Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of partitions containing records spanning a time interval, wherein the records include a set of values for a metric and a set of dimensions associated with the set of values. For each dimensional subset associated with the set of dimensions, the system aggregates, based on the set of partitions, a corresponding subset of values for the metric into a distribution-based representation of the metric. The system then stores a dictionary encoding of the dimensional subset in association with the distribution-based representation of the metric. Finally, the system calculates, based on the distribution-based representation, one or more quantiles associated with the dimensional subset and outputs the quantile(s) for use in characterizing a distribution of the metric.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of partitions containing records spanning a time interval, wherein the records include a set of values for a metric and a set of dimensions associated with the set of values. For each dimensional subset associated with the set of dimensions, the system aggregates, based on the set of partitions, a corresponding subset of values for the metric into a distribution-based representation of the metric. The system then stores a dictionary encoding of the dimensional subset in association with the distribution-based representation of the metric. Finally, the system calculates, based on the distribution-based representation, one or more quantiles associated with the dimensional subset and outputs the quantile(s) for use in characterizing a distribution of the metric.

Abstract: Various embodiments include a method and system for high-volume distributed script error report handling. A group of user terminals may be selected to report errors, where the group of user terminals is a subset of user terminals having open user session and the group of user terminals is smaller than a predetermined threshold value. An error report, generated by a user terminal in response to a script error, may then be received, where the user terminal is in the group of user terminals selected to report errors. The received error report may then be processed to create a processed error report. The processed error report may then be presented to a service user.

Abstract: Various embodiments include a method and system for high-volume distributed script error report handling. A group of user terminals may be selected to report errors, where the group of user terminals is a subset of user terminals having open user session and the group of user terminals is smaller than a predetermined threshold value. An error report, generated by a user terminal in response to a script error, may then be received, where the user terminal is in the group of user terminals selected to report errors. The received error report may then be processed to create a processed error report. The processed error report may then be presented to a service user.

Abstract: A system for processing data is provided. During operation, the system obtains a current window of one or more intervals of timeseries data collected from a monitored system. Next, the system continuously performs a statistical hypothesis test that compares the one or more intervals of the time-series data with baseline values from historic time-series data associated with the monitored system. When the statistical hypothesis test indicates a deviation of the time-series data from the baseline values, the system outputs an alert of an anomaly represented by the deviation.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of components of a time-series performance metric associated with an anomaly in a performance of one or more monitored systems. For each component in the set of components, the system performs a statistical hypothesis test on the component to assess a deviation of the component from a baseline value of the component. When the statistical hypothesis test identifies a statistically significant deviation of the component from the baseline value, the system outputs an alert comprising a root cause of the anomaly that is represented by the statistically significant deviation of the component from the baseline value.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a component of a time-series performance metric associated with a server-side root cause of an anomaly in the time-series performance metric. Next, the system obtains a call graph representation of the component, wherein the call graph representation includes a parent node having a parent value of the component and a set of child nodes of the parent node, each child node having a corresponding child value of the component. The system then analyzes the call graph representation to identify one or more of the child nodes as sources of the anomaly. Finally, the system outputs an alert that identifies the sources of the anomaly.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of metrics related to a performance of a single-page application (SPA), wherein the set of metrics includes an initial page load time of the SPA that is calculated from a page load event and a render completion time and a subsequent page load time of the SPA that is calculated from a transition time and the render completion time. Next, the system uses the set of metrics to display a graphical user interface (GUI) comprising a chart of a performance of the SPA. The system then displays, in the chart, a representation of the page load time for the SPA.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a first set of metrics generated during loading of a single-page application (SPA) in a web browser, wherein the first set of metrics includes a page load event and a render completion time. Next, the system uses the first set of metrics to calculate an initial page load time for the SPA. The system also obtains a second set of metrics generated during loading of a subsequent view in the SPA, wherein the second set of metrics includes a transition time and the render completion time. The system then uses the second set of metrics to calculate a subsequent page load time for the SPA. Finally, the system outputs the first and second sets of metrics, the initial page load time, and the subsequent page load time for use in analyzing a performance of the SPA.

