The suffix -If can be appended to the name of any aggregate function. In this case, the aggregate function accepts an extra argument – a condition (Uint8 type). The aggregate function processes only the rows that trigger the condition. If the condition was not triggered even once, it returns a default value (usually zeros or empty strings).
Examples: `sumIf(column, cond)`, `countIf(cond)`, `avgIf(x, cond)`, `quantilesTimingIf(level1, level2)(x, cond)`, `argMinIf(arg, val, cond)` and so on.
With conditional aggregate functions, you can calculate aggregates for several conditions at once, without using subqueries and `JOIN`s. For example, in Yandex.Metrica, conditional aggregate functions are used to implement the segment comparison functionality.
## -Array
The -Array suffix can be appended to any aggregate function. In this case, the aggregate function takes arguments of the 'Array(T)' type (arrays) instead of 'T' type arguments. If the aggregate function accepts multiple arguments, this must be arrays of equal lengths. When processing arrays, the aggregate function works like the original aggregate function across all array elements.
Example 1: `sumArray(arr)` - Totals all the elements of all 'arr' arrays. In this example, it could have been written more simply: `sum(arraySum(arr))`.
Example 2: `uniqArray(arr)`– Count the number of unique elements in all 'arr' arrays. This could be done an easier way: `uniq(arrayJoin(arr))`, but it's not always possible to add 'arrayJoin' to a query.
-If and -Array can be combined. However, 'Array' must come first, then 'If'. Examples: `uniqArrayIf(arr, cond)`, `quantilesTimingArrayIf(level1, level2)(arr, cond)`. Due to this order, the 'cond' argument can't be an array.
If you apply this combinator, the aggregate function doesn't return the resulting value (such as the number of unique values for the [uniq](reference.md#agg_function-uniq) function), but an intermediate state of the aggregation (for `uniq`, this is the hash table for calculating the number of unique values). This is an `AggregateFunction(...)` that can be used for further processing or stored in a table to finish aggregating later.
If you apply this combinator, the aggregate function takes the intermediate aggregation state as an argument, combines the states to finish aggregation, and returns the resulting value.
Merges the intermediate aggregation states in the same way as the -Merge combinator. However, it doesn't return the resulting value, but an intermediate aggregation state, similar to the -State combinator.
## -ForEach
Converts an aggregate function for tables into an aggregate function for arrays that aggregates the corresponding array items and returns an array of results. For example, `sumForEach` for the arrays `[1, 2]`, `[3, 4, 5]`and`[6, 7]`returns the result `[10, 13, 5]` after adding together the corresponding array items.
Allows to divide data by groups, and then separately aggregates the data in those groups. Groups are created by splitting the values of one of the columns into intervals.
-`start` — Starting value of the whole required interval for the values of `resampling_key`.
-`stop` — Ending value of the whole required interval for the values of `resampling_key`. The whole interval doesn't include the `stop` value `[start, stop)`.
-`step` — Step for separating the whole interval by subintervals. The `aggFunction` is executed over each of those subintervals independently.
-`resampling_key` — Column, which values are used for separating data by intervals.
-`aggFunction_params` — Parameters of `aggFunction`.
**Returned values**
- Array of `aggFunction` results for each of subintervals.
**Example**
Consider the `people` table with the following data:
```text
┌─name───┬─age─┬─wage─┐
│ John │ 16 │ 10 │
│ Alice │ 30 │ 15 │
│ Mary │ 35 │ 8 │
│ Evelyn │ 48 │ 11.5 │
│ David │ 62 │ 9.9 │
│ Brian │ 60 │ 16 │
└────────┴─────┴──────┘
```
Let's get the names of the persons which age lies in the intervals of `[30,60)` and `[60,75)`. As we use integer representation of age, then there are ages of `[30, 59]` and `[60,74]`.
For aggregating names into the array, we use the aggregate function [groupArray](reference.md#agg_function-grouparray). It takes a single argument. For our case, it is the `name` column. The `groupArrayResample` function should use the `age` column to aggregate names by age. To define required intervals, we pass the `(30, 75, 30)` arguments into the `groupArrayResample` function.
```sql
SELECT groupArrayResample(30, 75, 30)(name, age) from people
```
```text
┌─groupArrayResample(30, 75, 30)(name, age)─────┐
│ [['Alice','Mary','Evelyn'],['David','Brian']] │
└───────────────────────────────────────────────┘
```
Consider the results.
`Jonh` is out of the sample because he is too young. Other people are distributed according to the specified age intervals.
Now, let's count the total number of people and their average wage in the specified age intervals.
