Setup Feature Flag Tracking

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Feature flag data provides greater visibility into user experience and performance monitoring. It allows you to determine which users are being shown a specific feature and assess if any changes introduced are impacting user experience or negatively affecting performance.

By enriching your RUM data with feature flag data, you can be confident that your feature successfully launches without unintentionally causing a bug or performance regression. With this additional layer of insight, you can correlate feature releases with performance, pinpoint issues to specific releases, and troubleshoot faster.

Set up RUM monitoring

Feature flag tracking is available in the RUM Browser, iOS, Android, Flutter, and React Native SDK.

To enable feature flag data collection for the Browser SDK:

Set up RUM browser monitoring. You need the Browser RUM SDK version >= 4.25.0.

Initialize the RUM SDK and configure the enableExperimentalFeatures initialization parameter with ["feature_flags"].

npm

  import { datadogRum } from '@datadog/browser-rum';

  // Initialize Datadog Browser SDK
  datadogRum.init({
    ...
    enableExperimentalFeatures: ["feature_flags"],
    ...
});

CDN async

window.DD_RUM.onReady(function() {
    window.DD_RUM.init({
      ...
      enableExperimentalFeatures: ["feature_flags"],
      ...
    })
})

CDN sync

window.DD_RUM &&
    window.DD_RUM.init({
      ...
      enableExperimentalFeatures: ["feature_flags"],
      ...
    })

To enable feature flag data collection for your iOS application:

Set up RUM iOS monitoring. You need the iOS RUM SDK version >= 1.16.0.

To enable feature flag data collection for your Android application:

Set up RUM Android monitoring. You need the Android RUM SDK version >= 1.18.0.

To enable feature flag data collection for your Flutter application:

Set up RUM Flutter monitoring. You need the Flutter Plugin version >= 1.3.2.

To enable feature flag data collection for your React Native application:

Set up RUM React Native monitoring. You need the React Native RUM SDK version >= 1.7.0.

Set up a feature flag integration

You can start collecting feature flag data with custom feature flag management solutions, or by using one of Datadog’s integration partners listed below.

Note: The following special characters are not supported for Feature Flag Tracking: ., :, +, -, =, &&, ||, >, <, !, (, ), {, }, [, ], ^, ", “, ”, ~, *, ?, \. Datadog recommends avoiding these characters when possible in your feature flag names. If you are required to use one of these characters, replace the character before sending the data to Datadog. For example:

datadogRum.addFeatureFlagEvaluation(key.replace(':', '_'), value);

Amplitude integration

Before you initialize this feature flag integration, make sure you’ve set up RUM monitoring.

Initialize Amplitude’s SDK and create an exposure listener reporting feature flag evaluations to Datadog using the following snippet of code:

For more information about initializing Amplitude’s SDK, see Amplitude’s JavaScript SDK documentation.

  const experiment = Experiment.initialize("CLIENT_DEPLOYMENT_KEY", {
    exposureTrackingProvider: {
      track(exposure: Exposure)  {
        // Send the feature flag when Amplitude reports the exposure
        datadogRum.addFeatureFlagEvaluation(exposure.flag_key, exposure.variant);
      }
    }
  })

Initialize Amplitude’s SDK and create an inspector reporting feature flag evaluations to Datadog using the snippet of code below.

For more information about initializing Amplitude’s SDK, see Amplitude’s iOS SDK documentation.

  class DatadogExposureTrackingProvider : ExposureTrackingProvider {
    func track(exposure: Exposure) {
      // Send the feature flag when Amplitude reports the exposure
      if let variant = exposure.variant {
        RUMMonitor.shared().addFeatureFlagEvaluation(name: exposure.flagKey, value: variant)
      }
    }
  }

  // In initialization:
  ExperimentConfig config = ExperimentConfigBuilder()
    .exposureTrackingProvider(DatadogExposureTrackingProvider(analytics))
    .build()

Initialize Amplitude’s SDK and create an inspector reporting feature flag evaluations to Datadog using the snippet of code below.

