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Applies to: ✅ Azure Data Explorer ✅ Azure Monitor ✅ Microsoft Sentinel
Groups a set of strings together based on value similarity.
For each such group, the operator returns a pattern
, count
, and representative
. The pattern
best describes the group, in which the *
character represents a wildcard. The count
is the number of values in the group, and the representative
is one of the original values in the group.
Syntax
T |
reduce
[kind
=
ReduceKind] by
Expr [with
[threshold
=
Threshold] [,
characters
=
Characters]]
Learn more about syntax conventions.
Parameters
Name | Type | Required | Description |
---|---|---|---|
Expr | string |
✔️ | The value by which to reduce. |
Threshold | real |
A value between 0 and 1 that determines the minimum fraction of rows required to match the grouping criteria in order to trigger a reduction operation. The default value is 0.1. The threshold parameter determines the minimum level of similarity required for values to be grouped together. With a smaller threshold value (closer to 0), more similar values are grouped together, resulting in fewer but more similar groups. A larger threshold value (closer to 1) requires less similarity, resulting in more groups that are less similar. We recommend setting a small threshold value for large inputs. See Examples. |
|
Characters | string |
A list of characters that separate between terms. The default is every non-ascii numeric character. For examples, see Examples. | |
ReduceKind | string |
The only valid value is source . If source is specified, the operator appends the Pattern column to the existing rows in the table instead of aggregating by Pattern . |
Returns
A table with as many rows as there are groups and columns titled pattern
, count
, and representative
. The pattern
best describes the group, in which the *
character represents a wildcard, or placeholder for an arbitrary insertion string. The count
is the number of values in the group, and the representative
is one of the original values in the group.
For example, the result of reduce by city
might include:
Pattern | Count | Representative |
---|---|---|
San * | 5182 | San Bernard |
Saint * | 2846 | Saint Lucy |
Moscow | 3726 | Moscow |
* -on- * | 2730 | One -on- One |
Paris | 2716 | Paris |
Examples
The example in this section shows how to use the syntax to help you get started.
The examples in this article use publicly available tables in the help cluster, such as the
StormEvents
table in the Samples database.
The following example generates a range of numbers, creates a new column with concatenated strings and random integers, and then groups the rows by the new column with specific reduction parameters. The threshold is set to 0.001, which means that the operator groups values that are very similar to each other.
range x from 1 to 1000 step 1
| project MyText = strcat("MachineLearningX", tostring(toint(rand(10))))
| reduce by MyText with threshold=0.001 , characters = "X"
Output
Pattern | Count | Representative |
---|---|---|
MachineLearning* | 1000 | MachineLearningX4 |
The following example generates a range of numbers, creates a new column with concatenated strings and random integers, and then groups the rows by the new column with specific reduction parameters. The threshold is set to 0.9, which means that the operator groups together values less strictly and allows more variance.
range x from 1 to 1000 step 1
| project MyText = strcat("MachineLearningX", tostring(toint(rand(10))))
| reduce by MyText with threshold=0.9 , characters = "X"
Output
The result includes only those groups where the MyText value appears in at least 90% of the rows.
Pattern | Count | Representative |
---|---|---|
MachineLearning* | 177 | MachineLearningX9 |
MachineLearning* | 102 | MachineLearningX0 |
MachineLearning* | 106 | MachineLearningX1 |
MachineLearning* | 96 | MachineLearningX6 |
MachineLearning* | 110 | MachineLearningX4 |
MachineLearning* | 100 | MachineLearningX3 |
MachineLearning* | 99 | MachineLearningX8 |
MachineLearning* | 104 | MachineLearningX7 |
MachineLearning* | 106 | MachineLearningX2 |
If the Characters parameter is unspecified, by default the operator treats all non-alphanumeric characters (including spaces and punctuation) as term separators.
The following example shows how the reduce
operator behaves when the Characters parameter isn't specified.
range x from 1 to 10 step 1 | project str = strcat("foo", "Z", tostring(x)) | reduce by str
Output
Pattern | Count | Representative |
---|---|---|
others | 10 |
However, if you specify that "Z" is a separator, then it's as if each value in str
is two terms: foo
and tostring(x)
:
range x from 1 to 10 step 1 | project str = strcat("foo", "Z", tostring(x)) | reduce by str with characters="Z"
Output
Pattern | Count | Representative |
---|---|---|
foo* | 10 | fooZ1 |
The following example shows how one might apply the reduce
operator to a "sanitized"
input, in which GUIDs in the column being reduced are replaced before reducing:
Start with a few records from the Trace table. Then reduce the Text column which includes random GUIDs. As random GUIDs interfere with the reduce operation, replace them all by the string "GUID". Now perform the reduce operation. In case there are other "quasi-random" identifiers with embedded '-' or '_' characters in them, treat characters as non-term-breakers.
Trace
| take 10000
| extend Text = replace(@"[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}", "GUID", Text)
| reduce by Text with characters="-_"
Related content
Note
The implementation of reduce
operator is largely based on the paper A Data Clustering Algorithm for Mining Patterns From Event Logs, by Risto Vaarandi.