ClickHouse/docs/en/engines/table-engines/mergetree-family/aggregatingmergetree.md

---
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.

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.

<details markdown="1">

<summary>Deprecated Method for Creating a Table</summary>

:::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`.
</details>

## 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 `-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. If dump data into, for example, `TabSeparated` format with `SELECT` query then this dump can be loaded back using `INSERT` query.

## Example of an Aggregated Materialized View {#example-of-an-aggregated-materialized-view}

The following examples assumes that you have a database named `test` so make sure you create that if it doesn't already exist:

```sql
CREATE DATABASE test;
```

We will create the table `test.visits` that contain 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, we need to create an `AggregatingMergeTree` table that will store `AggregationFunction`s that keep track of the total number of visits and the number of unique users. 

`AggregatingMergeTree` materialized view that watches the `test.visits` table, and use 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);
```

And then let's 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;
```

Inserting 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, we need to 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 │
└─────────────────────────┴────────┴───────┘
```

And how about if we add another couple of records to `test.visits`, but this time we'll use 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);
```

If we then run the `SELECT` query again, we'll see 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)