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Use Observability Pipelines’ processors to parse, structure, and enrich your logs. All processors are available for all templates. Set up your processors in the Observability Pipelines UI after you have selected a template, source, and destinations. This is step 5 in the pipeline setup process:
There are pre-selected processors added to your processor group out of the box. You can add additional processors or delete any existing ones based on your processing needs.
Processor groups are executed from top to bottom. The order of the processors is important because logs are checked by each processor, but only logs that match the processor’s filters are processed. To modify the order of the processors, use the drag handle on the top left corner of the processor you want to move.
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.
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