Splunk Inc.

Gergely Danyi

Justin Pearson Smith

Steven Flanders

Mayank Agarwal

Maxime Petazzoni

Joseph Ari Ross

Eric Wohlstadter

Steven Karis

Scott Stewart

Chengyu Yang

Timothy Matthew Robin Williamson

Sahinaz Safari Sanjani

Sakshi Garg

Khawar Deen

John Bennett Wundes

Rashmi Kalyani Vasudevan

Umang Agarwal

Joshua Johnson

Anusha Konatala

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.

Configuring automated workflows for application performance monitoring

A performance monitoring system (PMS) displays a list of example URLs that matched a URL grouping rule used to group URLs. For a rule configured for a customer of the PMS, the example matched URLs are selected by the PMS from a candidate set of URLs identified from data associated with that customer. The PMS receives information identifying a Uniform Resource Locator (URL) grouping rule displayed in a graphical user interface (GUI). The PMS identified a list of candidate URLs occurring in the stored data. The PMS then identifies, from the list of candidate URLs, a set of matched URLs, the set of matched URLs including one or more URLs from the list of candidate URLs that matched the URL grouping rule. The PMS then causes at least one URL from the set of matched URLs to be displayed on the GUI.

Techniques for showing matched URLs for a URL grouping rule

A method of tracking errors in a system comprising microservices comprises ingesting a plurality of spans generated by the microservices during a given duration of time. The method further comprises consolidating the plurality of spans associated with the given duration of time into a plurality of traces, wherein each trace comprises a subset of the plurality of spans that comprise a common trace identifier. For each trace, the method comprises: a) mapping a respective trace to one or more error stacks computed for the respective trace and to one or more attributes determined for the respective trace; and b) emitting each error stack computed from the respective trace with an associated pair of attributes. The method then comprises reducing duplicate pairs of error stack and associated attributes and maintaining a count for each pair of error stack and associated attributes.

In a microservices-based application, tracking errors by mapping traces to error stacks

A method of normalizing URLs associated with a real user session comprises extracting uniform resource locators (URLs) from ingested spans where at least a portion of the URLs comprise unique URL strings. The method also comprises decomposing each of the URLs into a sequence of tokens and grouping together subsets of related URLs. Also, the method comprises representing each subset of related URLs with a normalized URL string.

URL normalization for rendering a service graph

A computer-implemented method for analyzing spans and traces associated with a microservices-based application executing in a distributed computing environment comprises aggregating a plurality of ingested spans associated with one or more applications executing in the distributed computing environment into a plurality of traces, wherein each of the plurality of ingested spans is associated with a plurality of tags. The method further comprises comparing durations of a set of related traces of the plurality of traces to determine patterns for the plurality of tags and generating a histogram that represents a distribution of the durations of the set of related traces. The method also comprises providing alerts for one or more tags from the plurality of tags associated with traces having a duration above a threshold based on the distribution of the durations.

Configuring alerts for tags associated with high-latency and error spans for instrumented software

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.

Identifying the root cause of failure observed in connection to a workflow

A method of performing error analysis in a system comprising microservices comprises identifying a root cause error span from among a plurality of error spans for a trace, wherein an error span is a span that returns an error to a microservice that generates the span, and wherein a root cause error span is an error span associated with an error originating microservice. The method further comprises determining a call path associated with the root cause error span, where the call path comprises a chain of spans starting at the root cause error span, and where each subsequent span in the chain is a parent span of a prior span. Subsequently the method comprises mapping each span in the chain to a span error frame to create an error stack and rendering an image of the error stack.

Displaying error stacks in a graphical user interface (GUI) to track error propagation across microservices-based applications

A method of rendering a graphical user interface (GUI) comprising an application topology graph for a microservice architecture comprises generating a plurality of traces from a first plurality of spans generated by instrumented services in the architecture and generating generate a second plurality of spans for uninstrumented services using information extracted from the first plurality of spans. The method further comprises grouping the second plurality of spans with the plurality of traces. Subsequently, the method comprises traversing the traces and collecting a plurality of span pairs from the plurality of traces, wherein each pair of the span pairs is associated with a call between two services. The method also comprises aggregating information across the plurality of span pairs to reduce duplicative information associated with multiple occurrences of a same span pair from the plurality of span pairs. Finally, the method comprises rendering the application topology graph using the aggregated information.

Application topology graph for representing uninstrumented objects in a microservices-based architecture

URL normalization for rendering a service graph and aggregating metrics associated with a real user session

Application topology graph for representing instrumented and uninstrumented objects in a microservices-based architecture

Analyzing tags associated with high-latency and error spans for instrumented software

Tracking error propagation across microservices-based applications

In a microservices-based application, mapping distributed error stacks across multiple dimensions

Graphical user interface (GUI) for representing instrumented and uninstrumented objects in a microservices-based architecture

A method of performing error analysis in a system comprising microservices comprises identifying a root cause error span from among a plurality of error spans for a trace associated with a user-request, wherein an error span is a span that returns an error to a microservice initiating a call resulting in the span, and wherein a root cause error span is an error span associated with an error originating microservice. The method further comprises determining a call path associated with the root cause error span, where the call path comprises a chain of spans starting at the root cause error span, and where each subsequent span in the chain is a parent span of a prior span. Subsequently the method comprises mapping each span in the chain to a span error frame to create an error stack and rendering an image of the error stack.

Growing community of inventors

Redwood City, CA, United States of America

Gergely Danyi