For more information about initializing Amplitude’s SDK, see Amplitude’s Android SDK documentation.

  internal class DatadogExposureTrackingProvider : ExposureTrackingProvider {
    override fun track(exposure: Exposure) {
        // Send the feature flag when Amplitude reports the exposure
        GlobalRumMonitor.get().addFeatureFlagEvaluation(
            exposure.flagKey,
            exposure.variant.orEmpty()
        )
    }
  }

  // In initialization:
  val config = ExperimentConfig.Builder()
      .exposureTrackingProvider(DatadogExposureTrackingProvider())
      .build()

Amplitude does not support this integration. Create a ticket with Amplitude to request this feature.

ConfigCat integration

Before you initialize this feature flag integration, make sure you’ve set up RUM monitoring.

When initializing the ConfigCat Javascript SDK, subscribe to the flagEvaluated event and report feature flag evaluations to Datadog:

const configCatClient = configcat.getClient(
  '#YOUR-SDK-KEY#',
  configcat.PollingMode.AutoPoll,
  {
    setupHooks: (hooks) =>
      hooks.on('flagEvaluated', (details) => {
        datadogRum.addFeatureFlagEvaluation(details.key, details.value);
      })
  }
);

For more information about initializing the ConfigCat Javascript SDK, see ConfigCat’s JavaScript SDK documentation.

When initializing the ConfigCat Swift iOS SDK, subscribe to the flagEvaluated event and report feature flag evaluations to Datadog:

  let client = ConfigCatClient.get(sdkKey: "#YOUR-SDK-KEY#") { options in
    options.hooks.addOnFlagEvaluated { details in
        RUMMonitor.shared().addFeatureFlagEvaluation(featureFlag: details.key, variation: details.value)
    }
  }

For more information about initializing the ConfigCat Swift (iOS) SDK, see ConfigCat’sSwift iOS SDK documentation.

When initializing the ConfigCat Android SDK, subscribe to the flagEvaluated event and report feature flag evaluations to Datadog:

  ConfigCatClient client = ConfigCatClient.get("#YOUR-SDK-KEY#", options -> {
    options.hooks().addOnFlagEvaluated(details -> {
        GlobalRumMonitor.get().addFeatureFlagEvaluation(details.key, details.value);
    });
  });

For more information about initializing the ConfigCat Android SDK, see ConfigCat’s Android SDK documentation.

When initializing the ConfigCat Dart SDK, subscribe to the flagEvaluated event and report feature flag evaluations to Datadog:

  final client = ConfigCatClient.get(
    sdkKey: '#YOUR-SDK-KEY#',
    options: ConfigCatOptions(
        pollingMode: PollingMode.autoPoll(),
        hooks: Hooks(
            onFlagEvaluated: (details) => {
              DatadogSdk.instance.rum?.addFeatureFlagEvaluation(details.key, details.value);
            }
        )
    )
  );

For more information about initializing the ConfigCat Dart (Flutter) SDK, see ConfigCat’s Dart SDK documentation.

When initializing the ConfigCat React SDK, subscribe to the flagEvaluated event and report feature flag evaluations to Datadog:

<ConfigCatProvider
  sdkKey="YOUR_SDK_KEY"
  pollingMode={PollingMode.AutoPoll}
  options={{
    setupHooks: (hooks) =>
      hooks.on('flagEvaluated', (details) => {
        DdRum.addFeatureFlagEvaluation(details.key, details.value);
      }),
  }}
>
  ...
</ConfigCatProvider>

For more information about initializing the ConfigCat React SDK, see ConfigCat’s React SDK documentation.

Custom feature flag management

Before you initialize a custom feature flag integration, make sure you’ve set up RUM monitoring.

Each time a feature flag is evaluated, add the following function to send the feature flag information to RUM:

datadogRum.addFeatureFlagEvaluation(key, value);

Each time a feature flag is evaluated, add the following function to send the feature flag information to RUM:

RUMMonitor.shared().addFeatureFlagEvaluation(key, value);

Each time a feature flag is evaluated, add the following function to send the feature flag information to RUM:

GlobalRumMonitor.get().addFeatureFlagEvaluation(key, value);

Each time a feature flag is evaluated, add the following function to send the feature flag information to RUM:

DatadogSdk.instance.rum?.addFeatureFlagEvaluation(key, value);

Each time a feature flag is evaluated, add the following function to send the feature flag information to RUM:

DdRum.addFeatureFlagEvaluation(key, value);

DevCycle integration

Before you initialize this feature flag integration, make sure you’ve set up RUM monitoring.

