Blockchain Data Block Addressing for Direct KV Retrieval
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current blockchain data retrieval efficiency is low due to the inefficiencies in traversing key-value pairs in KV databases, which are commonly used for storing blockchain data.
Innovation Solution
The method involves dividing a designated physical storage space into data block cluster spaces, each comprising multiple block nodes, and using spatial information and indices to determine the target data block cluster space and storage address, enabling efficient reading and writing of data blocks through a one-to-one mapping between block nodes and data blocks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If KV database is used to store blockchain data with key-value pairs, then data can be stored in an organized form, but the retrieval efficiency is low due to traversing all key-value pairs
Solution Approach 1:
The patent divides the physical storage space into multiple data block cluster spaces, each containing multiple block nodes. Each block node stores a specific data block with a one-to-one mapping relationship. This segmentation eliminates the need to traverse all key-value pairs by directly locating the target block through cluster index and block index calculations, significantly improving retrieval efficiency.
Solution Approach 2:
The patent introduces spatial information as an intermediary layer between the storage structure and data retrieval operations. Spatial information records the mapping relationships between block nodes and physical storage locations, enabling direct address calculation without traversal. This intermediary mechanism transforms the inefficient key-value pair traversal into a direct address-based access method.
2Productivity
If data block cluster spaces are divided with multiple block nodes, then addressing efficiency is improved, but the storage structure becomes more complex
Solution Approach 1:
The storage space is segmented into data block cluster spaces with block nodes, where each block node has a clear one-to-one mapping to a data block. This segmentation provides a systematic addressing structure that improves efficiency while maintaining manageable complexity through regular patterns and index-based navigation.
Solution Approach 2:
The block node structure serves multiple functions: it acts as a storage unit for data blocks, an addressing unit through its index, and an organizational unit within cluster spaces. This multi-functionality reduces the need for separate structures, thereby managing complexity while providing efficient addressing capabilities.
3Productivity
If spatial information is recorded at designated locations, then retrieval operations can directly locate data blocks, but additional storage overhead is required
Solution Approach 1:
Spatial information is pre-recorded at designated locations within the storage structure, establishing the mapping relationships between block nodes and physical locations before data retrieval operations occur. This preliminary action enables direct address calculation during retrieval, significantly improving access speed while using storage space efficiently through compact encoding of position information.
Data Source
Figure 1~2
Figure 3~4
Figure 5
AI summary
Disclosed are a method and an apparatus for reading and writing data. The method includes: determining a target data block cluster space a target data block to be read and written belongs to; determining a target storage address corresponding to the target data block according to a starting address of the data block cluster space, a spatial size of the block nodes in the data block cluster space and a block index of the target data block; and performing reading operation or writing operation on the target data block according to the target storage address.