Blockchain Validation Database Segmentation for Transaction Speed
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Solution Overview
Problem
Current blockchain transaction validation processes are inefficient due to the need to access large state databases, leading to performance bottlenecks and slow transaction throughput.
Innovation Solution
Implementing a validation database that maintains only a limited amount of data corresponding to the most recent blocks, allowing for in-memory storage and reducing the need to access slower storage media, thereby improving transaction validation efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the entire state database is accessed for transaction validation, then complete data consistency is ensured, but validation speed decreases due to slow storage access
Solution Approach 1:
The state database is segmented into two separate databases: a validation database containing only recent block state information needed for fast validation, and a full state database maintaining complete historical data for consistency. This segmentation allows the validation database to operate at high speed while the full database ensures data consistency, resolving the contradiction between speed and reliability.
Solution Approach 2:
A copy of the recent state data is created in the validation database, separate from the full state database. This copy contains sufficient information for transaction validation without requiring access to the entire state database, enabling fast validation while maintaining the integrity of the original full database for consistency checks.
2Productivity
If a validation database with limited data is used, then validation speed increases, but the complexity of maintaining data consistency increases
Solution Approach 1:
The system segments database maintenance into two independent processes: one for the validation database that only needs to maintain recent block data, and another for the full state database that maintains complete consistency. This segmentation reduces the complexity of maintaining data consistency in the validation database while enabling high throughput.
Solution Approach 2:
The validation database extracts and stores only the essential recent state information needed for validation, separating this function from the full state database. This extraction reduces the data volume and maintenance complexity in the validation database while preserving the ability to maintain high transaction throughput.
3Productivity
If in-memory storage is used for validation, then validation efficiency improves, but memory resource consumption increases
Solution Approach 1:
The system segments storage resources into two databases with different requirements: the validation database uses in-memory storage for recent data to achieve high validation efficiency, while the full state database uses disk storage for complete data. This segmentation allows efficient in-memory validation without requiring all data to be stored in memory, thus reducing overall memory consumption.
Solution Approach 2:
Different storage qualities are applied locally to different parts of the system: the validation database uses high-speed in-memory storage for recent data that requires fast access, while the full state database uses standard disk storage. This local quality differentiation optimizes validation efficiency for recent data without consuming excessive memory resources for historical data.
Data Source
Figure 1A
Figure 1B
Figure 2A
AI summary
An example operation may include one or more of creating, by a blockchain node, a validation database in a memory, simulating each of one or more transactions, determining that each transaction in a new block may be validated using the validation database, validating each transaction in the new block using the validation database, and committing the new block to the blockchain. The validation database includes data corresponding to a predetermined number of most recent blocks of a state database for a blockchain.