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 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:
It is appropriate to use
AggregatingMergeTree if it reduces the number of rows by orders.
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.
When creating a
AggregatingMergeTree table the same clauses are required, as when creating a
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.
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
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
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
AggregatingMergeTree materialized view that watches the
CREATE MATERIALIZED VIEW test.basic ENGINE = AggregatingMergeTree() PARTITION BY toYYYYMM(StartDate) ORDER BY (CounterID, StartDate) AS SELECT CounterID, StartDate, sumState(Sign) AS Visits, uniqState(UserID) AS Users FROM test.visits GROUP BY CounterID, StartDate;
Inserting data into the
INSERT INTO test.visits ...
The data are inserted in both the table and view
test.basic that will perform the aggregation.
To get the aggregated data, we need to execute a query such as
SELECT ... GROUP BY ... from the view
SELECT StartDate, sumMerge(Visits) AS Visits, uniqMerge(Users) AS Users FROM test.basic GROUP BY StartDate ORDER BY StartDate;