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* translate docs/zh/operations/table_engines/aggregatingmergetree.md * optimize docs/zh/operations/table_engines/aggregatingmergetree.md
94 lines
3.4 KiB
Markdown
94 lines
3.4 KiB
Markdown
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# AggregatingMergeTree
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The engine inherits from [MergeTree](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](mergetree.md)) with a single row (within a one data part) that stores a combination of states of aggregate functions.
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You can use `AggregatingMergeTree` tables for incremental data aggregation, including for aggregated materialized views.
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The engine processes all columns with [AggregateFunction](../../data_types/nested_data_structures/aggregatefunction.md) type.
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It is appropriate to use `AggregatingMergeTree` if it reduces the number of rows by orders.
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## Creating a Table
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``` sql
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CREATE TABLE [IF NOT EXISTS] [db.]table_name [ON CLUSTER cluster]
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(
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name1 [type1] [DEFAULT|MATERIALIZED|ALIAS expr1],
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name2 [type2] [DEFAULT|MATERIALIZED|ALIAS expr2],
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...
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) ENGINE = AggregatingMergeTree()
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[PARTITION BY expr]
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[ORDER BY expr]
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[SAMPLE BY expr]
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[SETTINGS name=value, ...]
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```
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For a description of request parameters, see [request description](../../query_language/create.md).
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**Query clauses**
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When creating a `AggregatingMergeTree` table the same [clauses](mergetree.md) are required, as when creating a `MergeTree` table.
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<details markdown="1"><summary>Deprecated Method for Creating a Table</summary>
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!!! attention
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Do not use this method in new projects and, if possible, switch the old projects to the method described above.
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```sql
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CREATE TABLE [IF NOT EXISTS] [db.]table_name [ON CLUSTER cluster]
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(
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name1 [type1] [DEFAULT|MATERIALIZED|ALIAS expr1],
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name2 [type2] [DEFAULT|MATERIALIZED|ALIAS expr2],
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...
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) ENGINE [=] AggregatingMergeTree(date-column [, sampling_expression], (primary, key), index_granularity)
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```
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All of the parameters have the same meaning as in `MergeTree`.
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</details>
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## SELECT and INSERT
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To insert data, use [INSERT SELECT](../../query_language/insert_into.md) query with aggregate -State- functions.
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When selecting data from `AggregatingMergeTree` table, use `GROUP BY` clause and the same aggregate functions as when inserting data, but using `-Merge` suffix.
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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.
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## Example of an Aggregated Materialized View
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`AggregatingMergeTree` materialized view that watches the `test.visits` table:
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``` sql
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CREATE MATERIALIZED VIEW test.basic
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ENGINE = AggregatingMergeTree() PARTITION BY toYYYYMM(StartDate) ORDER BY (CounterID, StartDate)
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AS SELECT
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CounterID,
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StartDate,
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sumState(Sign) AS Visits,
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uniqState(UserID) AS Users
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FROM test.visits
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GROUP BY CounterID, StartDate;
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```
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Inserting of data into the `test.visits` table.
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``` sql
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INSERT INTO test.visits ...
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```
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The data are inserted in both the table and view `test.basic` that will perform the aggregation.
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To get the aggregated data, we need to execute a query such as `SELECT ... GROUP BY ...` from the view `test.basic`:
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``` sql
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SELECT
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StartDate,
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sumMerge(Visits) AS Visits,
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uniqMerge(Users) AS Users
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FROM test.basic
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GROUP BY StartDate
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ORDER BY StartDate;
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```
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[Original article](https://clickhouse.yandex/docs/en/operations/table_engines/aggregatingmergetree/) <!--hide-->
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