Identifying metrics related to data ingestion associated with a defined time period

    公开(公告)号:US11288283B2

    公开(公告)日:2022-03-29

    申请号:US16394733

    申请日:2019-04-25

    Applicant: SPLUNK INC.

    Abstract: A data intake and query system measures an amount of raw data ingested by the system during defined periods of time. As used herein, ingesting raw data generally refers to receiving the raw data from one or more computing devices and processing the data for storage and searchability. Processing the data may include, for example, parsing the raw data into “events,” where each event includes a portion of the received data and is associated with a timestamp. Based on a calculated number of events generated by the system during one or more defined time periods, the system may calculate various metrics including, but not limited to, a number of events generated during a particular day, a number of events generated per day over a period of time, a maximum number of events generated in a day over a period of time, an average number of events generated per day, etc.

    Processing data streams received from instrumented software in real time using incremental-decremental implementation of the KPSS stationarity statistic

    公开(公告)号:US11281565B1

    公开(公告)日:2022-03-22

    申请号:US17030270

    申请日:2020-09-23

    Applicant: Splunk Inc.

    Inventor: Joseph Ari Ross

    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.

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