summarize operator

• Article
• 03/23/2023

In this article

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

Syntax

T | summarize [ SummarizeParameters ]
[[Column =] Aggregation [, …]]
[by
[Column =] GroupExpression [, …]]

Parameters

Name
Type
Required
Description

Column
string

The name for the result column. Defaults to a name derived from the expression.

Aggregation
string
✓
A call to an aggregation function such as count() or avg(), with column names as arguments.

GroupExpression
scalar
✓
A scalar expression that can reference the input data. The output will have as many records as there are distinct values of all the group expressions.

SummarizeParameters
string

Zero or more space-separated parameters in the form of Name = Value that control the behavior. See supported parameters.

Note

When the input table is empty, the output depends on whether GroupExpression
is used:

• If GroupExpression is not provided, the output will be a single (empty) row.
• If GroupExpression is provided, the output will have no rows.

Supported parameters

Name
Description

hint.num_partitions
Specifies the number of partitions used to share the query load on cluster nodes. See shuffle query
hint.shufflekey=<key>
The shufflekey query shares the query load on cluster nodes, using a key to partition data. See shuffle query
hint.strategy=shuffle
The shuffle strategy query shares the query load on cluster nodes, where each node will process one partition of the data. See shuffle query

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
(which may be zero). If there are no group keys provided, the result has a single
record.

To summarize over ranges of numeric values, use bin() to reduce ranges to discrete values.

Note

• 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).

Aggregates default values

The following table summarizes the default values of aggregations:

Operator
Default value

count(), countif(), dcount(), dcountif()
0

make_bag(), make_bag_if(), make_list(), make_list_if(), make_set(), make_set_if()
empty dynamic array ([])

All others
null

When using these aggregates over entities that includes null values, the null values will be ignored and won’t participate in the calculation (see examples below).

Examples

Unique combination

The following query determines what unique combinations of State and EventType there are for storms that resulted in direct injury. There are no aggregation functions, just group-by keys. The output will just show the columns for those results.

StormEvents
| where InjuriesDirect > 0
| summarize by State, EventType

Output

The following table shows only the first 5 rows. To see the full output, run the query.

State
EventType

TEXAS
Thunderstorm Wind

TEXAS
Flash Flood

TEXAS
Winter Weather

TEXAS
High Wind

TEXAS
Flood

…
…

Finds the minimum and maximum heavy rain storms in Hawaii. There’s no group-by clause, so there’s just one row in the output.

StormEvents
| where State == "HAWAII" and EventType == "Heavy Rain"
| project Duration = EndTime - StartTime
| summarize Min = min(Duration), Max = max(Duration)

Output

Min
Max

01:08:00
11:55:00

Distinct count

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:

StormEvents
| summarize TypesOfStorms=dcount(EventType) by State
| sort by TypesOfStorms

Output

The following table shows only the first 5 rows. To see the full output, run the query.

State
TypesOfStorms

TEXAS
27

CALIFORNIA
26

PENNSYLVANIA
25

GEORGIA
24

ILLINOIS
23

…
…

Histogram

The following example calculates a histogram storm event types that had storms lasting longer than 1 day. Because Duration has many values, use bin() to group its values into 1-day intervals.

StormEvents
| project EventType, Duration = EndTime - StartTime
| where Duration > 1d
| summarize EventCount=count() by EventType, Length=bin(Duration, 1d)
| sort by Length

Output

EventType
Length
EventCount

Drought
30.00:00:00
1646

Wildfire
30.00:00:00
11

Heat
30.00:00:00
14

Flood
30.00:00:00
20

Heavy Rain
29.00:00:00
42

…
…
…

Aggregates default values

When the input of summarize operator has at least one empty group-by key, its result is empty, too.

When the input of summarize operator doesn’t have an empty group-by key, the result is the default values of the aggregates used in the summarize:

datatable(x:long)[]
| summarize any_x=take_any(x), arg_max_x=arg_max(x, *), arg_min_x=arg_min(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)

Output

any_x
arg_max_x
arg_min_x
avg_x
schema_x
max_x
min_x
percentile_x_55
hll_x
stdev_x
sum_x
sumif_x
tdigest_x
variance_x

NaN

0
0
0

0

The result of avg_x(x) is NaN due to dividing by 0.

datatable(x:long)[]
| summarize  count(x), countif(x > 0) , dcount(x), dcountif(x, x > 0)

Output

count_x
countif_
dcount_x
dcountif_x

0
0
0
0

datatable(x:long)[]
| summarize  make_set(x), make_list(x)

Output

set_x
list_x

[]
[]

The aggregate avg sums all the non-nulls and counts only those which participated in the calculation (won’t 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)

Output

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)

Output

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)

Output

set_y
set_y1

[5.0]
[5.0]