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This processor generates either a count metric of logs that match a query or a distribution metric of a numeric value contained in the logs, such as a request duration.
To set up the processor:
Click Manage Metrics to create new metrics or edit existing metrics. This opens a side panel.
You can generate these types of metrics for your logs. See the Metrics Types and Distributions documentation for more details.
Metric type | Description | Example |
---|---|---|
COUNT | Represents the total number of event occurrences in one time interval. This value can be reset to zero, but cannot be decreased. | You want to count the number of logs with status:error . |
GAUGE | Represents a snapshot of events in one time interval. | You want to measure the latest CPU utilization per host for all logs in the production environment. |
DISTRIBUTION | Represent the global statistical distribution of a set of values calculated across your entire distributed infrastructure in one time interval. | You want to measure the average time it takes for an API call to be made. |
You can generate these types of metrics for your logs. See the Metrics Types and Distributions documentation for more details.
Metric type | Description | Example |
---|---|---|
COUNT | Represents the total number of event occurrences in one time interval. This value can be reset to zero, but cannot be decreased. | You want to count the number of logs with status:error . |
GAUGE | Represents a snapshot of events in one time interval. | You want to measure the latest CPU utilization per host for all logs in the production environment. |
DISTRIBUTION | Represent the global statistical distribution of a set of values calculated across your entire distributed infrastructure in one time interval. | You want to measure the average time it takes for an API call to be made. |
Each processor has a corresponding filter query in their fields. Processors only process logs that match their filter query. And for all processors except the filter processor, logs that do not match the query are sent to the next step of the pipeline. For the filter processor, logs that do not match the query are dropped.
For any attribute, tag, or key:value
pair that is not a reserved attribute, your query must start with @
. Conversely, to filter reserved attributes, you do not need to append @
in front of your filter query.
For example, to filter out and drop status:info
logs, your filter can be set as NOT (status:info)
. To filter out and drop system-status:info
, your filter must be set as NOT (@system-status:info)
.
Filter query examples:
NOT (status:debug)
: This filters for only logs that do not have the status DEBUG
.status:ok service:flask-web-app
: This filters for all logs with the status OK
from your flask-web-app
service.status:ok AND service:flask-web-app
.host:COMP-A9JNGYK OR host:COMP-J58KAS
: This filter query only matches logs from the labeled hosts.@user.status:inactive
: This filters for logs with the status inactive
nested under the user
attribute.Learn more about writing filter queries in Datadog’s Log Search Syntax.