SummingMergeTree

This engine differs from MergeTree in that it totals data while merging.

SummingMergeTree(EventDate, (OrderID, EventDate, BannerID, ...), 8192)

The columns to total are implicit. When merging, all rows with the same primary key value (in the example, OrderId, EventDate, BannerID, ...) have their values totaled in numeric columns that are not part of the primary key.

SummingMergeTree(EventDate, (OrderID, EventDate, BannerID, ...), 8192, (Shows, Clicks, Cost, ...))

The columns to total are set explicitly (the last parameter – Shows, Clicks, Cost, ...). When merging, all rows with the same primary key value have their values totaled in the specified columns. The specified columns also must be numeric and must not be part of the primary key.

If the values were zero in all of these columns, the row is deleted.

For the other columns that are not part of the primary key, the first value that occurs is selected when merging. But for the AggregateFunction type of columns, aggregation is performed according to the set function, so this engine actually behaves like AggregatingMergeTree.

Summation is not performed for a read operation. If it is necessary, write the appropriate GROUP BY.

In addition, a table can have nested data structures that are processed in a special way. If the name of a nested table ends in 'Map' and it contains at least two columns that meet the following criteria:

  • The first table is numeric ((U)IntN, Date, DateTime), which we'll refer to as the 'key'.
  • The other columns are arithmetic ((U)IntN, Float32/64), which we'll refer to as '(values...)'.

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 (values...).

Examples:

[(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)]

For aggregation of Map, use the function sumMap(key, value).

For nested data structures, you don't need to specify the columns as a list of columns for totaling.

This table engine is not particularly useful. Remember that when saving just pre-aggregated data, you lose some of the system's advantages.