Abstract: Disclosed is a system, method, and computer program product for analyzing sets of data in an efficient manner, such that analytics can be effectively performed over that data. Classification operations can be performed to generate groups of similar log records. This permits classification of the log records in a cohesive and informative manner.

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: Disclosed is a system, method, and computer program product for analyzing sets of data in an efficient manner, such that analytics can be effectively performed over that data. Classification operations can be performed to generate groups of similar log records. This permits classification of the log records in a cohesive and informative manner.

Abstract: Disclosed is a system, method, and computer program product for analyzing sets of data in an efficient manner, such that analytics can be effectively performed over that data. Classification operations can be performed to generate groups of similar log records. This permits classification of the log records in a cohesive and informative manner.

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: Disclosed is a system, method, and computer program product for analyzing sets of data in an efficient manner, such that analytics can be effectively performed over that data. Classification operations can be performed to generate groups of similar log records. This permits classification of the log records in a cohesive and informative manner.

Abstract: Disclosed is a system, method, and computer program product for analyzing sets of data in an efficient manner, such that analytics can be effectively performed over that data. Classification operations can be performed to generate groups of similar log records. This permits classification of the log records in a cohesive and informative manner.

Abstract: Disclosed is a system, method, and computer program product for analyzing sets of data in an efficient manner, such that analytics can be effectively performed over that data. Classification operations can be performed to generate groups of similar log records. This permits classification of the log records in a cohesive and informative manner.

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: A method for replaying a simulation workload is provided. The simulation workload identifies a plurality of workload parts. Each workload part is at least a portion of a respective captured workload. In an embodiment, a first workload part is a portion of a first workload executed relative to a first capture database server and a second workload part is a portion of a second workload executed relative to a second capture database server. A portion of the first workload part and a portion of the second workload part may be replayed concurrently.

Abstract: Described herein are methods for determining patterns based on requests received by a server. Based on the determined patterns, insight into the types of requests received by the server can be gained. Additionally, performance statistics and query statistics can be aggregated in a useful way. For example, performance statistics may be summarized for each determined pattern. One technique for determining patterns includes determining a sequence of template identifiers identifying templates that correspond to sub-sequences of requests in a sequence of server requests. A model may be created based on the sequence of template identifiers. Based on the model, template patterns may be determined. Template patterns may further be grouped into pattern clusters.

Abstract: A method for replaying a simulation workload is provided. The simulation workload identifies a plurality of workload parts. Each workload part is at least a portion of a respective captured workload. In an embodiment, a first workload part is a portion of a first workload executed relative to a first capture database server and a second workload part is a portion of a second workload executed relative to a second capture database server. A portion of the first workload part and a portion of the second workload part may be replayed concurrently.

Abstract: Described herein are methods for determining patterns based on requests received by a server. Based on the determined patterns, insight into the types of requests received by the server can be gained. Additionally, performance statistics and query statistics can be aggregated in a useful way. For example, performance statistics may be summarized for each determined pattern. One technique for determining patterns includes determining a sequence of template identifiers identifying templates that correspond to sub-sequences of requests in a sequence of server requests. A model may be created based on the sequence of template identifiers. Based on the model, template patterns may be determined. Template patterns may further be grouped into pattern clusters.