Blockchain Transaction Processing via Parallel Consensus Segmentation
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Solution Overview
Problem
The existing method of serial processing of transaction data by consensus nodes in blockchain systems results in low consensus performance and throughput, limiting the efficiency of the blockchain network.
Innovation Solution
A data processing method that identifies transaction data meeting specific execution conditions and packages them into a consensus-pending block, allowing parallel execution of certain transaction data while maintaining serial execution for others, thereby improving processing efficiency and throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If serial processing is used to ensure each transaction data is processed correctly, then processing accuracy is maintained, but system throughput decreases
Solution Approach 1:
The patent segments transaction data into different types (first transaction data and second transaction data) based on their execution conditions. First transaction data that meets specific conditions can be processed in parallel, while second transaction data is processed serially. This segmentation allows the system to apply different processing strategies to different data subsets, thereby improving throughput while maintaining accuracy for critical transactions.
Solution Approach 2:
The patent implements dynamic processing by determining execution conditions for transaction data and adjusting the processing mode accordingly. The system dynamically identifies which transaction data can be executed in parallel versus which must be executed serially based on real-time condition assessment, enabling flexible adaptation between serial and parallel processing to optimize both reliability and productivity.
2Productivity
If parallel processing is used to increase system throughput, then productivity improves, but processing complexity increases
Solution Approach 1:
The patent simplifies parallel processing complexity by segmenting transaction data into two categories: first transaction data suitable for parallel execution and second transaction data requiring serial execution. This clear segmentation provides a straightforward framework for implementing parallel processing without overwhelming system complexity, as the rules for determining which data type to process in parallel are well-defined and manageable.
3Device complexity
If all transaction data is processed serially to maintain simplicity, then system complexity remains low, but consensus performance deteriorates
Solution Approach 1:
The patent enhances consensus performance while maintaining relative simplicity through dynamic condition-based processing. The system automatically determines execution conditions for each transaction data and switches between serial and parallel modes as needed. This dynamic approach improves consensus performance by enabling parallel processing when conditions permit, while keeping the system design relatively simple through clear conditional rules rather than complex multi-layered processing architectures.
Data Source
AI summary
A data processing method includes: obtaining a plurality of groups of transaction data to be processed; determining, from the plurality of groups of transaction data according to transaction execution results of the plurality of groups of transaction data, at least one group of first transaction data that meets a preset execution condition; packaging the plurality of groups of transaction data, to generate a consensus-pending block, the consensus-pending block recording the at least group of first of first transaction data and at least one group of second transaction data, and the second transaction data being transaction data different from the first transaction data among the plurality of groups of transaction data; and performing first consensus processing on the first transaction data, and performing second consensus processing, different from the first consensus processing, on the second transaction data.