Abstract: Techniques are described herein for probabilistic monitoring of high-frequency, low-latency database queries. In some embodiments, a probabilistic query monitoring system periodically samples active database sessions. For example, the system may generate sample data every one second or at some other sampling rate for each database session that is currently active. The sample data may include a mapping between query identifiers to sample counter values that are extracted at different sample intervals. The system may then estimate performance metrics for the set of active database based on the counter values sampled across consecutive sample intervals.

Abstract: Techniques for leveraging frequent patterns identified in a captured workload are provided. In one approach, multiple frequent patterns detected in a captured workload may be ordered by frequency to determine, for example, which patterns should be targeted for optimization. In another approach, a model of a captured workload is created, where the model comprises nodes that represent templates (which in turn correspond to requests) and edges that represent transitions between templates. The model is used to create an artificial workload, such as a workload that is twice as large as the originally-captured workload. The model may also be edited before creating the artificial workload. In another approach, workload models are compared to identify errors, regressions, or security issues. In another approach, an artificial workload is created for an application that is not yet deployed and then executed to determine whether the artificial workload or the originally-captured workload executed faster.

Abstract: Techniques for leveraging frequent patterns identified in a captured workload are provided. In one approach, multiple frequent patterns detected in a captured workload may be ordered by frequency to determine, for example, which patterns should be targeted for optimization. In another approach, a model of a captured workload is created, where the model comprises nodes that represent templates (which in turn correspond to requests) and edges that represent transitions between templates. The model is used to create an artificial workload, such as a workload that is twice as large as the originally-captured workload. The model may also be edited before creating the artificial workload. In another approach, workload models are compared to identify errors, regressions, or security issues. In another approach, an artificial workload is created for an application that is not yet deployed and then executed to determine whether the artificial workload or the originally-captured workload executed faster.

Abstract: Techniques for selecting an anomaly based on a context are disclosed. A set of metrics corresponding to communications with nodes of a computer system are identified. A set of insights are generated based on the set of metrics. A context for determining a primary anomaly is determined. A subset of metrics associated with the context are identified. A subset of insights that are generated based on the subset of metrics are identified. An insight is selected from the subset of insights as the primary anomaly. A visualization associated with the primary anomaly is presented at a user interface. One or more secondary anomalies may be concurrently presented with the visualization. Additionally, the primary anomaly, the selected visualization, and/or the secondary anomaly is used to determine a new context for selecting another primary anomaly. Hence, a series of primary anomalies may be selected, each primary anomaly being related to each other.

Abstract: Techniques for selecting an anomaly based on a context are disclosed. A set of metrics corresponding to communications with nodes of a computer system are identified. A set of insights are generated based on the set of metrics. A context for determining a primary anomaly is determined. A subset of metrics associated with the context are identified. A subset of insights that are generated based on the subset of metrics are identified. An insight is selected from the subset of insights as the primary anomaly. A visualization associated with the primary anomaly is presented at a user interface. One or more secondary anomalies may be concurrently presented with the visualization. Additionally, the primary anomaly, the selected visualization, and/or the secondary anomaly is used to determine a new context for selecting another primary anomaly. Hence, a series of primary anomalies may be selected, each primary anomaly being related to each other.

Abstract: Techniques for selecting an anomaly based on a context are disclosed. A set of metrics corresponding to communications with nodes of a computer system are identified. A set of insights are generated based on the set of metrics. A context for determining a primary anomaly is determined. A subset of metrics associated with the context are identified. A subset of insights that are generated based on the subset of metrics are identified. An insight is selected from the subset of insights as the primary anomaly. A visualization associated with the primary anomaly is presented at a user interface. One or more secondary anomalies may be concurrently presented with the visualization. Additionally, the primary anomaly, the selected visualization, and/or the secondary anomaly is used to determine a new context for selecting another primary anomaly. Hence, a series of primary anomalies may be selected, each primary anomaly being related to each other.

