The engine inherits from MergeTree. The difference is that when merging data parts for
SummingMergeTree tables ClickHouse replaces all the rows with the same primary key (or more accurately, with the same sorting key) with one row which contains summarized values for the columns with the numeric data type. If the sorting key is composed in a way that a single key value corresponds to large number of rows, this significantly reduces storage volume and speeds up data selection.
We recommend to use the engine together with
MergeTree. Store complete data in
MergeTree table, and use
SummingMergeTree for aggregated data storing, for example, when preparing reports. Such an approach will prevent you from losing valuable data due to an incorrectly composed primary key.
CREATE TABLE [IF NOT EXISTS] [db.]table_name [ON CLUSTER cluster] ( name1 [type1] [DEFAULT|MATERIALIZED|ALIAS expr1], name2 [type2] [DEFAULT|MATERIALIZED|ALIAS expr2], ... ) ENGINE = SummingMergeTree([columns]) [PARTITION BY expr] [ORDER BY expr] [SAMPLE BY expr] [SETTINGS name=value, ...]
For a description of request parameters, see request description.
Parameters of SummingMergeTree
columns- a tuple with the names of columns where values will be summarized. Optional parameter.
The columns must be of a numeric type and must not be in the primary key.
columnsnot specified, ClickHouse summarizes the values in all columns with a numeric data type that are not in the primary key.
When creating a
SummingMergeTree 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 [=] SummingMergeTree(date-column [, sampling_expression], (primary, key), index_granularity, [columns])
All of the parameters excepting
columns have the same meaning as in
columns— tuple with names of columns values of which will be summarized. Optional parameter. For a description, see the text above.
Consider the following table:
CREATE TABLE summtt ( key UInt32, value UInt32 ) ENGINE = SummingMergeTree() ORDER BY key
Insert data to it:
INSERT INTO summtt Values(1,1),(1,2),(2,1)
ClickHouse may sum all the rows not completely (see below), so we use an aggregate function
GROUP BY clause in the query.
SELECT key, sum(value) FROM summtt GROUP BY key
┌─key─┬─sum(value)─┐ │ 2 │ 1 │ │ 1 │ 3 │ └─────┴────────────┘
When data are inserted into a table, they are saved as-is. ClickHouse merges the inserted parts of data periodically and this is when rows with the same primary key are summed and replaced with one for each resulting part of data.
ClickHouse can merge the data parts so that different resulting parts of data cat consist rows with the same primary key, i.e. the summation will be incomplete. Therefore (
SELECT) an aggregate function sum() and
GROUP BY clause should be used in a query as described in the example above.
The values in the columns with the numeric data type are summarized. The set of columns is defined by the parameter
If the values were 0 in all of the columns for summation, the row is deleted.
If column is not in the primary key and is not summarized, an arbitrary value is selected from the existing ones.
The values are not summarized for columns in the primary key.
Table can have nested data structures that are processed in a special way.
If the name of a nested table ends with
Map and it contains at least two columns that meet the following criteria:
- the first column is numeric
(*Int*, Date, DateTime)or a string
(String, FixedString), let’s call it
- the other columns are arithmetic
(*Int*, Float32/64), let’s call it
then this nested table is interpreted as a mapping of
key => (values...), and when merging its rows, the elements of two data sets are merged by
key with a summation of the corresponding
[(1, 100)] + [(2, 150)] -> [(1, 100), (2, 150)] [(1, 100)] + [(1, 150)] -> [(1, 250)] [(1, 100)] + [(1, 150), (2, 150)] -> [(1, 250), (2, 150)] [(1, 100), (2, 150)] + [(1, -100)] -> [(2, 150)]
When requesting data, use the sumMap(key, value) function for aggregation of
For nested data structure, you do not need to specify its columns in the tuple of columns for summation.