Abstract: A method of computing real-time metrics for automated workflows includes aggregating a set of ingested spans into a set of traces. The method further includes executing a set of rules to determine a set of workflows associated with the set of traces, wherein each workflow of the set of workflows is associated with a respective trace of the set of traces, and wherein each workflow is operable to group together activity associated with a client process within a respective trace. The method also includes assigning a name to each workflow based on the rules and computing real-time metrics for each of the workflows.

Abstract: A method of identifying a root cause of a failure for a trace within a microservices-based application includes determining if a root span of the trace is an error span resulting in an error experienced by a user at a front end of the microservices-based application. If the root span of the trace is an error span, the method analyzes a plurality of spans comprising the trace to determine if the trace comprises at least one leaf error span. If the trace comprises a single leaf error span, the method attributes the root cause of the failure in the trace to a service associated with the single leaf error span. If the trace comprises multiple leaf error spans the method attributes the root cause of the failure in the trace to a service associated with a leaf error span of the multiple leaf error spans comprising a latest starting timestamp.

Abstract: A method of diagnosing anomalous patterns from metrics data associated with a microservices-based application comprises aggregating a plurality of ingested spans into a plurality of streams of metric data. The method also comprises performing computations on a stream of metric data from the plurality of streams of metric data to identify an anomalous pattern. Further, the method comprises generating an alert in response to the anomalous pattern and querying a data set using metadata associated with the alert to retrieve additional information pertaining to the anomalous pattern.