Abstract:
The disclosed embodiments provide a system that facilitates the processing of network data. During operation, the system causes for display a graphical user interface (GUI) for configuring the generation of time-series event data from network packets captured by one or more remote capture agents. Next, the system causes for display, in the GUI, a first set of user-interface elements containing a set of statistics associated with one or more event streams that comprise the time-series event data. The system then causes for display, in the GUI, one or more graphs comprising one or more values from the set of statistics. Finally, the system causes for display, in the GUI, a value of a statistic from the set of statistics based on a position of a cursor over the one or more graphs.
Abstract:
Systems and methods are disclosed for processing queries against a common storage utilizing dynamically allocated partitions operating on one or more worker nodes. The common storage can include one or more data stores, which collectively contain a data set divided across multiple buckets of data. To query the common storage, a query coordinator can retrieve metadata regarding the multiple buckets, in order to determine a subset of buckets that are potentially relevant to a query. The query coordinator can then dynamically allocate partitions operating on worker nodes to retrieve and intake individual buckets of the subset into a phased search process. The dynamic allocation can be selected to maximize parallelization of the buckets across partitions, thus increasing a speed at which the common storage can be searched.
Abstract:
Systems and methods are disclosed for processing queries against a common storage utilizing dynamically allocated partitions operating on one or more worker nodes. The common storage can include one or more data stores, which collectively contain a data set divided across multiple buckets of data. To query the common storage, a query coordinator can retrieve metadata regarding the multiple buckets, in order to determine a subset of buckets that are potentially relevant to a query. The query coordinator can then dynamically allocate partitions operating on worker nodes to retrieve and intake individual buckets of the subset into a phased search process. The dynamic allocation can be selected to maximize parallelization of the buckets across partitions, thus increasing a speed at which the common storage can be searched.
Abstract:
The disclosed embodiments provide a system that facilitates the processing of network data. During operation, the system causes for display a graphical user interface (GUI) for configuring the generation of time-series event data from network packets captured by one or more remote capture agents. Next, the system causes for display, in the GUI, a first set of user-interface elements containing a set of statistics associated with one or more event streams that comprise the time-series event data. The system then causes for display, in the GUI, one or more graphs comprising one or more values from the set of statistics. Finally, the system causes for display, in the GUI, a value of a statistic from the set of statistics based on a position of a cursor over the one or more graphs.
Abstract:
The disclosed embodiments provide a system that facilitates the processing of network data. During operation, the system causes for display a graphical user interface (GUI) for configuring the generation of time-series event data from network packets captured by one or more remote capture agents. Next, the system causes for display, in the GUI, a first set of user-interface elements containing a set of statistics associated with one or more event streams that comprise the time-series event data. The system then causes for display, in the GUI, one or more graphs comprising one or more values from the set of statistics. Finally, the system causes for display, in the GUI, a value of a statistic from the set of statistics based on a position of a cursor over the one or more graphs.
Abstract:
Systems and methods are described for using a streaming data processor to group notable events reflecting operation of a computing system into episodes of related events reflecting an incident on the computing system, such as to enable root cause analysis of the incident. Each notable event can be generated based on one or more events detected within raw machine data. The streaming data processor can ingest a data stream of notable events, and apply a clustering algorithm to the events to cluster those events into episodes. When the episodes satisfy an action rule, the streaming data processor can take an action appropriate to that rule, such as transmitting an alert or programmatically altering operation of the computing system. The streaming data processor can utilize feedback as to the grouping of events into episodes to modify the clustering algorithm and improve accuracy of clustering.
Abstract:
The disclosed embodiments provide a system that facilitates the processing of network data. During operation, the system causes for display a graphical user interface (GUI) for configuring the generation of time-series event data from network packets captured by one or more remote capture agents. Next, the system causes for display, in the GUI, a first set of user-interface elements containing a set of statistics associated with one or more event streams that comprise the time-series event data. The system then causes for display, in the GUI, one or more graphs comprising one or more values from the set of statistics. Finally, the system causes for display, in the GUI, a value of a statistic from the set of statistics based on a position of a cursor over the one or more graphs.
Abstract:
The disclosed embodiments provide a system that facilitates the processing of network data. During operation, the system causes for display a graphical user interface (GUI) for configuring the generation of time-series event data from network packets captured by one or more remote capture agents. Next, the system causes for display, in the GUI, a first set of user-interface elements containing a set of statistics associated with one or more event streams that comprise the time-series event data. The system then causes for display, in the GUI, one or more graphs comprising one or more values from the set of statistics. Finally, the system causes for display, in the GUI, a value of a statistic from the set of statistics based on a position of a cursor over the one or more graphs.
Abstract:
Systems and methods are described for training a machine learning (ML) model to group notable events reflecting operation of a computing system into episodes of related events reflecting an incident on the computing system, such as to enable root cause analysis of the incident. The ML model is trained using pairwise binary similarity labels (PBSLs) indicating that two events must or must not be grouped together. An interface is provided that facilitates rapid generating of PBSLs by relocating one or more events from a first episode to a second episode. The relocation input is translated into PBSLs that are then used to train the ML model.
Abstract:
The disclosed embodiments provide a system that facilitates the processing of network data. During operation, the system causes for display a graphical user interface (GUI) for configuring the generation of time-series event data from network packets captured by one or more remote capture agents. Next, the system causes for display, in the GUI, a first set of user-interface elements containing a set of statistics associated with one or more event streams that comprise the time-series event data. The system then causes for display, in the GUI, one or more graphs comprising one or more values from the set of statistics. Finally, the system causes for display, in the GUI, a value of a statistic from the set of statistics based on a position of a cursor over the one or more graphs.