Key Trie Encoding Method for Memory Efficiency
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Solution Overview
Problem
Long key lengths in key Tries require significant memory and computational resources for storage and retrieval, making direct search inefficient.
Innovation Solution
An encoding method that generates meta data for non-leaf nodes in a key Trie, reducing storage needs and enabling efficient decoding through selective updating of key index values based on input key bits, with a buffer size requirement of (N−1)*(log2 M+log2 N) and decoding complexity of O(N) in the worst case.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If direct search is performed based on the key Trie with long keys, then the key-value retrieval function is achieved, but large memory capacity and many comparison computations are required
Solution Approach 1:
The patent segments the key Trie into non-leaf nodes and leaf nodes, encoding only the non-leaf nodes into compact meta data. This segmentation allows the system to store only the necessary structural information (non-leaf nodes) while omitting redundant data (leaf node details), thereby reducing memory requirements while maintaining search functionality through the encoded path information.
Solution Approach 2:
The patent extracts the essential information from the key Trie by taking out only the non-leaf nodes and encoding them into meta data. The leaf nodes and their associated values are not stored in the compressed structure. Instead, the extracted non-leaf node information suffices to navigate back to the correct leaf node during decoding, achieving space efficiency while preserving retrieval capability.
2Reliability
If the key Trie stores all non-leaf nodes, then the tree structure is complete, but the storage space required increases significantly
Solution Approach 1:
The patent applies local quality by differentiating the encoding requirement based on node type: non-leaf nodes are encoded into compact meta data with specific structure (depth value and left sub-Trie leaf count), while leaf nodes are not encoded. This selective encoding approach maintains the necessary structural information for navigation (local quality of non-leaf nodes) while avoiding redundant storage of leaf node details, achieving space efficiency without compromising tree structure completeness for search purposes.
Data Source
AI summary
An encoding method for a key Trie includes generating a plurality of meta data by applying encoding to a portion of non-leaf nodes of the key Trie, and storing an encoding result of the key Trie into a storage device, wherein the encoding result includes the plurality of meta data corresponding to the portion of non-leaf nodes, respectively.