Abstract: Computerized techniques are described for detecting performance degradation during features ramp up. A first version of web content is sent to first client devices. The first version of the web content lacks a particular feature. First performance data that indicates performance of each first client device with respect to processing the web content is received. A second version of the web content is sent to second client devices. The second version of the web content has the particular feature. Second performance data that indicates performance of each second client device with respect to processing the web content is received. The first performance data is compared with the second performance data to determine a difference between a processing metric associated with the first version of the web content and a processing metric associated with the second version of the web content.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a first set of metrics generated during loading of a single-page application (SPA) in a web browser, wherein the first set of metrics includes a page load event and a render completion time. Next, the system uses the first set of metrics to calculate an initial page load time for the SPA. The system also obtains a second set of metrics generated during loading of a subsequent view in the SPA, wherein the second set of metrics includes a transition time and the render completion time. The system then uses the second set of metrics to calculate a subsequent page load time for the SPA. Finally, the system outputs the first and second sets of metrics, the initial page load time, and the subsequent page load time for use in analyzing a performance of the SPA.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of metrics related to a performance of a single-page application (SPA), wherein the set of metrics includes an initial page load time of the SPA that is calculated from a page load event and a render completion time and a subsequent page load time of the SPA that is calculated from a transition time and the render completion time. Next, the system uses the set of metrics to display a graphical user interface (GUI) comprising a chart of a performance of the SPA. The system then displays, in the chart, a representation of the page load time for the SPA.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a current window of one or more intervals of time-series data collected from a monitored system. Next, the system continuously performs a statistical hypothesis test that compares the one or more intervals of the time-series data with baseline values from historic time-series data associated with the monitored system. When the statistical hypothesis test indicates a deviation of the time-series data from the baseline values, the system outputs an alert of an anomaly represented by the deviation.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a component of a time-series performance metric associated with a server-side root cause of an anomaly in the time-series performance metric. Next, the system obtains a call graph representation of the component, wherein the call graph representation includes a parent node having a parent value of the component and a set of child nodes of the parent node, each child node having a corresponding child value of the component. The system then analyzes the call graph representation to identify one or more of the child nodes as sources of the anomaly. Finally, the system outputs an alert that identifies the sources of the anomaly.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of components of a time-series performance metric associated with an anomaly in a performance of one or more monitored systems. For each component in the set of components, the system performs a statistical hypothesis test on the component to assess a deviation of the component from a baseline value of the component. When the statistical hypothesis test identifies a statistically significant deviation of the component from the baseline value, the system outputs an alert comprising a root cause of the anomaly that is represented by the statistically significant deviation of the component from the baseline value.

Abstract: Computerized techniques are described for detecting performance degradation during features ramp up. A first version of web content is sent to first client devices. The first version of the web content lacks a particular feature. First performance data that indicates performance of each first client device with respect to processing the web content is received. A second version of the web content is sent to second client devices. The second version of the web content has the particular feature. Second performance data that indicates performance of each second client device with respect to processing the web content is received. The first performance data is compared with the second performance data to determine a difference between a processing metric associated with the first version of the web content and a processing metric associated with the second version of the web content.

Abstract: Computerized techniques are described for detecting performance degradation during features ramp up. A first version of web content is sent to first client devices. The first version of the web content lacks a particular feature. First performance data that indicates performance of each first client device with respect to processing the web content is received. A second version of the web content is sent to second client devices. The second version of the web content has the particular feature. Second performance data that indicates performance of each second client device with respect to processing the web content is received. The first performance data is compared with the second performance data to determine a difference between a processing metric associated with the first version of the web content and a processing metric associated with the second version of the web content.

Abstract: Computerized techniques are described for detecting performance degradation during features ramp up. A first version of web content is sent to first client devices. The first version of the web content lacks a particular feature. First performance data that indicates performance of each first client device with respect to processing the web content is received. A second version of the web content is sent to second client devices. The second version of the web content has the particular feature. Second performance data that indicates performance of each second client device with respect to processing the web content is received. The first performance data is compared with the second performance data to determine a difference between a processing metric associated with the first version of the web content and a processing metric associated with the second version of the web content.

Abstract: Various embodiments include a method and system for high-volume distributed script error report handling. A group of user terminals may be selected to report errors, where the group of user terminals is a subset of user terminals having open user session and the group of user terminals is smaller than a predetermined threshold value. An error report, generated by a user terminal in response to a script error, may then be received, where the user terminal is in the group of user terminals selected to report errors. The received error report may then be processed to create a processed error report. The processed error report may then be presented to a service user.

Abstract: Various embodiments include a method and system for high-volume distributed script error report handling. An error report group threshold may be obtained. The error report group threshold may be determined by available computing resources, at an error facility, to receive error reports. A subset of current user terminals with representative user terminal environments may be identified. The subset may have a cardinality less than the error report group threshold. Members of the subset may be notified to report errors for a distributed program.