Dynamic Load Balancing in Stream Processing Parallel Regions
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Solution Overview
Problem
Current stream processing methods are inefficient in handling large volumes of data from sources like IoT devices and sensors, leading to missed real-time trends due to the need for costly memory resources and delayed processing.
Innovation Solution
A method and system for dynamically balancing data flow in parallel regions of a distributed data processing system, using attributes of the data and computing system to adjust operators and resources, allowing for real-time or near real-time processing without prior data storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If data is stored in database or file system before processing, then data can be queried and computed as needed, but processing delay increases and real-time trends are missed
Solution Approach 1:
The patent applies preliminary action by pre-processing and filtering data at the source before it enters the main processing system. Data is transformed and enriched in advance, so when it arrives at the processing system, it is already in the required format and ready for immediate analysis, eliminating the need for storage and subsequent querying delays
Solution Approach 2:
The patent extracts only the essential and relevant data elements from incoming data streams, filtering out redundant information. This extraction occurs at the data source or entry point, allowing the system to process only critical data in real-time without being burdened by complete data sets that would require storage and later retrieval
2Quantity of substance
If memory resources are increased to handle large data volumes, then more data can be processed, but system cost increases
Solution Approach 1:
The system extracts and processes only the most critical and relevant data elements from incoming streams, filtering out redundant or less important information. This selective extraction allows the system to handle large volumes of incoming data while using minimal memory resources to store and process only the essential subset
Solution Approach 2:
The patent segments the data processing workflow into multiple stages: filtering at source, selective extraction of key elements, incremental processing, and hierarchical storage. This segmentation allows different data portions to be handled with appropriate resource allocation, minimizing overall memory requirements while maintaining处理能力 for large data volumes
3Adaptability or versatility
If fixed number of operators are used in parallel region, then system is simpler to manage, but cannot adapt to changing data flow rates
Solution Approach 1:
The patent implements dynamic operator allocation where the number of parallel operators automatically adjusts based on real-time data flow characteristics. When data flow rate increases, more operators are activated; when it decreases, operators are deactivated. This dynamic adaptation maintains optimal processing performance without manual intervention
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor data flow rates, processing queue depths, and operator performance metrics. This feedback information is used to automatically adjust the number of active operators, creating a self-regulating system that adapts to changing conditions while maintaining manageable complexity through automated control
Data Source
AI summary
In response to receiving a flow of data, one or more operators in a parallel region included in a distributed data processing system for stream processing the flow of data are initialized. In response to identifying a change to the flow of data or a change to the distributed data processing system, at least one of the flow of data and a size of the parallel region included in the distributed data processing system are dynamically adjusted. The flow of data is processed via the dynamically adjusted one of the flow of data and the parallel region included in the distributed data processing system.