A technique for verifying data structure consistency across computing environments includes computing a first signature for a data structure of an application subject to checkpointing corresponding to a first computing environment residing on a first computer. A second signature for the data structure of the application corresponding to a second computing environment residing on a second computer is computed. The first and second signatures are compared to determine whether a change to the data structure exists. Responsive to a lack of change to the data structure based on the comparison, a mobility operation is enabled for the application between the first computer and the second computer.

Methods and systems are described for provisioning cloud-based database systems and performing decoupled maintenance. For example, conventional systems may rely on database management systems to provision and modify databases hosted by a service provider. However, for entities operating complex database systems with the need for highly customized cloud infrastructure, database management systems fail to provide the granular customization and the control necessary to create and service these systems. In contrast, the described solutions provide an improvement over conventional database management system architecture by providing direct communication between an entity and its cloud-based database systems via command line prompts or API calls, decoupling database system maintenance from database system provisioning process to increase the speed and granular customization of the database system. Moreover, the disclosed solution leverages machine learning to predict optimal database system provisioning and maintenance processes and resources.

Methods, computer program products, and/or systems are provided that perform the following operations: determining that a log multi-process debug mode is specified; obtaining a log file for debugging a source code, wherein the log file includes a plurality of log records; inserting a plurality of process identifier fields into each current log record in the log file; inserting a new log record into the log file for a created new process; and providing for performance of debugging for the source code based in part on the plurality of process identifier fields inserted into each current log record.

Examples described herein relate to systems and methods consistent with the disclosure. For instance, the system can comprise a processing resource, and a non-transitory machine-readable medium storing instructions executable by the processing resource determine when an application on the system is activated, monitor the application to determine an application failure using a tracking feature, take a snapshot of computing information related to the determined application failure, determine a coding language of the determined application failure, store the determined application failure, snapshot of computing information, and the coding language of the determined application to a memory device, and send the stored determined application failure, snapshot of computing information, and the coding language of the determined application to a server.

A system and method for dynamic log management of stream processing in a distributed computing environment, such as, for example, a streaming application or stream analytics system. A streaming application can be deployed or published to a cluster, to execute as a client application. A cluster manager coordinates with worker nodes, to commit tasks associated with the streaming application. If a need arises to generate lower-level log data associated with the streaming application, for example to diagnose an underlying cause of a warning/error message, a configuration job can be committed to the cluster to execute as a separate log-configuration application. The log-configuration application operates with the cluster manager to determine the set of working nodes currently associated with the streaming application, and modify the logger configuration at those nodes, to record or otherwise provide log data according to a modified logging level, for example to provide lower-level log messages.

A data processing pipeline may be generated to include an orchestrator node, a preparator node, and an executor node. The preparator node may generate a training dataset. The executor node may execute machine learning trials by applying, to the training dataset, a machine learning model and/or a different set of trial parameters. The orchestrator node may identify, based on a result of the machine learning trials, a machine learning model for performing a task. Data associated with the execution of the data processing pipeline may be collected for storage in a tracking database. A report including de-normalized and enriched data from the tracking database may be generated. The hyper-parameter space of the machine learning model may be analyzed based on the report. A root cause of at least one fault associated with the execution of the data processing pipeline may be identified based on the analysis.

Embodiments of the invention are directed to active detection and mitigation of anomalies in program code construction interfaces. The system provides a proactive plug-in with a dynamic machine learning (ML) anomaly detection model cloud component structured to dynamically detect architectural flaws in program code in real-time in a user coding interface. In particular, the system activates a machine learning (ML) anomaly detection plug-in for dynamically analyzing the first technology program code being constructed in the user coding interface. Moreover, the system modifies, via the ML anomaly detection plug-in, the user coding interface to embed interface elements associated with the one or more flaws in the first technology program code detected by the ML anomaly detection model cloud component.

Computer systems and associated methods are disclosed to implement a model development environment (MDE) that allows a team of users to perform iterative model experiments to develop machine learning (ML) media models. In embodiments, the MDE implements a media data management interface that allows users to annotate and manage training data for models. In embodiments, the MDE implements a model experimentation interface that allows users to configure and run model experiments, which include a training run and a test run of a model. In embodiments, the MDE implements a model diagnosis interface that displays the model's performance metrics and allows users to visually inspect media samples that were used during the model experiment to determine corrective actions to improve model performance for later iterations of experiments. In embodiments, the MDE allows different types of users to collaborate on a series of model experiments to build an optimal media model.

An improved SDK includes a set of APIs and a crash handler registered with the operating system. Each API is an interface accessible by a computer software application. Up on entrance, each API determines the current thread identifier, and inserts it into a list if it is not already in the list. Each thread identifier corresponds to an API call counter, which is incremented by one at the entrance and decremented by one at the exit point of the API. The SDK also records the identifier of the thread it creates for callback functions. When a crash occurs, the crash handler is executed. It determines that the crash is related to a callback interface if the crash thread identifier matches the callback thread identifier. The crash is determined to be caused by the SDK if the API call counter corresponding to the crash thread identifier is greater than zero.

Intelligent collection and analysis of in-app failure data is disclosed herein. Upon an application failure in a client device, the client device may collect failure information uniquely identifying a specific failure and provide the failure information to an analysis system. The analysis system may identify a specific failure that identifies the application and a specific portion of the code in the application, based on the failure information and match an action correlated to the specific failure where the action is uniquely designed to resolve the specific failure in the application. The action may include instructions for the client device used to intelligently lead to a resolution of the specific failure. The analysis system may transmit the action to the client device to perform the action and provide any follow up information to the analysis server. The analysis server may use the information to further analyze the specific failure.