mirror of
https://github.com/ClickHouse/ClickHouse.git
synced 2024-11-20 14:42:02 +00:00
89 lines
3.6 KiB
Markdown
89 lines
3.6 KiB
Markdown
# AggregatingMergeTree
|
|
|
|
This engine differs from `MergeTree` in that the merge combines the states of aggregate functions stored in the table for rows with the same primary key value.
|
|
|
|
For this to work, it uses the `AggregateFunction` data type, as well as `-State` and `-Merge` modifiers for aggregate functions. Let's examine it more closely.
|
|
|
|
There is an `AggregateFunction` data type. It is a parametric data type. As parameters, the name of the aggregate function is passed, then the types of its arguments.
|
|
|
|
Examples:
|
|
|
|
```sql
|
|
CREATE TABLE t
|
|
(
|
|
column1 AggregateFunction(uniq, UInt64),
|
|
column2 AggregateFunction(anyIf, String, UInt8),
|
|
column3 AggregateFunction(quantiles(0.5, 0.9), UInt64)
|
|
) ENGINE = ...
|
|
```
|
|
|
|
This type of column stores the state of an aggregate function.
|
|
|
|
To get this type of value, use aggregate functions with the `State` suffix.
|
|
|
|
Example:
|
|
`uniqState(UserID), quantilesState(0.5, 0.9)(SendTiming)`
|
|
|
|
In contrast to the corresponding `uniq` and `quantiles` functions, these functions return the state, rather than the prepared value. In other words, they return an `AggregateFunction` type value.
|
|
|
|
An `AggregateFunction` type value can't be output in Pretty formats. In other formats, these types of values are output as implementation-specific binary data. The `AggregateFunction` type values are not intended for output or saving in a dump.
|
|
|
|
The only useful thing you can do with `AggregateFunction` type values is combine the states and get a result, which essentially means to finish aggregation. Aggregate functions with the 'Merge' suffix are used for this purpose.
|
|
Example: `uniqMerge(UserIDState), where UserIDState has the AggregateFunction` type.
|
|
|
|
In other words, an aggregate function with the 'Merge' suffix takes a set of states, combines them, and returns the result.
|
|
As an example, these two queries return the same result:
|
|
|
|
```sql
|
|
SELECT uniq(UserID) FROM table
|
|
|
|
SELECT uniqMerge(state) FROM (SELECT uniqState(UserID) AS state FROM table GROUP BY RegionID)
|
|
```
|
|
|
|
There is an ` AggregatingMergeTree` engine. Its job during a merge is to combine the states of aggregate functions from different table rows with the same primary key value.
|
|
|
|
You can't use a normal INSERT to insert a row in a table containing `AggregateFunction` columns, because you can't explicitly define the `AggregateFunction` value. Instead, use `INSERT SELECT` with `-State` aggregate functions for inserting data.
|
|
|
|
With SELECT from an `AggregatingMergeTree` table, use GROUP BY and aggregate functions with the '-Merge' modifier in order to complete data aggregation.
|
|
|
|
You can use `AggregatingMergeTree` tables for incremental data aggregation, including for aggregated materialized views.
|
|
|
|
Example:
|
|
|
|
Create an `AggregatingMergeTree` materialized view that watches the `test.visits` table:
|
|
|
|
```sql
|
|
CREATE MATERIALIZED VIEW test.basic
|
|
ENGINE = AggregatingMergeTree(StartDate, (CounterID, StartDate), 8192)
|
|
AS SELECT
|
|
CounterID,
|
|
StartDate,
|
|
sumState(Sign) AS Visits,
|
|
uniqState(UserID) AS Users
|
|
FROM test.visits
|
|
GROUP BY CounterID, StartDate;
|
|
```
|
|
|
|
Insert data in the `test.visits` table. Data will also be inserted in the view, where it will be aggregated:
|
|
|
|
```sql
|
|
INSERT INTO test.visits ...
|
|
```
|
|
|
|
Perform `SELECT` from the view using `GROUP BY` in order to complete data aggregation:
|
|
|
|
```sql
|
|
SELECT
|
|
StartDate,
|
|
sumMerge(Visits) AS Visits,
|
|
uniqMerge(Users) AS Users
|
|
FROM test.basic
|
|
GROUP BY StartDate
|
|
ORDER BY StartDate;
|
|
```
|
|
|
|
You can create a materialized view like this and assign a normal view to it that finishes data aggregation.
|
|
|
|
Note that in most cases, using `AggregatingMergeTree` is not justified, since queries can be run efficiently enough on non-aggregated data.
|
|
|