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This topic describes metrics that LaunchDarkly autogenerates from observability events. The LaunchDarkly observability session replay SDK plugins provide error monitoring and metric collection for errors, web vitals, and document loading in your browser application. The functionality is in separate plugins, which you enable in the initialization options for the LaunchDarkly SDK. The session replay plugins automatically collect and send data to LaunchDarkly, where you can review metrics, events, and errors from your application. The observability events are prefixed with $ld:telemetry and LaunchDarkly automatically generates metrics from these events. These expandable sections explain the metrics that LaunchDarkly autogenerates from events recorded by the observability plugins for LaunchDarkly browser SDKs:
Metric kind: Custom numericSuggested analysis unit: UserDefinition: Average event values per user, then compute the Average of those values where Lower is betterUnits without events: ExcludedDescription: Measures the average largest burst per context of layout shift scores for every unexpected layout shift that occurs during the entire lifecycle of a page.Example usage: Observing the latency of interactions an end user makes with your application
Metric kind: Custom numericSuggested analysis unit: RequestDefinition: Average event values per request, then compute the P95 of those values where Lower is betterUnits without events: ExcludedDescription: Measures the 95th percentile largest burst per context of layout shift scores for every unexpected layout shift that occurs during the entire lifecycle of a page.Example usage: Observing the latency of interactions an end user makes with your application
Metric kind: Custom numericSuggested analysis unit: RequestDefinition: Average event values per request, then compute the P99 of those values where Lower is betterUnits without events: ExcludedDescription: Measures the 99th percentile largest burst per context of layout shift scores for every unexpected layout shift that occurs during the entire lifecycle of a page.Example usage: Observing the latency of interactions an end user makes with your application
Metric kind: Custom numericSuggested analysis unit: UserDefinition: Average event values per user, then compute the Average of those values where Lower is betterUnits without events: ExcludedDescription: Measures the average DOM load duration in milliseconds per contextExample usage: Observing the latency of interactions an end user makes with your application
Metric kind: Custom numericSuggested analysis unit: RequestDefinition: Average event values per request, then compute the P95 of those values where Lower is betterUnits without events: ExcludedDescription: Measures the 95th percentile DOM load duration in milliseconds per contextExample usage: Observing the latency of interactions an end user makes with your application
Metric kind: Custom numericSuggested analysis unit: RequestDefinition: Average event values per request, then compute the P99 of those values where Lower is betterUnits without events: ExcludedDescription: Measures the 99th percentile DOM load duration in milliseconds per contextExample usage: Observing the latency of interactions an end user makes with your application
Metric kind: Custom conversion binarySuggested analysis unit: UserDefinition: Percent of user units that sent the event where Lower is betterUnits without events: Include units and set the value to 0Description: Measures the percentage of contexts that encountered an error at least once. This metric is autogenerated by an initial $ld:telemetry:session:init event and populated by subsequent $ld:telemetry:error events. This means you can use the metric even if your app has not yet generated any errors.Example usage: Running a guarded rollout to make sure the error change doesn’t result in a higher error rate
Metric kind: Custom numericSuggested analysis unit: UserDefinition: Average event values per user, then compute the Average of those values where Lower is betterUnits without events: ExcludedDescription: Measures the average time in milliseconds per context between first navigation to a page and when any part of the page’s content is rendered.Example usage: Observing the latency of interactions an end user makes with your application
Metric kind: Custom numericSuggested analysis unit: RequestDefinition: Average event values per request, then compute the P95 of those values where Lower is betterUnits without events: ExcludedDescription: Measures the 95th percentile time in milliseconds per context between first navigation to a page and when any part of the page’s content is rendered.Example usage: Observing the latency of interactions an end user makes with your application
Metric kind: Custom numericSuggested analysis unit: RequestDefinition: Average event values per request, then compute the P99 of those values where Lower is betterUnits without events: ExcludedDescription: Measures the 99th percentile time in milliseconds per context between first navigation to a page and when any part of the page’s content is rendered.Example usage: Observing the latency of interactions an end user makes with your application
