---
slug: /en/engines/table-engines/mergetree-family/aggregatingmergetree
sidebar_position: 60
sidebar_label: AggregatingMergeTree
---
# AggregatingMergeTree
The engine inherits from [MergeTree](../../../engines/table-engines/mergetree-family/mergetree.md#table_engines-mergetree), altering the logic for data parts merging. ClickHouse replaces all rows with the same primary key (or more accurately, with the same [sorting key](../../../engines/table-engines/mergetree-family/mergetree.md)) with a single row (within a one data part) that stores a combination of states of aggregate functions.
You can use `AggregatingMergeTree` tables for incremental data aggregation, including for aggregated materialized views.
You can see an example of how to use the AggregatingMergeTree and Aggregate functions in the below video:
The engine processes all columns with the following types:
## [AggregateFunction](../../../sql-reference/data-types/aggregatefunction.md)
## [SimpleAggregateFunction](../../../sql-reference/data-types/simpleaggregatefunction.md)
It is appropriate to use `AggregatingMergeTree` if it reduces the number of rows by orders.
## Creating a Table {#creating-a-table}
``` sql
CREATE TABLE [IF NOT EXISTS] [db.]table_name [ON CLUSTER cluster]
(
name1 [type1] [DEFAULT|MATERIALIZED|ALIAS expr1],
name2 [type2] [DEFAULT|MATERIALIZED|ALIAS expr2],
...
) ENGINE = AggregatingMergeTree()
[PARTITION BY expr]
[ORDER BY expr]
[SAMPLE BY expr]
[TTL expr]
[SETTINGS name=value, ...]
```
For a description of request parameters, see [request description](../../../sql-reference/statements/create/table.md).
**Query clauses**
When creating an `AggregatingMergeTree` table, the same [clauses](../../../engines/table-engines/mergetree-family/mergetree.md) are required as when creating a `MergeTree` table.
Deprecated Method for Creating a Table
:::note
Do not use this method in new projects and, if possible, switch the old projects to the method described above.
:::
``` sql
CREATE TABLE [IF NOT EXISTS] [db.]table_name [ON CLUSTER cluster]
(
name1 [type1] [DEFAULT|MATERIALIZED|ALIAS expr1],
name2 [type2] [DEFAULT|MATERIALIZED|ALIAS expr2],
...
) ENGINE [=] AggregatingMergeTree(date-column [, sampling_expression], (primary, key), index_granularity)
```
All of the parameters have the same meaning as in `MergeTree`.
## SELECT and INSERT {#select-and-insert}
To insert data, use [INSERT SELECT](../../../sql-reference/statements/insert-into.md) query with aggregate -State- functions.
When selecting data from `AggregatingMergeTree` table, use `GROUP BY` clause and the same aggregate functions as when inserting data, but using the `-Merge` suffix.
In the results of `SELECT` query, the values of `AggregateFunction` type have implementation-specific binary representation for all of the ClickHouse output formats. For example, if you dump data into `TabSeparated` format with a `SELECT` query, then this dump can be loaded back using an `INSERT` query.
## Example of an Aggregated Materialized View {#example-of-an-aggregated-materialized-view}
The following example assumes that you have a database named `test`, so create it if it doesn't already exist:
```sql
CREATE DATABASE test;
```
Now create the table `test.visits` that contains the raw data:
``` sql
CREATE TABLE test.visits
(
StartDate DateTime64 NOT NULL,
CounterID UInt64,
Sign Nullable(Int32),
UserID Nullable(Int32)
) ENGINE = MergeTree ORDER BY (StartDate, CounterID);
```
Next, you need an `AggregatingMergeTree` table that will store `AggregationFunction`s that keep track of the total number of visits and the number of unique users.
Create an `AggregatingMergeTree` materialized view that watches the `test.visits` table, and uses the `AggregateFunction` type:
``` sql
CREATE TABLE test.agg_visits (
StartDate DateTime64 NOT NULL,
CounterID UInt64,
Visits AggregateFunction(sum, Nullable(Int32)),
Users AggregateFunction(uniq, Nullable(Int32))
)
ENGINE = AggregatingMergeTree() ORDER BY (StartDate, CounterID);
```
Create a materialized view that populates `test.agg_visits` from `test.visits`:
```sql
CREATE MATERIALIZED VIEW test.visits_mv TO test.agg_visits
AS SELECT
StartDate,
CounterID,
sumState(Sign) AS Visits,
uniqState(UserID) AS Users
FROM test.visits
GROUP BY StartDate, CounterID;
```
Insert data into the `test.visits` table:
``` sql
INSERT INTO test.visits (StartDate, CounterID, Sign, UserID)
VALUES (1667446031000, 1, 3, 4), (1667446031000, 1, 6, 3);
```
The data is inserted in both `test.visits` and `test.agg_visits`.
To get the aggregated data, execute a query such as `SELECT ... GROUP BY ...` from the materialized view `test.mv_visits`:
```sql
SELECT
StartDate,
sumMerge(Visits) AS Visits,
uniqMerge(Users) AS Users
FROM test.agg_visits
GROUP BY StartDate
ORDER BY StartDate;
```
```text
┌───────────────StartDate─┬─Visits─┬─Users─┐
│ 2022-11-03 03:27:11.000 │ 9 │ 2 │
└─────────────────────────┴────────┴───────┘
```
Add another couple of records to `test.visits`, but this time try using a different timestamp for one of the records:
```sql
INSERT INTO test.visits (StartDate, CounterID, Sign, UserID)
VALUES (1669446031000, 2, 5, 10), (1667446031000, 3, 7, 5);
```
Run the `SELECT` query again, which will return the following output:
```text
┌───────────────StartDate─┬─Visits─┬─Users─┐
│ 2022-11-03 03:27:11.000 │ 16 │ 3 │
│ 2022-11-26 07:00:31.000 │ 5 │ 1 │
└─────────────────────────┴────────┴───────┘
```
## Related Content
- Blog: [Using Aggregate Combinators in ClickHouse](https://clickhouse.com/blog/aggregate-functions-combinators-in-clickhouse-for-arrays-maps-and-states)