Initialize DevCycle’s SDK and subscribe to the variableEvaluated event, choosing to subscribe to all variable evaluations variableEvaluated:* or particular variable evaluations variableEvaluated:my-variable-key.

For more information about initializing DevCycle’s SDK, see DevCycle’s JavaScript SDK documentation and for more information about DevCycle’s event system, see DevCycle’s SDK Event Documentation.

const user = { user_id: "<USER_ID>" };
const dvcOptions = { ... };
const dvcClient = initialize("<DVC_CLIENT_SDK_KEY>", user, dvcOptions);
...
dvcClient.subscribe(
    "variableEvaluated:*",
    (key, variable) => {
        // track all variable evaluations
        datadogRum.addFeatureFlagEvaluation(key, variable.value);
    }
)
...
dvcClient.subscribe(
    "variableEvaluated:my-variable-key",
    (key, variable) => {
        // track a particular variable evaluation
        datadogRum.addFeatureFlagEvaluation(key, variable.value);
    }
)

DevCycle does not support this integration. Create a ticket with DevCycle to request this feature.

Eppo integration

Before you initialize this feature flag integration, make sure you’ve set up RUM monitoring.

Initialize Eppo’s SDK and create an assignment logger that additionally reports feature flag evaluations to Datadog using the snippet of code shown below.

For more information about initializing Eppo’s SDK, see Eppo’s JavaScript SDK documentation.

const assignmentLogger: IAssignmentLogger = {
  logAssignment(assignment) {
    datadogRum.addFeatureFlagEvaluation(assignment.featureFlag, assignment.variation);
  },
};

await eppoInit({
  apiKey: "<API_KEY>",
  assignmentLogger,
});

Initialize Eppo’s SDK and create an assignment logger that additionally reports feature flag evaluations to Datadog using the snippet of code shown below.

For more information about initializing Eppo’s SDK, see Eppo’s iOS SDK documentation.

func IAssignmentLogger(assignment: Assignment) {
  RUMMonitor.shared().addFeatureFlagEvaluation(featureFlag: assignment.featureFlag, variation: assignment.variation)
}

let eppoClient = EppoClient(apiKey: "mock-api-key", assignmentLogger: IAssignmentLogger)

Initialize Eppo’s SDK and create an assignment logger that additionally reports feature flag evaluations to Datadog using the snippet of code shown below.

For more information about initializing Eppo’s SDK, see Eppo’s Android SDK documentation.

AssignmentLogger logger = new AssignmentLogger() {
    @Override
    public void logAssignment(Assignment assignment) {
      GlobalRumMonitor.get().addFeatureFlagEvaluation(assignment.getFeatureFlag(), assignment.getVariation());
    }
};

EppoClient eppoClient = new EppoClient.Builder()
    .apiKey("YOUR_API_KEY")
    .assignmentLogger(logger)
    .application(application)
    .buildAndInit();

Eppo does not support this integration. Contact Eppo to request this feature.

Initialize Eppo’s SDK and create an assignment logger that additionally reports feature flag evaluations to Datadog using the snippet of code shown below.

For more information about initializing Eppo’s SDK, see Eppo’s React native SDK documentation.

const assignmentLogger: IAssignmentLogger = {
  logAssignment(assignment) {
    DdRum.addFeatureFlagEvaluation(assignment.featureFlag, assignment.variation);
  },
};

await eppoInit({
  apiKey: "<API_KEY>",
  assignmentLogger,
});

Flagsmith Integration

Before you initialize this feature flag integration, make sure you’ve set up RUM monitoring.

Initialize Flagsmith’s SDK with the datadogRum option, which reports feature flag evaluations to Datadog using the snippet of code shown below.

Optionally, you can configure the client so that Flagsmith traits are sent to Datadog via datadogRum.setUser(). For more information about initializing Flagsmith’s SDK, check out Flagsmith’s JavaScript SDK documentation.

 // Initialize the Flagsmith SDK
 flagsmith.init({
     datadogRum: {
         client: datadogRum,
         trackTraits: true,
     },
     ...
 })

Flagsmith does not support this integration. Create a ticket with Flagsmith to request this feature.

Flagsmith does not currently support this integration. Create a ticket with Flagsmith to request this feature.

LaunchDarkly integration

Before you initialize this feature flag integration, make sure you’ve set up RUM monitoring.

Initialize LaunchDarkly’s SDK and create an inspector reporting feature flags evaluations to Datadog using the snippet of code shown below.

