Abstract: An instrumentation analysis system processes data streams received from servers executing instrumented software. The system determines a set of servers that satisfy a given criteria, for example, a set of servers with high resource utilization. The set of servers may be determined by the system based on triggers or specified by a user. The system analyzes properties of servers to determine a property that characterizes the set of servers. The property characterizing the servers is provided to users via a user interface or alerts for further analysis, for example, to analyze the cause of high resource utilization.

Abstract: Described are systems, methods, and techniques for collecting, analyzing, processing, and storing time series data and for evaluating and determining whether and how to include late or delayed data points for inclusion when publishing or storing the time series data. Maximum delay values can identify a duration for waiting for late or delayed data, such as prior to publication. In some examples, maximum delay values can be dynamically adjustable based on a statistical evaluation process. For late or delayed data points that are received after the maximum delay elapses, some data points can be included in the stored time series data, such as if they are received in the same order that they are generated.

Abstract: A method of normalizing URLs associated with a real user session comprises extracting uniform resource locators (URLs) from ingested spans where at least a portion of the URLs comprise unique URL strings. The method also comprises decomposing each of the URLs into a sequence of tokens and grouping together subsets of related URLs. Also, the method comprises representing each subset of related URLs with a normalized URL string.

Abstract: A system receives a time series of data values from instrumented software executing on an external system. Each data value corresponds to a metric of the external system. The system stores a level value representing a current estimate of the time series and a trend value representing a trend in the time series. The level and trend values are based on data in a window having a trailing value. In response to receiving a most recent value, the system updates the level value and the trend value to add an influence of the most recent value and remove an influence of the trailing value. The system forecasts based on the updated level and trend values, and in response to determining that the forecast indicates the potential resource shortage event, takes action.

Abstract: A process for analyzing an incident includes setting up an alert for a high error rate on a particular endpoint. Once the alert is triggered, a set of traces for transactions exhibiting errors on the offending endpoint is queried. All traces for other services/operations that include errors on the offending endpoint are also enumerated. A set of baseline transactions that involve the offending endpoint, but do not result in error may be utilized to determine whether the errors are always present, or are distinctive for certain offending transactions. All traces are ranked based on a statistic. Once the traces have been ranked, they may be traced down to a deepest/most terminal error. A set of transactions that correlate to the terminal error may also be analyzed to determine infrastructure causes.

Abstract: A computer-implemented method for analyzing spans and traces associated with a microservices-based application executing in a distributed computing environment comprises aggregating a plurality of ingested spans associated with one or more applications executing in the distributed computing environment into a plurality of traces, wherein each of the plurality of ingested spans is associated with a plurality of tags. The method further comprises comparing durations of a set of related traces of the plurality of traces to determine patterns for the plurality of tags and generating a histogram that represents a distribution of the durations of the set of related traces. The method also comprises providing alerts for one or more tags from the plurality of tags associated with traces having a duration above a threshold based on the distribution of the durations.

Abstract: Embodiments of the present disclosure provide for trace and span sampling and analysis for instrumented software. Each span may be annotated with one or more tags that provide context about an executed task, such as a user instrumenting the software, a document involved in a request, an infrastructure element used in servicing a request, etc. A sampler may perform tail-based sampling of traces comprising spans. The sampler may select a portion of the traces having selected features and send them to an analyzer. The analyzer may receive the selected traces and determine whether the selected traces are indicative of configuration problems for the instrumented software. An alert may be generated based on identified configuration problems.

Abstract: Embodiments are directed to facilitating identifying seasonal frequencies. In particular, a set of candidate seasonal frequencies associated with a time series data set are determined based on ACF peaks identified in association with a representation of the time series data set. Thereafter, the filters are applied to analyze the candidate seasonal frequencies and update the candidate seasonal frequencies by removing any candidate seasonal frequencies that fail a filter. An example filter can include comparing ACF peaks with peaks associated with SDF peaks. Thereafter, a candidate seasonal frequency of the updated candidate seasonal frequencies can be identified as a seasonal frequency for the time series data set, and such a seasonal frequency can be provided (e.g., to a user or another process) for use in performing data analysis.

Abstract: A method of normalizing URLs associated with a real user session comprises extracting uniform resource locators (URLs) from ingested spans where at least a portion of the URLs comprise unique URL strings. The method also comprises decomposing each of the URLs into a sequence of tokens and grouping together subsets of related URLs. Also, the method comprises representing each subset of related URLs with a normalized URL string.

Abstract: An analysis system receives a time series. The data values of the time series correspond to a metric describing a characteristic of the computing system that changes over time. The analysis system stores a statistic value that represents the stationarity of the time series. In response to receiving a most recent value, the analysis system assigns the most recent value as the leading value in a window before retrieving the trailing value of the window. The analysis system updates the statistic value to add an influence of the most recent value and remove an influence of the trailing value. If the statistic value is less than a threshold, the analysis system determines that the time series is stationary. In response to determining the time series is stationary, the analysis system assigns an alert to the metric. The analysis system detects an anomaly in the metric based on the assigned alert.

