Custom Grouping

Overview

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.fingerprint is used as-is without any modification.
Errors from the same service and with the same error.fingerprint attribute are grouped into the same issue.
Errors with different service attributes are grouped into different issues.

Setup

Custom grouping only needs an error span and an error.fingerprint string span tag.

If you aren’t already collecting APM traces with Datadog, see the APM documentation to set up APM.

Example

If you’re already sending APM spans, add a new error.fingerprint tag to your error span.

Here’s an example in Python:

with tracer.trace("throws.an.error") as span:
  span.set_tag('error.fingerprint', 'my-custom-grouping-material')
  raise Exception("Something went wrong")

Exception information is captured and attached to a span if there is one active when the exception is raised. In this case, my-custom-grouping-material is used to group these error spans into a single issue in Error Tracking.

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.

Example

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.

In order to use custom grouping, you need the Datadog Browser SDK v4.42.0 or later, a browser RUM error, and an additional string attribute.

If you aren’t already collecting Browser RUM events with Datadog, see the Browser Monitoring setup documentation.

Example

If you’re already collecting browser errors, it’s possible to add the attribute by either:

Adding a dd_fingerprint attribute to the error object:

import { datadogRum } from '@datadog/browser-rum';
// Send a custom error with context
const error = new Error('Something went wrong');
error.dd_fingerprint = 'my-custom-grouping-fingerprint'
datadogRum.addError(error);

Or, using the beforeSend callback with an error.fingerprint attribute:

DD_RUM.init({
  ...
  beforeSend: () => {
    if (event.type === 'error') {
      event.error.fingerprint = 'my-custom-grouping-fingerprint'
    }
  },
})

In both cases, my-custom-grouping-material is used to group the Browser RUM errors into a single issue in Error Tracking.