The present invention is directed to a system and a method for ensuring high availability and guarantee delivery of event streams to target consumer applications. The distributed event streaming platform of the present invention is provided with an event streaming layer comprising a plurality of independent and non-replicating event streaming clusters that are configured to process events received from a producer module. A monitoring module is provided to monitor the propagation and delivery of each propagated event based on event notifications received from an event producer module and a plurality of the consumer modules.

Described are a method, system, and computer program product for operating dynamic shadow testing environments for machine-learning models. The method includes generating a shadow testing environment operating at least two transaction services. The method also includes receiving a plurality of transaction authorization requests. The method further includes determining a first percentage associated with a first testing policy of the first transaction service and a second percentage associated with a second testing policy of the second transaction service. The method further includes replicating in the shadow testing environment, in real-time with processing the payment transactions, a first portion of the plurality of transaction authorization requests and a second portion of the plurality of transaction authorization requests. The method further includes testing the first transaction service using the first set of replicated transaction data and the second transaction service using the second set of replicated transaction data.

Back end calls triggered by a user interaction with a client user interface may be identified. The user interaction may be correlated with a logic flow, and the logic flow may be associated with the back end calls. A supervised learning model may be trained using a labeled data set comprising the back end calls and their associated logic flow. Rules may be derived from the supervised learning model for classifying other back end calls. The rules may be outputted to a classifier that utilizes the rules to associate the other back end calls with the logic flow.

A method, computer program product, and system include a processor(s) that obtains a request for a transaction to be processed by an application in a computing system. The processor(s) applies, to the request for the transaction, an outlier detection model, to determine whether the transaction comprises attributes matching transaction tuples of one or more historical transactions identified as triggering issues in the computing system when the application processed the historical transactions. The processor(s) classifies the transaction as being an outlier transaction or as being a standard transaction, based on applying the outlier detection model. Based on determining that the transaction is an outlier transaction, concurrently with the application processing the transaction, the processor(s) turn on the debug trace to debug trace the application processing of the transaction.

Techniques for transaction tracking for a high availability architecture are described herein. An aspect includes receiving a first request from a client, the first request corresponding to a start of a transaction having transaction affinity. Another aspect includes, based on receiving the first request from the client, generating a transaction tracking token. Another aspect includes sending the first request with the transaction tracking token to a gateway. Another aspect includes receiving a first response corresponding to the first request from the gateway, the first response including middleware instance information corresponding to a middleware instance, wherein a plurality of subsequent requests from the client corresponding to the transaction are processed by the middleware instance corresponding to the middleware instance information.

An illustrative data storage management system comprises a management database that stores administrative preferences and system configurations, as well as results and/or statistics of completed secondary storage operations, i.e., information needed by the system to protect customers' data and to track and recover the protected data, including secondary copies such as backup copies, archive copies, etc. The disclosed data storage management system is configured to protect its own system data subject to a very aggressive (short) Recovery Point Objective (RPO), by using an innovative infrastructure that enables the system's storage manager to fail over to any number of other failover destination storage managers, each one comprising a destination management database. An illustrative database granularly tracks whether each and every transaction log file has been successfully applied to each and every destination management database to synchronize with the source management database.

A transaction processing method includes: dividing a to-be-processed transaction obtained from a database into at least two subtransactions; dividing each subtransaction into N parts with an association relationship; processing the N parts of each subtransaction based on the association relationship, to obtain a processing result of a lastly executed part of the N parts; determining, upon detecting an abnormal subtransaction based on the processing result, a processing policy matching an abnormality reason of the abnormal subtransaction; and processing the abnormal subtransaction by using the processing policy, to obtain a final processing result of the to-be-processed transaction.

Disclosed are various embodiments for tracking the flow of data through a network environment. A monitor can detect that a data transaction event has occurred. Then, the monitor can identify data involved in the data transaction event. Next, a trace identifier can be assigned to the data involved in the data transaction event. Subsequently, a transaction data subset representing a subset of the data involved in the data transaction event that is subject to a common data processing event can be identified. Then, a span identifier can be assigned to the transaction data subset. Next, a correlation identifier can be link to a combination of the span identifier and the trace identifier. Finally, a transaction event record can be written to a distributed ledger, the transaction event record comprising the span identifier and the transaction data subset.

A computing network includes nodes of different work groups. Nodes of a work group are dedicated to transactions of the work group. If a node of a first work group is predicted to have an idleness window, a second work group may borrow the node to execute a transaction of the second work group. At least a subset of steps of the transaction may be categorized into a step group. Trees of a transaction may be categorized into one or more tree groups. A node is selected for executing a transaction, if the predicted idleness duration of the node is sufficient relative to the predicted runtime of the transaction, the step group, and/or tree group. A credit system is maintained. A first work group transfers a credit to a second work group when borrowing a node of the second work group for executing a transaction of the first work group.

A computing network includes nodes of different work groups. Nodes of a work group are dedicated to transactions of the work group. If a node of a first work group is predicted to have an idleness window, a second work group may borrow the node to execute a transaction of the second work group. At least a subset of steps of the transaction may be categorized into a step group. Trees of a transaction may be categorized into one or more tree groups. A node is selected for executing a transaction, if the predicted idleness duration of the node is sufficient relative to the predicted runtime of the transaction, the step group, and/or tree group. A credit system is maintained. A first work group transfers a credit to a second work group when borrowing a node of the second work group for executing a transaction of the first work group.