Predictive Determinism in Stream Computing Processing Elements
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Solution Overview
Problem
Current database systems face limitations in processing large volumes of data in real-time due to the rate at which they can receive and store incoming data, which restricts their ability to handle high data throughput in stream computing applications.
Innovation Solution
Implementing a method that uses predictive determinism by establishing an operator graph in stream computing applications, where processing elements can transmit predicted output data based on previously computed values if the output is deterministic, thereby reducing the need for processing current input data and alleviating backpressure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If database systems process data using traditional store-then-query model, then data storage is achieved, but real-time processing performance deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-computing and caching output values for deterministic processing elements. When an input value is received, the system checks if the corresponding output has been pre-computed and cached, allowing immediate retrieval without executing the full processing logic, thus achieving both data storage reliability and real-time processing performance
Solution Approach 2:
The system dynamically adapts its processing strategy based on determinism analysis. Processing elements are classified as deterministic or non-deterministic, and the system selectively applies caching strategies to deterministic elements while maintaining full processing for non-deterministic ones, optimizing real-time throughput without sacrificing storage reliability
2Quantity of substance
If database systems increase data reception and storage rate, then more data can be stored, but processing capacity becomes a bottleneck
Solution Approach 1:
The system creates copies of previously computed output values and stores them in a cache structure. When the same input values are received again, the system uses these cached copies instead of re-computing, allowing the system to handle larger data volumes without increasing processing capacity requirements
Solution Approach 2:
The system performs preliminary computation and caching of output values for deterministic processing elements. This preliminary action enables the system to rapidly handle subsequent identical or similar data streams without proportionally increasing processing capacity, thus resolving the bottleneck between data volume and processing speed
3Measurement precision
If processing elements process all current inputs in real-time, then processing accuracy is maintained, but system backpressure increases
Solution Approach 1:
The system implements feedback through determinism analysis, where the output of processing elements is analyzed to determine if it follows predictable patterns. This feedback mechanism allows the system to switch between full processing mode (for accuracy) and cached output mode (for reducing backpressure), maintaining processing accuracy when needed while reducing system stress during high-load periods
Solution Approach 2:
The system dynamically adjusts its processing behavior based on the determinism characteristics of each processing element. Deterministic elements use cached outputs to reduce backpressure, while non-deterministic elements maintain full processing to ensure accuracy, creating a balanced system that manages both accuracy and backpressure effectively
Data Source
AI summary
Techniques are described for transmitting predicted output data on a processing element in a stream computing application instead of processing currently received input data. The stream computing application monitors the output of a processing element and determines whether its output is predictable, for example, if the previously transmitted output values are within a predefined range or if one or more input values correlate with the same one or more output values. The application may then generate a predicted output value to transmit from the processing element instead of transmitting a processed output value based on current input values. The predicted output value may be, for example, an average of the previously transmitted output values or a previously transmitted output value that was transmitted in response to a previously received input value that is similar to a currently received input value.