Metric kind: Custom numericSuggested analysis unit: UserDefinition: Average event values per user, then compute the Average of those values where Lower is betterUnits without events: ExcludedDescription: Measures the average time in milliseconds per context between a user’s first interaction (click, tap, or key press) and the time when the browser starts processing event handlers in response to that interaction.Example usage: Observing the latency of interactions an end user makes with your application
Metric kind: Custom numericSuggested analysis unit: RequestDefinition: Average event values per request, then compute the P95 of those values where Lower is betterUnits without events: ExcludedDescription: Measures the 95th percentile time, in milliseconds per context, between a user’s first interaction (click, tap, or key press) and the time when the browser starts processing event handlers in response to that interaction.Example usage: Observing the latency of interactions an end user makes with your application
Metric kind: Custom numericSuggested analysis unit: RequestDefinition: Average event values per request, then compute the P99 of those values where Lower is betterUnits without events: ExcludedDescription: Measures the 99th percentile time, in milliseconds per context, between a user’s first interaction (click, tap, or key press) and the time when the browser starts processing event handlers in response to that interaction.Example usage: Observing the latency of interactions an end user makes with your application
Metric kind: Custom numericSuggested analysis unit: UserDefinition: Average event values per user, then compute the Average of those values where Lower is betterUnits without events: ExcludedDescription: Measures the average response time in milliseconds per context of all click, tap, and keyboard interactions during the lifespan of a visit to a page.Example usage: Observing the latency of interactions an end user makes with your application
Metric kind: Custom numericSuggested analysis unit: RequestDefinition: Average event values per request, then compute the P95 of those values where Lower is betterUnits without events: ExcludedDescription: Measures the 95th percentile response time in milliseconds per context of all click, tap, and keyboard interactions during the lifespan of a visit to a page.Example usage: Observing the latency of interactions an end user makes with your application
Metric kind: Custom numericSuggested analysis unit: RequestDefinition: Average event values per request, then compute the P99 of those values where Lower is betterUnits without events: ExcludedDescription: Measures the 99th percentile response time in milliseconds per context of all click, tap, and keyboard interactions during the lifespan of a visit to a page.Example usage: Observing the latency of interactions an end user makes with your application
Metric kind: Custom numericSuggested analysis unit: UserDefinition: Average event values per user, then compute the Average of those values where Lower is betterUnits without events: ExcludedDescription: Measures the average time in milliseconds per context to render the largest image, text block, or video visible when first navigating to a pageExample usage: Observing the latency of interactions an end user makes with your application
Metric kind: Custom numericSuggested analysis unit: RequestDefinition: Average event values per request, then compute the P95 of those values where Lower is betterUnits without events: ExcludedDescription: Measures the 95th percentile time in milliseconds per context to render the largest image, text block, or video visible when first navigating to a pageExample usage: Observing the latency of interactions an end user makes with your application
Metric kind: Custom numericSuggested analysis unit: RequestDefinition: Average event values per request, then compute the P99 of those values where Lower is betterUnits without events: ExcludedDescription: Measures the 99th percentile time in milliseconds per context to render the largest image, text block, or video visible when first navigating to a pageExample usage: Observing the latency of interactions an end user makes with your application
Metric kind: Custom numericSuggested analysis unit: UserDefinition: Average event values per user, then compute the Average of those values where Lower is betterUnits without events: ExcludedDescription: Measures the average time in milliseconds per context between the request for a resource and when the first byte of a response begins to arrive.Example usage: Observing the latency of interactions an end user makes with your application
Metric kind: Custom numericSuggested analysis unit: RequestDefinition: Average event values per request, then compute the P95 of those values where Lower is betterUnits without events: ExcludedDescription: Measures the 95th percentile time in milliseconds per context between the request for a resource and when the first byte of a response begins to arrive.Example usage: Observing the latency of interactions an end user makes with your application
Metric kind: Custom numericSuggested analysis unit: RequestDefinition: Average event values per request, then compute the P99 of those values where Lower is betterUnits without events: ExcludedDescription: Measures the 99th percentile time in milliseconds per context between the request for a resource and when the first byte of a response begins to arrive.Example usage: Observing the latency of interactions an end user makes with your application