summarize operator

Produces a table that aggregates the content of the input table.

T | summarize count(), avg(price) by fruit, supplier

A table that shows the number and average price of each fruit from each supplier. There's a row in the output for each distinct combination of fruit and supplier. The output columns show the count, average price, fruit and supplier. All other input columns are ignored.

T | summarize count() by price_range=bin(price, 10.0)

A table that shows how many items have prices in each interval [0,10.0], [10.0,20.0], and so on. This example has a column for the count and one for the price range. All other input columns are ignored.

Syntax

T | summarize [[Column =] Aggregation [, ...]] [by [Column =] GroupExpression [, ...]]

Arguments

  • Column: Optional name for a result column. Defaults to a name derived from the expression.

  • Aggregation: A call to an aggregation function such as count() or avg(), with column names as arguments. See the list of aggregation functions.

  • GroupExpression: An expression over the columns, that provides a set of distinct values. Typically it's either a column name that already provides a restricted set of values, or bin() with a numeric or time column as argument.

    If you don't provide a GroupExpression, the whole table is summarized in a single output row.

Returns

The input rows are arranged into groups having the same values of the by expressions. Then the specified aggregation functions are computed over each group, producing a row for each group. The result contains the by columns and also at least one column for each computed aggregate. (Some aggregation functions return multiple columns.)

The result has as many rows as there are distinct combinations of by values. If you want to summarize over ranges of numeric values, use bin() to reduce ranges to discrete values.

Notes

Although you can provide arbitrary expressions for both the aggregation and grouping expressions, it's more efficient to use simple column names, or apply bin() to a numeric column.

The automatic hourly bins for datetime columns is no longer supported. Use explicit binning instead - for example summarize by bin(timestamp, 1h).

List of aggregation functions

Function Description
any() Returns random non-empty value for the group
arg_max() Returns one or more expressions when argument is maximized
arg_min() Returns one or more expressions when argument is minimized
avg() Returns average value across the group
buildschema() Returns the minimal schema that admits all values of the dynamic input
count() Returns count of the group
countif() Returns count with the predicate of the group
dcount() Returns approximate distinct count of the group elements
make_bag() Returns a property bag of dynamic values within the group
make_list() Returns a list of all the values within the group
make_set() Returns a set of distinct values within the group
max() Returns the maximum value across the group
min() Returns the minimum value across the group
percentiles() Returns the percentile approximate of the group
stdev() Returns the standard deviation across the group
sum() Returns the sum of the elements withing the group
variance() Returns the variance across the group

Aggregates default values

The following table summarizes the default values of aggregations

Operator Default value
count(), countif(), dcount(), dcountif() 0
make_set(), make_list() empty dynamic array ([])
any(), arg_max(). arg_min(), avg(), buildschema(), hll(), max(), min(), percentiles(), stdev(), sum(), sumif(), tdigest(), variance() null

In addition, when using these aggregates over entities which includes null values, the null values will be ignored and won't participate in the calculation (See examples below).

Examples

alt text

Example

Determine what unique combinations of ActivityType and CompletionStatus there are in a table. Note that there are no aggregation functions, just group-by keys. The output will just show the columns for those results:

Activities | summarize by ActivityType, completionStatus
ActivityType completionStatus
dancing started
singing started
dancing abandoned
singing completed

Example

Finds the minimum and maximum timestamp of all records in the Activities table. There is no group-by clause, so there is just one row in the output:

Activities | summarize Min = min(Timestamp), Max = max(Timestamp)
Min Max
1975-06-09 09:21:45 2015-12-24 23:45:00

Example

Create a row for each continent, showing a count of the cities in which activities occur. Because there are few values for "continent", no grouping function is needed in the 'by' clause:

Activities | summarize cities=dcount(city) by continent
cities continent
4290 Asia
3267 Europe
2673 North America

Example

The following example calculates a histogram for each activity type. Because Duration has many values, we use bin to group its values into 10-minute intervals:

Activities | summarize count() by ActivityType, length=bin(Duration, 10m)
count_ ActivityType length
354 dancing 0:00:00.000
23 singing 0:00:00.000
2717 dancing 0:10:00.000
341 singing 0:10:00.000
725 dancing 0:20:00.000
2876 singing 0:20:00.000
...

Examples for the aggregates default values

When the input of summarize operator that has at least one group-by key is empty then it's result is empty too.

When the input of summarize operator that doesn't have any group-by key is empty, then the result is the default values of the aggregates used in the summarize:

range x from 1 to 10 step 1
| where 1 == 2
| summarize any(x), argmax(x, x), argmin(x, x), avg(x), buildschema(todynamic(tostring(x))), max(x), min(x), percentile(x, 55), hll(x) ,stdev(x), sum(x), sumif(x, x > 0), tdigest(x), variance(x)


any_x max_x max_x_x min_x min_x_x avg_x schema_x max_x1 min_x1 percentile_x_55 hll_x stdev_x sum_x sumif_x tdigest_x variance_x
range x from 1 to 10 step 1
| where 1 == 2
| summarize  count(x), countif(x > 0) , dcount(x), dcountif(x, x > 0)
count_x countif_ dcount_x dcountif_x
0 0 0 0
range x from 1 to 10 step 1
| where 1 == 2
| summarize  make_set(x), make_list(x)
set_x list_x
[] []

The aggregate avg sums all the non-nulls and counts only those which participated in the calculation (will not take nulls into account).

range x from 1 to 2 step 1
| extend y = iff(x == 1, real(null), real(5))
| summarize sum(y), avg(y)
sum_y avg_y
5 5

The regular count will count nulls:

range x from 1 to 2 step 1
| extend y = iff(x == 1, real(null), real(5))
| summarize count(y)
count_y
2
range x from 1 to 2 step 1
| extend y = iff(x == 1, real(null), real(5))
| summarize make_set(y), make_set(y)
set_y set_y1
[5.0] [5.0]