Abstract: Described are systems, methods, and techniques for collecting, analyzing, processing, and storing time series data and for evaluating and determining whether and how to include late or delayed data points for inclusion when publishing or storing the time series data. Maximum delay values can identify a duration for waiting for late or delayed data, such as prior to publication. In some examples, maximum delay values can be dynamically adjustable based on a statistical evaluation process. For late or delayed data points that are received after the maximum delay elapses, some data points can be included in the stored time series data, such as if they are received in the same order that they are generated.

Abstract: Described are systems, methods, and techniques for collecting, analyzing, processing, and storing time series data and for evaluating and dynamically estimating a resolution of one or more streams of data points and updating an output resolution. Responsive to receiving a stream of data points, a data resolution can be derived and an output resolution can be set to a first value. When a change to the data resolution is detected, the output resolution can be changed, modifying a frequency at which output data points are generated and/or transmitted. In some instances, a detector can be implemented to trigger an alert responsive to ingested data points corresponding with triggering parameters. An output resolution for the detector can be dynamically modified based on dynamically detecting a change to the data resolution of the stream of data.

Abstract: Described are systems, methods, and techniques for collecting, analyzing, processing, and storing time series data and for evaluating and dynamically estimating a resolution of one or more streams of data points and updating an output resolution. Responsive to receiving a stream of data points, a data resolution can be derived and an output resolution can be set to a first value. When a change to the data resolution is detected, the output resolution can be changed, modifying a frequency at which output data points are generated and/or transmitted. In some instances, a detector can be implemented to trigger an alert responsive to ingested data points corresponding with triggering parameters. An output resolution for the detector can be dynamically modified based on dynamically detecting a change to the data resolution of the stream of data.

Abstract: A method of normalizing URLs associated with a real user session comprises extracting uniform resource locators (URLs) from ingested spans where at least a portion of the URLs comprise unique URL strings. The method also comprises decomposing each of the URLs into a sequence of tokens and grouping together subsets of related URLs. Also, the method comprises representing each subset of related URLs with a normalized URL string.

Abstract: 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.

Abstract: A process for analyzing an incident includes setting up an alert for a high error rate on a particular endpoint. Once the alert is triggered, a set of traces for transactions exhibiting errors on the offending endpoint is queried. All traces for other services/operations that include errors on the offending endpoint are also enumerated. A set of baseline transactions that involve the offending endpoint, but do not result in error may be utilized to determine whether the errors are always present, or are distinctive for certain offending transactions. All traces are ranked based on a statistic. Once the traces have been ranked, they may be traced down to a deepest/most terminal error. A set of transactions that correlate to the terminal error may also be analyzed to determine infrastructure causes.

Abstract: An instrumentation analysis system processes data streams received from servers executing instrumented software. The system determines a set of servers that satisfy a given criteria, for example, a set of servers with high resource utilization. The set of servers may be determined by the system based on triggers or specified by a user. The system analyzes properties of servers to determine a property that characterizes the set of servers. The property characterizing the servers is provided to users via a user interface or alerts for further analysis, for example, to analyze the cause of high resource utilization.

Abstract: A computer-implemented method for analyzing spans and traces associated with a microservices-based application executing in a distributed computing environment comprises aggregating a plurality of ingested spans associated with one or more applications executing in the distributed computing environment into a plurality of traces, wherein each of the plurality of ingested spans is associated with a plurality of tags. The method further comprises comparing durations of a set of related traces of the plurality of traces to determine patterns for the plurality of tags and generating a histogram that represents a distribution of the durations of the set of related traces. The method also comprises providing alerts for one or more tags from the plurality of tags associated with traces having a duration above a threshold based on the distribution of the durations.

Abstract: An analysis system receives a time series. The data values of the time series correspond to a metric describing a characteristic of the computing system that changes over time. The analysis system stores a statistic value that represents the stationarity of the time series. In response to receiving a most recent value, the analysis system assigns the most recent value as the leading value in a window before retrieving the trailing value of the window. The analysis system updates the statistic value to add an influence of the most recent value and remove an influence of the trailing value. If the statistic value is less than a threshold, the analysis system determines that the time series is stationary. In response to determining the time series is stationary, the analysis system assigns an alert to the metric. The analysis system detects an anomaly in the metric based on the assigned alert.

Abstract: A system receives a time series of data values from instrumented software executing on an external system. Each data value corresponds to a metric of the external system. The system stores a level value representing a current estimate of the time series and a trend value representing a trend in the time series. The level and trend values are based on data in a window having a trailing value. In response to receiving a most recent value, the system updates the level value and the trend value to add an influence of the most recent value and remove an influence of the trailing value. The system forecasts based on the updated level and trend values, and in response to determining that the forecast indicates the potential resource shortage event, takes action.