Splunk Inc.

Sonya Chang

Justin Pearson Smith

Sarah Matarese

Clark Eugene Mullen

Alexandra Victoria Nuttbrown

Danika Patrick

Amy Katherine Hunnel Bianchi

Patrick Lin

Park Kittipatkul

Harsh Vardhan Vashistha

Joseph Ari Ross

Maxime Petazzoni

Sahinaz Safari Sanjani

A system can display content generated from one type of machine-generated data to a user via a graphical user interface. Based on an interaction with the machine-generated data, the system can determine an entity identifier associated with the machine-generated data and determine an entity type for the entity identifier. The system can map the entity type to one or more content generators associated with the entity type and communicate the entity identifier to the identified content generators. The content generators can determine if they have content associated with the machine-generated data. The system can generate and display a link to the related content via a graphical user interface.

Related content identification for different types of machine-generated data

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.

Application performance monitoring (APM) detectors for flagging application performance alerts

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San Mateo, CA, United States of America

Sonya Chang