Abstract: Techniques for selecting an anomaly based on a context are disclosed. A set of metrics corresponding to communications with nodes of a computer system are identified. A set of insights are generated based on the set of metrics. A context for determining a primary anomaly is determined. A subset of metrics associated with the context are identified. A subset of insights that are generated based on the subset of metrics are identified. An insight is selected from the subset of insights as the primary anomaly. A visualization associated with the primary anomaly is presented at a user interface. One or more secondary anomalies may be concurrently presented with the visualization. Additionally, the primary anomaly, the selected visualization, and/or the secondary anomaly is used to determine a new context for selecting another primary anomaly. Hence, a series of primary anomalies may be selected, each primary anomaly being related to each other.

Abstract: Techniques for selecting an anomaly based on a context are disclosed. A set of metrics corresponding to communications with nodes of a computer system are identified. A set of insights are generated based on the set of metrics. A context for determining a primary anomaly is determined. A subset of metrics associated with the context are identified. A subset of insights that are generated based on the subset of metrics are identified. An insight is selected from the subset of insights as the primary anomaly. A visualization associated with the primary anomaly is presented at a user interface. One or more secondary anomalies may be concurrently presented with the visualization. Additionally, the primary anomaly, the selected visualization, and/or the secondary anomaly is used to determine a new context for selecting another primary anomaly. Hence, a series of primary anomalies may be selected, each primary anomaly being related to each other.

Abstract: Techniques for leveraging frequent patterns identified in a captured workload are provided. In one approach, multiple frequent patterns detected in a captured workload may be ordered by frequency to determine, for example, which patterns should be targeted for optimization. In another approach, a model of a captured workload is created, where the model comprises nodes that represent templates (which in turn correspond to requests) and edges that represent transitions between templates. The model is used to create an artificial workload, such as a workload that is twice as large as the originally-captured workload. The model may also be edited before creating the artificial workload. In another approach, workload models are compared to identify errors, regressions, or security issues. In another approach, an artificial workload is created for an application that is not yet deployed and then executed to determine whether the artificial workload or the originally-captured workload executed faster.

Abstract: Techniques for leveraging frequent patterns identified in a captured workload are provided. In one approach, multiple frequent patterns detected in a captured workload may be ordered by frequency to determine, for example, which patterns should be targeted for optimization. In another approach, a model of a captured workload is created, where the model comprises nodes that represent templates (which in turn correspond to requests) and edges that represent transitions between templates. The model is used to create an artificial workload, such as a workload that is twice as large as the originally-captured workload. The model may also be edited before creating the artificial workload. In another approach, workload models are compared to identify errors, regressions, or security issues. In another approach, an artificial workload is created for an application that is not yet deployed and then executed to determine whether the artificial workload or the originally-captured workload executed faster.

Abstract: Techniques for selecting an anomaly based on a context are disclosed. A set of metrics corresponding to communications with nodes of a computer system are identified. A set of insights are generated based on the set of metrics. A context for determining a primary anomaly is determined. A subset of metrics associated with the context are identified. A subset of insights that are generated based on the subset of metrics are identified. An insight is selected from the subset of insights as the primary anomaly. A visualization associated with the primary anomaly is presented at a user interface. One or more secondary anomalies may be concurrently presented with the visualization. Additionally, the primary anomaly, the selected visualization, and/or the secondary anomaly is used to determine a new context for selecting another primary anomaly. Hence, a series of primary anomalies may be selected, each primary anomaly being related to each other.

Abstract: Techniques for leveraging frequent patterns identified in a captured workload are provided. In one approach, multiple frequent patterns detected in a captured workload may be ordered by frequency to determine, for example, which patterns should be targeted for optimization. In another approach, a model of a captured workload is created, where the model comprises nodes that represent templates (which in turn correspond to requests) and edges that represent transitions between templates. The model is used to create an artificial workload, such as a workload that is twice as large as the originally-captured workload. The model may also be edited before creating the artificial workload. In another approach, workload models are compared to identify errors, regressions, or security issues. In another approach, an artificial workload is created for an application that is not yet deployed and then executed to determine whether the artificial workload or the originally-captured workload executed faster.