Microsoft Technology Licensing, LLC

Mayank Agarwal

Alexander Dadiomov

Xinhua Ji

Matthew L Jacobs

James Rapp

Daniel Griffing

Ben Nesson

Drake A Campbell

Paulo Cesar Sales Janotti

Hazim Shafi

Vikram Padmakar Bapat

Alexey Kamenev

Eric Ledoux

Ryan A Nowak

Jared Robert Van Leeuwen

George Essex Englebeck

One or more systems and/or techniques for generating an adaptive tree structure for storing event data based upon data density of events are disclosed. In particular, the adaptive tree structure may comprise one or more levels of nodes, where a level may correspond to a resolution of events. Nodes may correspond to particular time spans over which event data was recorded. A node may be designated as a raw node comprising raw events or a summary node comprising summary events based upon the number of events occurring within a time span covered by the node.

Adaptive tree structure for visualizing data

The present invention extends to methods, systems, and computer program products for linking diagnostic visualizations to regions of application code. Diagnostic visualizations emitted during execution of an application are displayed. The diagnostic visualizations partially represent the abstract objective of the application (e.g., as envisioned by a developer). Diagnostic data for at least one of a plurality of components is displayed. The diagnostic data indicates the performance of the at least one of the plurality of components during execution of the application. The displayed one or more diagnostic visualizations and the displayed diagnostic data is correlated to link the one or more diagnostic visualizations to the at least one of the plurality of components. Linking the one or more diagnostic visualizations to the at least one of the plurality of components can better indicate how the application's behavior reconciles the abstract objective.

Linking diagnostic visualizations to application code

The present invention extends to methods, systems, and computer program products for calculating zoom level timeline data. Event aggregators are configured to read execution data from sequentially received input buffers and pre-calculate data chunks for a specified zoom level to create a zoom tree. The density of execution data can be used to determine when pre-calculation is beneficial. In response to a user request, pre-calculated data can be combined with responsively calculated data for presentation to a user. User requests can specify zoom levels and time ranges over which data is to be presented.

Calculating zoom level timeline data

The construction or use of a hierarchical time tree that represents an execution time of a program. To construct the hierarchical time tree, the time frame corresponding to the root node is the execution time during which the plurality of call stack samples were gathered from the program. Beginning at the root node, and proceeding recursively, each node is then populated in a manner that all of the call stacks for a given time period need not be accessed in order to provide a report regarding the given time period.

Dynamic calculation of sample profile reports

The present invention extends to methods, systems, and computer program products for time-based navigation within resource utilization data. A computer system is configured to present resource utilization data representing performance of computer resources. The resource utilization data is displayed on a diagnostic data trace during the execution of the application. The user can select a desired time range and the resource utilization data within the time range will be displayed at other traces. The diagnostic data trace is still presented so that the user can understand the relation between the selected time range and the overall time length. Further, the user can modify the selected time range by change the extents of the selected time range using resizing tool. The resource utilization data within the modified selected time range, similarly, is also displayed along with the diagnostic data trace.

Time-based navigation within resource utilization data

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Redmond, WA, United States of America

Mayank Agarwal