Abstract: A system receiving time series data will store the data in a first format suited for quick writing. That data will subsequently be converted from the first format to a second format better suited than the first format for compact storage and queries. Over time, the data in the second format can be aggregated and/or down sampled. In one embodiment, the first format includes a set of blocks of information, with each block of information storing data for a set of metrics during a time period. The second format includes a header, a set of blocks of data and a footer. Each bock of data in the second format stores information for a metric over multiple time periods. The footer includes pointers to each block of data.

Abstract: A system receiving time series data will store the data in a first format suited for quick writing. That data will subsequently be converted from the first format to a second format better suited than the first format for compact storage and queries. Over time, the data in the second format can be aggregated and/or down sampled. In one embodiment, the first format includes a set of blocks of information, with each block of information storing data for a set of metrics during a time period. The second format includes a header, a set of blocks of data and a footer. Each bock of data in the second format stores information for a metric over multiple time periods. The footer includes pointers to each block of data.

Abstract: Deviation of expected response times is used to characterize the health of one or more backend machines invoked by an application to process a request. Performance data generated in response to monitoring application execution is processed to select backend response time data. The selected data is processed to predict future values of a time series associated with backend response time. The predicted response time values are compared to actual response time values in the time series to determine a deviation from the predicted value. Deviation information for the time series data of response times is then reported to a user through an interface in a simple manner.

Abstract: Deviation of an expected error rate for a backend server invoked by an application to process a request is used to characterize the backend server health. Performance data generated in response to monitoring application execution is processed to select backend error rate data. The selected data is processed to predict future values of a time series associated with backend error rates. The predicted error rate values are compared to actual error rate values in the time series to determine a deviation from the predicted value. Deviation information for the time series data of error rates is then reported to a user through an interface in a simplified manner.

Abstract: One or more new methods are added to existing object code. The existing object code includes a first method that is capable of producing a result. New code is added to the first method. The new code provides the result to one or more of the new methods. After the modification, the result (e.g. a return value or exception) from the first method can be accessed and used by other threads, processes, systems, entities etc. that were not originally programmed to access the result or exception.

Abstract: Deviation of an expected error rate for a backend server invoked by an application to process a request is used to characterize the backend server health. Performance data generated in response to monitoring application execution is processed to select backend error rate data. The selected data is processed to predict future values of a time series associated with backend error rates. The predicted error rate values are compared to actual error rate values in the time series to determine a deviation from the predicted value. Deviation information for the time series data of error rates is then reported to a user through an interface in a simplified manner.

Abstract: Deviation of expected response times is used to characterize the health of one or more backend machines invoked by an application to process a request. Performance data generated in response to monitoring application execution is processed to select backend response time data. The selected data is processed to predict future values of a time series associated with backend response time. The predicted response time values are compared to actual response time values in the time series to determine a deviation from the predicted value. Deviation information for the time series data of response times is then reported to a user through an interface in a simple manner.

Abstract: A system is disclosed for accessing information about an object, where such information is available at the time of creation of the object but not necessarily available at all times later. In one embodiment, existing object code is modified to add the ability to access the information. The modified code is then executed.

Abstract: Potential sources of memory leaks are identified by tracking the size of groups of stored items and determining based on the growth pattern of the groups whether the groups of stored items are potential sources of memory leaks. An example of a group of stored items is an instance of a Java collection. If the growth pattern of a particular group of stored items indicates that it may be the source of a memory leak, that group is reported to a user and will continue to be tracked.

Abstract: Methods are classified as simple or complex. Complex methods are modified to add a tracer. Methods classified as simple are not modified to add a tracer. There are many different standards that can be used within the spirit of the present invention to classify methods as simple or complex. In one embodiment, a method is complex if it meets three criteria: (1) the method has an access level of public or package; (2) the method is non-synthetic and (3) the method calls at least one other method. Methods that do not satisfy all three criteria are classified as simple methods.

Abstract: Potential sources of memory leaks are identified by tracking the size of groups of stored items and determining based on the growth pattern of the groups whether the groups of stored items are potential sources of memory leaks. An example of a group of stored items is an instance of a Java collection. If the growth pattern of a particular group of stored items indicates that it may be the source of a memory leak, that group is reported to a user and will continue to be tracked.