Coordinating Server Selection for Blockchain Data Processing
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Solution Overview
Problem
The high frequency of read and write operations by client terminals on blockchain servers leads to increased access pressure and reduced data processing speed in existing data processing systems.
Innovation Solution
A method and apparatus that select a target coordinating server based on data attributes to distribute data processing requests, allowing server clusters to process requests preferentially, thereby achieving load balancing and accelerating data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If client terminals perform frequent read and write operations on the blockchain server, then the system can maintain real-time data accuracy and operational status, but the access pressure on the blockchain server increases and data processing speed decreases
Solution Approach 1:
The system segments the monolithic blockchain server into multiple coordinating servers organized in server clusters. Each coordinating server handles specific data processing tasks, dividing the overall system load into manageable portions. This segmentation allows parallel processing of read and write operations across multiple servers, improving data processing speed while maintaining data accuracy through distributed consensus mechanisms.
Solution Approach 2:
Coordinating servers act as intermediaries between client terminals and the core blockchain network. These intermediaries buffer and manage frequent read and write operations, preprocessing requests and caching data to reduce direct access pressure on the main blockchain server. The coordinating servers maintain data accuracy by validating operations against blockchain consensus rules while improving processing speed through local optimization.
2Device complexity
If all data processing requests are handled by a single blockchain server, then system architecture remains simple, but access pressure concentrates on one server reducing overall processing efficiency
Solution Approach 1:
The system divides the single blockchain server into multiple coordinating servers grouped in clusters, with each server handling specific subsets of data processing requests. This segmentation distributes access pressure across multiple nodes while maintaining a relatively simple architecture through standardized server interfaces and centralized coordination mechanisms. The modular design allows linear scaling by adding more coordinating servers as needed.
Solution Approach 2:
The system transitions from a single-server vertical architecture to a multi-server horizontal cluster architecture. By organizing coordinating servers in clusters with defined relationships and communication protocols, the system adds dimensional complexity in terms of distribution and topology while maintaining operational simplicity through standardized interfaces. This dimensional change enables parallel processing and improved efficiency without dramatically complicating the overall system structure.
Data Source
AI summary
Embodiments of the present specification provide a method, an apparatus, a client terminal, and a server for data processing. The method includes: selecting, based on a data attribute of to-be-processed data, a target coordinating server from a plurality of coordinating servers, the plurality of coordinating servers belonging to a plurality of server clusters respectively; and sending a data processing request to the target coordinating server, such that a server cluster to which the target coordinating server belongs processes the data processing request preferentially, the data processing request directing to the to-be-processed data.