A method for processing data using a distributed data processing system includes providing input data to the distributed data processing system from a distributed data processing system interface component in communication with the distributed data processing system. The distributed data processing system processes the input data to generate a response, which it provides to the distributed data processing system interface component. The distributed data processing system interface component holds the response in escrow until it receives an indication from the distributed data processing system that the response has reached a sufficient level of durability.

A method for processing state update requests in a distributed data processing system with a number of processing nodes includes maintaining a number of counters including a working counter indicating a current time interval, a replication counter indicating a time interval for which all requests associated with that time interval are replicated at multiple processing nodes of the number of processing nodes, and a persistence counter indicating a time interval of the number of time intervals for which all requests associated with that time interval are stored in persistent storage. The counters are used to manage processing of the state update requests.

A method for managing a distributed data processing system, the method implementing counters to track durability states of data units in the distributed data processing system, wherein the counters are used to manage processing of the data units in the distributed data processing system.

A method for managing data in a distributed data processing system including a number of processing nodes includes storing data units in data stores that are associated with a number of different levels of durability. The method includes maintaining indicators including a first indicator associated with a first durability level and a second indicator associated with a second durability level. The first indicator is maintained to reflect a time interval at which all sets of data units associated with the time interval are stored at the first durability level. The second indicator is maintained to reflect a timer interval at which all sets of data units associated with the time interval are stored at the second durability level. The first and second indicators are used to manage processing of the data units in the distributed data processing system.

A method for processing state update requests in a distributed data processing system includes processing a set of state update requests associated with a first time interval including maintaining a count of issued state update requests for the set of state update requests, maintaining a count of state updates performed for the first set of state update requests, and updating a state consistency indicator to indicate that state updates associated with all state update requests of the first set of state update requests have been performed in response to determining that the count of state updates performed for the first set of state update requests equals the count of issued state update requests for the first set of state update requests.

According to some embodiments, an application error detection platform computer processor may monitor execution of an application for a customer and determine that an application error has occurred. Responsive to the determination that an application error has occurred, an error fingerprint creation platform computer processor may access a stack trace representing execution of the application when the application error occurred. An error fingerprint associated with the occurrence of the application error may then be determined by applying a hash function (e.g., SHA-1) to information contained in the stack trace. Electronic records including an indication of the error fingerprint may then be received by and stored in an application error log data store. The application error log data store may then be searched for duplicate error fingerprints to facilitate application error resolution.

A method and device for performing failsafe computation, and a method of compiling code to perform a failsafe computation are provided. The method includes performing a first calculation (212) to generate a first result (214). A second calculation (218) is performed using a scalar (216) and the first calculation (212) to generate a second result (220). The second calculation (218) includes multiplying the first calculation (212) by the scalar (216) to generate a scaled result, and dividing the scaled result by the scalar (216) to generate the second result (220). The first result (214) and the second result (220) are compared to determine if they are equivalent.

One example method includes performing replication operations and/or failure recovery operations for replication operations in a computing system using matrix clocks and replication controllers. The replication controller includes or has access to vectors of a matrix clock. The replication controller also includes a multiplexer. The multiplexer is configured as an m?n multiplexer using the matrix clock to determine m and n. The operation is performed using the configured multiplexer. This allows an imbalance of resources to be managed by the replication controller. The matrix clock can be updated, which may result in the multiplexer being reconfigured.

One example method includes performing replication operations and/or failure recovery operations for replication operations in a computing system using matrix clocks. Each node or process in a computing system is associated with a matrix clock that reflects streams required and/or available at the source and destination nodes. As the replication operation is performed, the matrix clocks are updated. When a failure is detected or when insufficient streams are available, the nodes may be throttled until sufficient streams are available. The matrix clocks facilitate a recovery line such that replication can resume when sufficient streams are available.

A method for creating a common platform graphical user interface is provided. The interface may enable a user to trigger a data load job from a tool. The tool may monitor file upload events, trigger jobs and identify lists of missing or problematic file names. The tool may run on a single thread, thereby consuming relatively less system resources than a multi-thread program to perform its capabilities. The tool may enable selection of file names using wildcard variables or keyword variables. The tool may validate a list of files received against a master file list for each data load job. The tool may receive user input relating to each data load job. The tool may generate a loop within the single thread to receive information. The tool may analyze the received information and use the received information to predict future metadata associated with future data load jobs.