Default Grouping
Error Tracking intelligently groups similar errors into issues. This grouping is based on the following error properties:
service: The service where the error occurred.error.typeorerror.kind: The class of the error.error.message: A description of the error.error.stack: The filename and function name of the top-most meaningful stack frame.
The error stack trace is the code path followed by an error between being thrown and being captured by Datadog instrumentation. Error Tracking evaluates the topmost stack frame (the location of the error) and uses it to group the error.
If any stack frame properties differ for two given errors, the two errors are grouped under different issues. For example, Error Tracking does not group issues across services or error types. Error Tracking also ignores numbers, punctuation, and anything that is between quotes or parentheses: only word-like tokens are used.
Tip: To ensure optimal grouping, enclose variables in your error messages in quotes or parentheses.
Note: To improve grouping accuracy, Error Tracking removes variable stack frame properties such as versions, ids, dates, and so on.
Custom Grouping
Error Tracking intelligently groups similar errors into issues with a default strategy. By using custom fingerprinting, you can gain full control over the grouping decision and customize the grouping behavior for your error spans.
You can customize grouping by providing an
error.fingerprint for the error. The fingerprint is provided in
an attribute or tag, depending on the error source (see
Setup for details). While the value of
error.fingerprint does not have any particular format or
requirement, the content must be a string.
If error.fingerprint is provided, the grouping behavior follows
these rules:
- Custom grouping takes precedence over the default strategy.
- Custom grouping can be applied only to a subset of your errors and can coexist with the default strategy.
- The content of
error.fingerprintis used as-is without any modification (although it is converted to a standardized fingerprint format). - Errors from the same service and with the same
error.fingerprintattribute are grouped into the same issue. - Errors with different
serviceattributes are grouped into different issues.
Setup
Custom grouping only needs an error log and an
error.fingerprint string attribute.
If you aren't already collecting logs with Datadog, see the Log Management documentation to set up logs.
Ensure that the source tag (specifying language) is properly
configured.
Server
If you're already logging in JSON format, add a new
error.fingerprint attribute to your error log.
Here's an example in Python for a JSON-formatted logger:
import logging
import json_log_formatter
formatter = json_log_formatter.JSONFormatter()
json_handler = logging.FileHandler(filename='/var/log/my-log.json')
json_handler.setFormatter(formatter)
logger = logging.getLogger('my_json')
logger.addHandler(json_handler)
logger.setLevel(logging.INFO)
logger.error('Error processing request', extra={'error.fingerprint': 'my-custom-grouping-material'})
In this case, my-custom-grouping-material is used to group
these error logs into a single issue in Error Tracking.
Android
To use custom grouping, you need the Datadog Android SDK
2.7.0 or higher.
In Datadog's mobile SDKs, you can add a custom error fingerprint when logging an error by adding a predefined attribute to the log call:
val errorFingerprint = "my-custom-grouping-material"
val attributes = mapOf(LogAttributes.ERROR_FINGERPRINT to errorFingerprint)
logger.e("My error message", error, attributes)
Or, you can add or adjust the fingerprint in the log mapper:
val mapper = object : EventMapper<LogEvent> {
override fun map(event: LogEvent): LogEvent {
event.fingerprint = "my-custom-grouping-material"
return event
}
}
val logsConfiguration = LogsConfiguration.Builder()
.setEventMapper(mapper)
.build()
Logs.enable(logsConfiguration)
Flutter
To use custom grouping, you need the Datadog Flutter SDK
2.4.0 or higher.
In Datadog's mobile SDKs, you can add a custom error fingerprint when logging an error by adding a predefined attribute to the log call:
final errorFingerprint = "my-custom-grouping-material";
logger.error(
'My error message',
errorStackTrace: st,
attributes {
DatadogAttributes.errorFingerprint: "my-custom-grouping-material",
}
);
Or, you can add or adjust the fingerprint in the log mapper:
LogEvent? mapLogEvent(LogEvent event) {
event.error?.fingerprint = "my-custom-grouping-material";
return event;
}
final loggingConfiguration = DatadogLoggingConfiguration(
eventMapper: mapLogEvent,
);
final configuration = DatadogConfiguration(
// ...
loggingConfiguration: loggingConfiguration,
);
iOS
To use custom grouping, you need the Datadog iOS SDK
2.8.1 or higher.
In Datadog's mobile SDKs, you can add a custom error fingerprint when logging an error by adding a predefined attribute to the log call:
let errorFingerprint = "my-custom-grouping-material"
logger.error(
"My error message",
error: error,
attributes: [
Logs.Attributes.errorFingerprint: errorFingerprint
]
)
Or, you can add or adjust the fingerprint in the log mapper:
let logsConfiguration = Logs.Configuration(
eventMapper: { log in
var log = log
log.error?.fingerprint = "my-custom-grouping-material"
return log
}
)
Logs.enable(
with: logsConfiguration
)
React Native
To use custom grouping, you need the Datadog RUM SDK
2.4.2 or higher.
In Datadog's mobile SDKs, you can add a custom error fingerprint when logging an error by adding a predefined attribute to the log call:
DdLogs.error(
'message',
'my-error-type',
'my-error-message',
'my-stack-trace',
{ my: 'context' },
'my-custom-fingerprint'
);
Or, you can add or adjust the fingerprint in the log mapper:
configuration.errorEventMapper = event => {
event.fingerprint = 'my-custom-fingerprint'
return event;
};