Stream Operator Processing Rate Adjustment for Data Surges
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Stream computing systems face challenges in dynamically adjusting processing rates to handle varying data volumes and complexities, leading to inefficiencies and potential data loss due to bottlenecks or incomplete analysis, especially when dealing with real-time and unstructured data from intelligent devices and sensors.
Innovation Solution
The implementation of Runtime Processing Rate Adjustments (RPRA) within stream applications, which allow for dynamic configuration of filter statements to adjust the processing rate of stream operators based on predetermined criteria, ensuring optimal processing of tuples and handling of data volumes, thereby enhancing accuracy and resilience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If stream computing systems process large quantities of data in real-time, then data analysis completeness is improved, but processing efficiency deteriorates due to bottlenecks
Solution Approach 1:
The patent implements dynamic processing rate adjustment for stream operators based on runtime metrics. The system monitors processing rates and dynamically adjusts the processing speed of stream operators to match incoming data rates, preventing bottlenecks while maintaining complete data analysis. This dynamic adaptation resolves the contradiction by allowing the system to process all data (improving completeness) without fixed-rate limitations (maintaining efficiency).
Solution Approach 2:
The system changes the processing rate parameter of stream operators at runtime based on observed data volumes and processing capabilities. By adjusting this key parameter dynamically, the system can handle varying data loads efficiently while ensuring complete analysis of all incoming tuples, thus resolving the contradiction between analysis completeness and processing efficiency.
2Device complexity
If stream operators use precompiled fixed processing rates, then system simplicity is improved, but adaptability to varying data volumes deteriorates
Solution Approach 1:
The patent transforms the static processing rate into a dynamic parameter that adjusts automatically based on runtime conditions. The system maintains simplicity by implementing automatic adjustment mechanisms that respond to data volume changes without requiring complex manual configuration, thus improving adaptability while preserving system simplicity.
Solution Approach 2:
The stream computing system performs self-adjustment of processing rates based on its own runtime metrics and observed data flows. This self-service capability allows the system to adapt to varying data volumes automatically without external intervention, resolving the contradiction between maintaining simplicity and achieving adaptability.
3Productivity
If processing rate is increased to handle data surges, then productivity is improved, but data loss increases due to incomplete analysis
Solution Approach 1:
The patent implements a feedback mechanism that monitors the processing rate of stream operators and uses this information to dynamically adjust processing speeds. When data surges occur, the system receives feedback about processing capacity and adjusts rates accordingly to handle increased volume without losing data, thus resolving the contradiction between productivity and data loss prevention.
Solution Approach 2:
The system dynamically adjusts processing rates based on real-time conditions rather than using fixed rates. This dynamic approach allows the system to increase processing rate during data surges while maintaining complete analysis through adaptive coordination between operators, preventing data loss while improving productivity when needed.
Data Source
AI summary
A stream of tuples is monitored. The stream of tuples is to be processed by a plurality of processing elements of a stream application that operate on one or more compute nodes, each processing element having one or more stream operators. A processing rate of a first stream operator of the stream application is calculated. The processing rate is based on the number of tuples that are processed by the first stream operator. It is determined that the processing rate of the first stream operator meets a predetermined tuple processing criterion. The processing rate of the first stream operator is adjusted based on the predetermined tuple processing criterion.