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: 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: Techniques are provided for automatically accounting and apportioning of observed service execution times. In an embodiment, the computers determine a total amount of time to process a request. The computers determine which services are called in order to process the request. The called services include a first service and a second service. The computers determine a first amount of time that the first service spends in response to processing the request and a second amount of time that the second service spends in response to processing the request. The computers calculate a first percentage for the first service based on the first amount of time and the total amount of time and a second percentage for the second service based on the second amount of time and the total amount of time.

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: Techniques are provided for analyzing client-side performance data. In one technique, first client performance data and second client performance data is received. The first client performance data was generated by a first client device and indicates a first plurality of processing items that includes a first processing item that is associated with first item performance data. The second client performance data was generated by a second client device and indicates a second plurality of processing items that includes a second processing item that is associated with second item performance data. In response to determining that the first processing item is associated with the second processing item, an aggregate data value is computed based on the first item performance data and the second item performance data.

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 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: 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: 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: 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.