ClickHouse/docs/en/table_engines/summingmergetree.md

# SummingMergeTree

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

```sql
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.

```sql
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 null in all of these columns, the row is deleted. (The exception is cases when the data part would not have any rows left in it.)

For the other columns that are not part of the primary key, the first value that occurs is selected when merging. But if a column is of AggregateFunction type, then it is merged according to that function, which effectively makes this engine behave 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:

```text
[(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.