Abstract: An agent executed by a computer identifies a critical path of a transaction in a web application. The transaction includes a plurality of methods each representing an operation of the application and asynchronously executed by a server between a beginning of the transaction and an end of the transaction. The agent receives metrics for the transaction, which include identifiers of each executed method and specify relationships between the transaction methods. Based on the received metrics, the agent identifies a method completed at the end of the transaction. Based on the relationships between the methods, the agent determines one or more other methods preceding the identified method in the transaction and affecting a duration of the transaction. The determined methods and the method completed at the end of the transaction are identified as a critical path. Identifiers of the methods on the critical path are sent for display to a user.

Abstract: An agent executed by a computer identifies a critical path of a transaction in a web application. The transaction includes a plurality of methods each representing an operation of the application and asynchronously executed by a server between a beginning of the transaction and an end of the transaction. The agent receives metrics for the transaction, which include identifiers of each executed method and specify relationships between the transaction methods. Based on the received metrics, the agent identifies a method completed at the end of the transaction. Based on the relationships between the methods, the agent determines one or more other methods preceding the identified method in the transaction and affecting a duration of the transaction. The determined methods and the method completed at the end of the transaction are identified as a critical path. Identifiers of the methods on the critical path are sent for display to a user.

Abstract: By monitoring requests to and from components of an application, an application analysis engine generates an inter-component graph for an application that identifies how the various components in the application are connected. When a performance issue is detected in association with the application, a traversal module traverses the inter-component graph to determine the possible execution paths that may have been the cause of the detected issue. The traversal module transmits requests to the correlation module to compare the metrics time series of the different components in the execution path with the detected issue. The correlation module compares metrics time series with the issue metric to identify correlations between execution patterns. The results of the correlation may be presented in a report that visually identify the root cause of the detected issues.

Abstract: By monitoring requests to and from components of an application, an application analysis engine generates an inter-component graph for an application that identifies how the various components in the application are connected. When a performance issue is detected in association with the application, a traversal module traverses the inter-component graph to determine the possible execution paths that may have been the cause of the detected issue. The traversal module transmits requests to the correlation module to compare the metrics time series of the different components in the execution path with the detected issue. The correlation module compares metrics time series with the issue metric to identify correlations between execution patterns. The results of the correlation may be presented in a report that visually identify the root cause of the detected issues.

Abstract: By monitoring requests to and from components of an application, an application analysis engine generates an inter-component graph for an application that identifies how the various components in the application are connected. When a performance issue is detected in association with the application, a traversal module traverses the inter-component graph to determine the possible execution paths that may have been the cause of the detected issue. The traversal module transmits requests to the correlation module to compare the metrics time series of the different components in the execution path with the detected issue. The correlation module compares metrics time series with the issue metric to identify correlations between execution patterns. The results of the correlation may be presented in a report that visually identify the root cause of the detected issues.

Abstract: An agent executed by a computer identifies a critical path of a transaction in a web application. The transaction includes a plurality of methods each representing an operation of the application and asynchronously executed by a server between a beginning of the transaction and an end of the transaction. The agent receives metrics for the transaction, which include identifiers of each executed method and specify relationships between the transaction methods. Based on the received metrics, the agent identifies a method completed at the end of the transaction. Based on the relationships between the methods, the agent determines one or more other methods preceding the identified method in the transaction and affecting a duration of the transaction. The determined methods and the method completed at the end of the transaction are identified as a critical path. Identifiers of the methods on the critical path are sent for display to a user.

Abstract: By monitoring requests to and from components of an application, an application analysis engine generates an inter-component graph for an application that identifies how the various components in the application are connected. When a performance issue is detected in association with the application, a traversal module traverses the inter-component graph to determine the possible execution paths that may have been the cause of the detected issue. The traversal module transmits requests to the correlation module to compare the metrics time series of the different components in the execution path with the detected issue. The correlation module compares metrics time series with the issue metric to identify correlations between execution patterns. The results of the correlation may be presented in a report that visually identify the root cause of the detected issues.