Stream Processing System for Real-Time Data Aggregation and Control Feedback
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Solution Overview
Problem
Existing stream processing systems face maturity issues, leading to the need for customized solutions in large-scale data processors, particularly in addressing latency and resource management in real-time data processing for control loop feedback algorithms.
Innovation Solution
A stream processing system comprising a publish/subscribe and message queueing subsystem, integrated with an execution subsystem using Apache Kafka and Apache Hadoop YARN, which processes real-time transaction streams to aggregate data and provide input for control loop feedback processes, reducing latency and optimizing resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing stream processing systems are used, then basic data processing is possible, but latency is high and the systems are not mature enough for large-scale data processors
Solution Approach 1:
The system divides stream processing into separate specialized components: a publish/subscribe subsystem for message routing, a message queueing subsystem for buffering, and an execution subsystem for processing. This segmentation allows each component to be optimized independently, reducing overall latency while maintaining reliability.
Solution Approach 2:
The patent introduces message queues as intermediary components between event producers and consumers. These queues buffer messages and regulate flow, decoupling the timing of event generation from processing, thereby reducing latency spikes while maintaining system stability and maturity.
2Adaptability or versatility
If customized stream processing solutions are developed, then unique problems in large-scale data processors can be addressed, but system complexity increases
Solution Approach 1:
The patent creates a universal stream processing architecture that can handle multiple unique problems through configuration rather than custom development. The publish/subscribe model and message queueing mechanisms provide versatile, reusable components that adapt to different large-scale data processing scenarios without increasing inherent system complexity.
3Speed
If real-time data processing is implemented, then control loop feedback can be achieved, but resource management becomes challenging
Solution Approach 1:
The system implements periodic processing through time-based triggers and batch operations on accumulated messages. Instead of continuous processing of every message, the execution subsystem processes messages in periodic batches or based on time intervals, reducing resource consumption while maintaining real-time feedback capabilities through configurable processing frequencies.
Data Source
AI summary
Systems and methods for real-time message processing and control loop feedback are described. A stream processing system includes (i) a publish/subscribe and message queueing subsystem and (ii) an execution subsystem. A real-time stream of transactions is received at the publish/subscribe and message queueing subsystem. A job is created to aggregate data from the real-time stream of transactions with data from one or more other streams received at the publish/subscribe and messaging queueing subsystem. The job is executed at the execution subsystem, and the aggregated data resulting from the execution of the job is received at the publish/subscribe and message queueing subsystem. At least a portion of the aggregated data is provided as input to a control loop feedback process, which is executed to generate a result.