Splunk Inc.

Cisco Technology, Inc.

Steven Karis

Mayank Agarwal

Maxime Petazzoni

Steven Flanders

Amin Moshgabadi

Scott Stewart

Matthew William Pound

Charles Smith

Rajesh Raman

Gergely Danyi

Siri Singamneni

Joshua Vertes

Jared Breeden

Ankit Bhagat

Joseph Ari Ross

Eric Wohlstadter

Rebecca Tortell

Chengyu Yang

Dmitrii Anoshin

Sarah Matarese

Hema Krishnamurthy Mohan

Brian Krueger

Erik Oscar Riiska

Justin Smith

In order to extract more contextualized metrics associated with a particular service in a microservices-based application, a method of aggregating metrics comprises analyzing tags of spans for each trace and, based on a first predetermined set of rules, extracting one or more tags pre-selected by a user of the monitoring service. A pre-selected tag may appear in one or more spans associated with a particular service in a trace and may be extracted as a numeric metric. The method comprises computing a set of metrics for the particular service using the numeric metric extracted from the one or more spans of the trace. The method also comprises determining a set of aggregated metrics over the selected time duration for the particular service by aggregating the set of metrics over multiple traces.

Extracting contextualized metrics from spans in a microservices-based architecture

Systems and methods are described for generation of a query using a non-textual input. For example, the query can be generated using a point and click input. A selection of a data source can be identified and an initial query can be automatically generated based on the selection of the data source. A graphical user interface can be displayed and populated with one or more selectable parameters based on the initial query. A selection of the one or more selectable parameters can be received as a non-textual input and a query can be automatically generated based on the selection. For example, a query for execution by a data intake and query system can be generated based on the selection. The query can be provided to the data intake and query system. The data intake and query system may then execute the query on a set of data.

Generation of queries using non-textual input

A method of generating metrics data associated with a microservices-based application comprises ingesting a plurality of spans and mapping an ingested span of the plurality of spans to a span identity, wherein the span identity comprises a tuple of information identifying a type of span associated with the span identity, wherein the tuple of information comprises user-configured dimensions. The method further comprises grouping the ingested span by the span identity, wherein the ingested span is grouped with other spans from the plurality of spans comprising a same span identity. The method also comprises computing metrics associated with the span identity and using the metrics to generate a stream of metric data associated with the span identity.

Generating span related metric data streams by an analytic engine

Automatic generation of queries using non-textual input

Systems and methods are described for generation of queries for execution by a separate system. In order establish a connection with the separate system, credentials can be obtained. For example, the credentials may be based on a user identifier and/or a login identifier. Indices can be identified that correspond to the credentials and a query can be identified that includes a selection of at least one of the indices. For example, the query may identify a set of log data ingested and indexed by the separate system. A request that includes the query, the credentials, and a connection identifier can be communicated to the separate system. In response to the request, a set of data can be received from the separate system. The set of data can be provided to a computing device. For example, the set of data can be provided to a computing device providing the query.

Generation of queries for execution at a separate system

Monitoring and troubleshooting tools provide the capability to visualize different levels of a client's application that is deployed as a suite of independent but cooperating services (e.g., an application that includes a monolithic application and a microservices-based application), collect values of monitored or tracked metrics at those different levels, and visualize values of the metrics at those levels. For example, metrics values can be generated for components of the monolithic application and/or for components of a microservice of the microservice-based application.

Generating metrics values at component levels of a monolithic application and of a microservice of a microservices-based architecture

According to embodiments, a method for virtual partitioning of data includes receiving a data stream comprising a plurality of traces, each trace comprising a plurality of spans from a plurality of users. The method also includes assigning the plurality of traces of the data stream to a plurality of virtual partitions based on each user of the plurality of users, each virtual partition of the plurality of virtual partitions comprising data of a user of the plurality of users. The method also includes scheduling at least a subset of the plurality of virtual partitions to at least one user partition of a shared topic, the at least one user partition comprising data from at least one virtual partition of at least one user of the plurality of users. The method also includes indexing each user partition of the shared topic based on each user and each virtual partition.

Virtual partitioning of a shared message bus

A method of analyzing a performance of a microservices-based application comprises generating a plurality of traces from a plurality of spans associated with the microservices-based application. The method also comprises generating a plurality of data sets each associated with a respective analysis mode of a plurality of analysis modes using the plurality of traces, wherein each analysis mode extracts a different level of detail for analyzing the performance of the services in the application from the plurality of spans. Further, the method comprises selecting, based on a first user query, a first analysis mode from the plurality of analysis modes for generating a response to the first user query. The method also comprises accessing a data set of the plurality of data sets that is associated with the first analysis mode and generating the response to the first user query using the data set associated with the first analysis mode.

Multiple modes of storing and querying trace data in a microservices-based architecture

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

Monitoring and troubleshooting tools provide the capability to visualize different levels of a client's application that is deployed as a suite of independent but cooperating services (e.g., as microservices of a microservices-based architecture), collect values of monitored or tracked metrics at those different levels, and visualize values of the metrics at those levels. For example, metrics values can be generated for teams of the microservices.

Generating metrics values for teams of microservices of a microservices-based architecture

Systems and methods are described for generating metrics from real-time streaming log data. In order to generate the metrics, a metricization rule associated with the log data can be obtained. For example, the metricization rule may be obtained from a user. The metricization rule may include one or more field-value pairs that define how the metrics are generated from the log data. Preview metric data can be generated by applying the metricization rule to the log data. For example, the preview metric data may be displayed via a user interface. Further, the metricization rule can be accepted or approved by the user. Further, the additional log data can be ingested and based on determining that the metricization rule has been accepted, metric data may be generated by applying the metricization rule to the additional log data.

Generating metric data from log data using metricization rules

Systems and methods are described for providing a user interface for display of metric data. A plurality of graphical controls can be displayed in the user interface to modify the generation of the metric data. For example, a first graphical control may enable a user to specify a processing criterion and a second graphical control may enable a user to specify an aggregation criterion and a grouping criterion. Based on the aggregation criterion, aggregated data can be generated from data processed using the processing criterion. The grouping criterion can be used to extract metric data from the aggregated data and a preview of the metric data can be displayed in the user interface.

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Redwood City, CA, United States of America

Steven Karis