32))
|
||
) ENGINE = Log;
|
||
```
|
||
```sql
|
||
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:
|
||
|
||
``` sql
|
||
SELECT
|
||
timeslot,
|
||
sumMap(statusMap.status, statusMap.requests),
|
||
sumMap(statusMapTuple)
|
||
FROM sum_map
|
||
GROUP BY timeslot
|
||
```
|
||
|
||
Result:
|
||
|
||
``` text
|
||
┌────────────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](../combinators.md#-map)
|
||
- [sumMapWithOverflow](../reference/summapwithoverflow.md)
|