AggregatingMergeTree
The engine inherits from 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) with a single row (within a single 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
SimpleAggregateFunction
It is appropriate to use AggregatingMergeTree if it reduces the number of rows by orders.
Creating a Table
For a description of request parameters, see request description.
Query clauses
When creating an AggregatingMergeTree table, the same clauses are required as when creating a MergeTree table.
Deprecated Method for Creating a Table
Do not use this method in new projects and, if possible, switch the old projects to the method described above.
All of the parameters have the same meaning as in MergeTree.
SELECT and INSERT
To insert data, use INSERT SELECT 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
The following example assumes that you have a database named test. Create it if it doesn't already exist using the command below:
Now create the table test.visits that contains the raw data:
Next, you need an AggregatingMergeTree table that will store AggregationFunctions 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:
Create a materialized view that populates test.agg_visits from test.visits:
Insert data into the test.visits table:
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.visits_mv:
Add another couple of records to test.visits, but this time try using a different timestamp for one of the records:
Run the SELECT query again, which will return the following output:
In some cases, you might want to avoid pre-aggregating rows at insert time to shift the cost of aggregation from insert time
to merge time. Ordinarily, it is necessary to include the columns which are not part of the aggregation in the GROUP BY
clause of the materialized view definition to avoid an error. However, you can make use of the initializeAggregation
function with setting optimize_on_insert = 0 (it is turned on by default) to achieve this. Use of GROUP BY
is no longer required in this case:
When using initializeAggregation, an aggregate state is created for each individual row without grouping.
Each source row produces one row in the materialized view, and the actual aggregation happens later when the
AggregatingMergeTree merges parts. This is only true if optimize_on_insert = 0.
