Distributed Graph Processing via Blockchain Consensus
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Solution Overview
Problem
Conventional graph computation methods based on centralization face inefficiencies due to high network throughput and data update delays, especially when processing hyper-scale data, leading to bottlenecks and inefficient computations.
Innovation Solution
A distributed graph data processing method utilizing a blockchain network for distributed computing node clusters, where subgraph data is processed, global data is shared, and local data is buffered, reducing network communication overhead and enabling iterative computations without a centralized driver.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized graph computation methods are used, then data consistency is maintained, but network throughput increases and data update delays occur
Solution Approach 1:
The patent segments the centralized computation system into multiple distributed computing nodes that independently process graph data. Each node maintains local state and communicates selectively with neighbors, eliminating the single-point bottleneck while preserving data consistency through consensus mechanisms inherent in blockchain technology.
Solution Approach 2:
The patent introduces a blockchain network as an intermediary layer that enables trustless coordination between distributed computing nodes. The blockchain serves as a shared ledger for global data updates, allowing nodes to synchronize without centralized control, thus maintaining consistency while improving throughput.
2Productivity
If distributed computing nodes process graph data independently, then network communication overhead is reduced, but data consistency may be compromised
Solution Approach 1:
The patent implements feedback mechanisms where computing nodes continuously monitor and update their local state based on global data changes propagated through the blockchain. This feedback loop ensures that independent processing does not lead to inconsistency, as nodes adjust their computations based on the latest verified global state.
Solution Approach 2:
The patent merges the advantages of distributed processing with the consistency guarantees of blockchain technology. By combining independent node execution with shared ledger verification, the system achieves both high processing efficiency and data consistency that neither approach could achieve alone.
3Loss of time
If global data is frequently updated across all nodes, then data freshness is improved, but network communication traffic increases
Solution Approach 1:
The patent applies local quality by allowing computing nodes to maintain and process data locally without requiring immediate synchronization with all other nodes. Nodes update their local state based on selective information from the blockchain, reducing unnecessary network traffic while maintaining data freshness where needed.
Solution Approach 2:
The patent implements partial action by updating only the necessary portions of global data across the network rather than broadcasting complete datasets. Nodes receive and process only the specific data changes relevant to their computations, minimizing communication overhead while ensuring data freshness for critical updates.
Data Source
AI summary
This application relates to a graph data processing method performed by a distributed computer node cluster including a plurality of computer devices, each computer device distributed on a respective computing node of the distributed computer node cluster, the method including: obtaining subgraph data divided from to-be-processed graph data; performing a computation task on the subgraph data to obtain corresponding global data and local data; writing the global data to a blockchain network, the global data of the blockchain network being updated by the distributed computing node cluster; obtaining latest global data from the blockchain network; and iteratively performing, according to the obtained latest global data and the local data, the computation task on the subgraph data without obtaining a computation result until an iteration stopping condition is met.