Warning Data Management in Stream Computing Execution
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Solution Overview
Problem
The increasing volume of warning data in computing environments, particularly during code development in stream and distributed computing, poses challenges in efficiently managing and utilizing this data to identify errors and improve development processes.
Innovation Solution
Collecting and storing warning data across various phases of code development, including compilation and execution, to correlate problems with their causes and provide actionable insights to developers, facilitating better decision-making and resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If warning data is collected and stored during execution phase, then code quality and development efficiency are improved, but system complexity and data management overhead increase
Solution Approach 1:
The system collects and stores warning data during the execution phase before final code deployment, preparing the data in advance for subsequent analysis and quality improvement activities. This preliminary data collection enables developers to identify and fix issues before production deployment.
Solution Approach 2:
A warning data management system acts as an intermediary layer between the execution environment and developers, automatically collecting, storing, and organizing warning data. This intermediary handles the complexity of data management, allowing developers to focus on code quality improvement without being burdened by data handling overhead.
2Reliability
If comprehensive warning data is collected across all development phases, then error identification and problem correlation are improved, but data processing time and storage requirements increase
Solution Approach 1:
The warning data collection process is segmented into distinct phases (compilation phase and execution phase), with different types of warning data collected at appropriate times. This segmentation allows for efficient data organization and reduces processing overhead by collecting data in manageable segments rather than as a monolithic process.
Solution Approach 2:
The system implements feedback mechanisms where warning data is continuously collected, analyzed, and used to improve code quality. The feedback loop enables automatic identification of errors and problems, reducing the time required for manual analysis while maintaining high reliability in error identification.
3Loss of information
If warning data is integrated with computing artifacts, then traceability and problem correlation are improved, but data integration complexity and processing overhead increase
Solution Approach 1:
Warning data is merged with computing artifacts (such as code objects, compilation outputs, and execution results) to create integrated data structures that maintain traceability. This merging allows developers to correlate warnings with specific code elements and execution contexts without requiring separate data management systems.
Solution Approach 2:
The warning data structure is designed with universal properties that enable it to be integrated with multiple types of computing artifacts across different development phases. This multi-functional design reduces integration complexity by using a standardized approach that works across compilation, execution, and deployment phases.
Data Source
AI summary
Aspects of the disclosure relate to managing a set of warning data with respect to an execution phase in a computing environment. In embodiments, the computing environment may include a distributed computing environment or a stream computing environment. The set of warning data may be detected with respect to the execution phase. In embodiments, the set of warning data may be coupled with a computing artifact. In embodiments, the computing artifact may include a compilation which has a computing object in association with the set of warning data. Using the set of warning data, an execution action which pertains to the computing artifact may be determined. In embodiments, the execution action may include a code deployment to a set of computing units, a run-time check modification, or a process attribute modification. The execution action which pertains to the computing artifact may be performed.