LogReduce Operator
The summarize operator has been renamed the LogReduce operator, to match the LogReduce button on the Messages tab. Both operators will continue to work in search queries as synonyms for a limited time. We recommend that you rewrite saved queries replacing summarize with logreduce.
The LogReduce® algorithm uses fuzzy logic to group messages together based on string and pattern similarity. You can use the logreduce operator to quickly assess activity patterns for things like a range of devices or traffic on a website. Focus the LogReduce algorithm on an area of interest by defining that area in the keyword expression.
There are two ways to use the operator.
- Use the LogReduce button displayed on the results table after running a search.
- Manually add the operator to your query following its syntax.
For information on how to interpret and influence the outcome of LogReduce results, see Detecting Patterns with LogReduce and Influencing the LogReduce Outcome.
LogReduce button
When you've already run a search query with non-aggregate results, you can use the LogReduce button in the Messages tab to automatically apply the LogReduce operator to the current results.
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Run a search query with non-aggregate results.
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In the Messages tab, the LogReduce button displays. Click it to automatically apply the LogReduce operator to your results.
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The Signatures tab is displayed with your results.
Rules
- The logreduce operator cannot be used with group-by operators such as "count by field".
Syntax
... | logreduce [field<field>] [by<byField>] [limit<limit>] [, criteria<criteria>]
Parameter | Description |
---|---|
field | The field to group by similarity. If no field is provided the raw message is used. |
byField | Field to group signatures by. Results are returned aggregated. |
limit | Limits the number of signatures returned. The total number of signatures involved in a search query can be overwhelming, making final results hard to digest and comprehend. Use this parameter to limit the number of returned signatures. |
criteria | By default, LogReduce tries to find the most anomalous signatures. The criteria parameter can override the default criteria to either of the following values:
|
Details option
Using the details option launches a new query adding a unique signature ID that allows you to view the logs grouped under that signature. The signature ID is not available to run this manually, you will need to use the web interface.
After running a LogReduce operation, from the Signatures tab, you can view logs grouped together in a signature. To see the raw log data from signatures the operator provides the details option. You can view details in two ways:
- Click the number in the Count column for a signature.
- Check the checkboxes in the Select column for any number of signatures and click the View Details button on the top right of the table.
Details option syntax:
... | logreduce | details <signatureId>
Once a LogReduce query has run with the details operator, you cannot use that query again, for example, in a separate Search tab.
Optimize option
LogReduce Optimize is a scaled-out version of LogReduce that groups logs by existing signatures. You need to prerun LogReduce on new data sources to create its signatures. In many cases, the optimized version provides up to 10x speedup over classic LogReduce on datasets with hundreds of thousands of logs. The only caveat with the optimized version is that some LogReduce classic features are not supported. The table below compares classic LogReduce with its optimized version based on available features and performance:
Features | LogReduce | LogReduce Optimize |
---|---|---|
Throughput | ~10x logreduce | |
Groups logs by existing signatures | Yes | Yes |
Creates new signatures at runtime | Yes | No |
Displays signature relevance | Yes | No |
Allows interactive feedback (favoriting, disliking, editing, splitting, merging) | Yes | No |
Supports details investigation | Yes | Yes |
Optimize option syntax:
... | logreduce [field<field>] optimize
For example:
_sourceCategory=cloudtrail
| logreduce optimize
_sourceCategory=kubernetes-audit
| json auto
| logreduce field=object optimize
Examples
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First optimization:
_sourceCategory = "Labs/AWS/GuardDuty_V8"
| json keys "resource", "partition", "region"
| logreduce -
Next LogReduce by region:
_sourceCategory = "Labs/AWS/GuardDuty_V8"
| json keys "resource", "partition", "region"
| logreduce(partition) by region limit=5,criteria=mostcommon -
The LogReduce operator can act as an aggregate operator, supporting grouping by
_timeslice
as well as by other dimensions, such as_sourceHost
.... | logreduce by _sourceHost
By grouping by
timeslice
, you can determine how signature counts change over a period of time.... | timeslice 1m | logreduce by _timeslice
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LogReduce by timeslice:
_sourceCategory=MyApp
| timeslice 1m
| logreduce by _timeslice limit=5,criteria=mostcommon
| transpose row _timeslice column signature -
LogReduce by sourceHost:
_sourceCategory=MyApp`
| logreduce by _sourceHost limit=5,criteria=mostcommon
| transpose row _sourceHost column signature