For more information about initializing LaunchDarkly’s SDK, see LaunchDarkly’s JavaScript SDK documentation.

const client = LDClient.initialize("<CLIENT_SIDE_ID>", "<CONTEXT>", {
  inspectors: [
    {
      type: "flag-used",
      name: "dd-inspector",
      method: (key: string, detail: LDClient.LDEvaluationDetail) => {
        datadogRum.addFeatureFlagEvaluation(key, detail.value);
      },
    },
  ],
});

LaunchDarkly does not support this integration. Create a ticket with LaunchDarkly to request this feature.

LaunchDarkly does not currently support this integration. Create a ticket with LaunchDarkly to request this feature.

Split Integration

Before you initialize this feature flag integration, make sure you’ve set up RUM monitoring.

Initialize Split’s SDK and create an impression listener reporting feature flag evaluations to Datadog using the following snippet of code:

For more information about initializing Split’s SDK, see Split’s JavaScript SDK documentation.

const factory = SplitFactory({
    core: {
      authorizationKey: "<APP_KEY>",
      key: "<USER_ID>",
    },
    impressionListener: {
      logImpression(impressionData) {
          datadogRum
              .addFeatureFlagEvaluation(
                  impressionData.impression.feature,
                  impressionData.impression.treatment
              );
    },
  },
});

const client = factory.client();

Initialize Split’s SDK and create an inspector reporting feature flag evaluations to Datadog using the snippet of code below.

For more information about initializing Split’s SDK, see Split’s iOS SDK documentation.

  let config = SplitClientConfig()
  // Send the feature flag when Split reports the impression
  config.impressionListener = { impression in
      if let feature = impression.feature,
          let treatment = impression.treatment {
          RUMMonitor.shared().addFeatureFlagEvaluation(name: feature, value: treatment)
      }
  }

Initialize Split’s SDK and create an inspector reporting feature flag evaluations to Datadog using the snippet of code below.

For more information about initializing Split’s SDK, see Split’s Android SDK documentation.

  internal class DatadogSplitImpressionListener : ImpressionListener {
    override fun log(impression: Impression) {
        // Send the feature flag when Split reports the impression
        GlobalRumMonitor.get().addFeatureFlagEvaluation(
            impression.split(),
            impression.treatment()
        )
    }
    override fun close() {
    }
  }

  // In initialization:
  val apikey = BuildConfig.SPLIT_API_KEY
  val config = SplitClientConfig.builder()
      .impressionListener(DatadogSplitImpressionListener())
      .build()

Initialize Split’s SDK and create an inspector reporting feature flag evaluations to Datadog using the snippet of code below.

For more information about initializing Split’s SDK, see Split’s Flutter plugin documentation.

  StreamSubscription<Impression> impressionsStream = _split.impressionsStream().listen((impression) {
    // Send the feature flag when Split reports the impression
    final split = impression.split;
    final treatment = impression.treatment;
    if (split != null && treatment != null) {
      DatadogSdk.instance.rum?.addFeatureFlagEvaluation(split, treatment);
    }
  });

Initialize Split’s SDK and create an impression listener reporting feature flag evaluations to Datadog using the following snippet of code:

For more information about initializing Split’s SDK, see Split’s React Native SDK documentation.

const factory = SplitFactory({
    core: {
      authorizationKey: "<APP_KEY>",
      key: "<USER_ID>",
    },
    impressionListener: {
      logImpression(impressionData) {
          DdRum
              .addFeatureFlagEvaluation(
                  impressionData.impression.feature,
                  impressionData.impression.treatment
              );
    },
  },
});

const client = factory.client();

Statsig Integration

Before you initialize this feature flag integration, make sure you’ve set up RUM monitoring.

Initialize Statsig’s SDK with statsig.initialize.

Update your Browser RUM SDK version 4.25.0 or above.
Initialize the RUM SDK and configure the enableExperimentalFeatures initialization parameter with ["feature_flags"].

Initialize Statsig’s SDK (>= v4.34.0) and implement the gateEvaluationCallback option as shown below:

 await statsig.initialize('client-<STATSIG CLIENT KEY>',
 {userID: '<USER ID>'},
 {
     gateEvaluationCallback: (key, value) => {
         datadogRum.addFeatureFlagEvaluation(key, value);
     }
 }
 );