Hash Database Configuration via Key-Value Segmentation
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Solution Overview
Problem
Existing hash databases face performance degradation due to large data volumes, resulting in inefficient key-value pair access and high disk input/output operations, especially when hash collisions occur and binary trees become deep, leading to performance jitter and long tails.
Innovation Solution
The method involves separating keys and values into distinct index and data areas on a disk, with the index area divided into equal-sized pages and an extended area for overflow management, using modulo operations for allocation and bitmap technology for efficient storage and retrieval, thereby reducing disk IO operations and improving performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a binary tree is used to resolve hash collisions in a variable-length hash K-V database, then collision resolution capability is improved, but when data volume is large, the tree becomes deep causing performance jitter and long tail latency
Solution Approach 1:
The patent segments the storage structure by separating keys and values into different areas (index area and data area), and further segments the index area into multiple index pages. This segmentation avoids the deep binary tree structure by distributing key-value pairs across multiple pages, reducing the maximum access depth and eliminating the long tail latency caused by deep tree traversal.
2Adaptability or versatility
If a variable-length hash K-V database stores key-value pairs using hash algorithm, then storage scalability is improved, but when hash collisions occur, the binary tree structure causes excessive disk IO operations
Solution Approach 1:
The patent introduces a new dimensional organization by creating multiple index pages (p pages) instead of a single deep tree structure. Keys are distributed across these pages using hash functions, and the system manages key-value pairs in a multi-page structure rather than a single deep tree, reducing the number of disk IO operations needed for access.
3Reliability
If the hash database uses a deep binary tree to handle large data volumes, then collision resolution is maintained, but performance dramatically degrades under high pressure
Solution Approach 1:
The patent performs preliminary actions by pre-allocating and organizing the index area into multiple pages before data insertion. The index pages are pre-structured to accommodate key-value pairs, and the system maintains this organized structure during operations, avoiding the performance degradation that occurs when dynamically building deep trees under high pressure.
Data Source
AI summary
A hash database configuration method and apparatus, where the method includes establishing an index area and a data area on a disk, where the index area includes p disk pages with an equal size, and receiving an allocation request of key-value pairs, allocating m keys in the key-value pairs to disk pages in the index area, and allocating n values in the key-value pairs to the data area, where m, n, and p are all integers greater than 1, and the efficiency of accessing the hash database is improved.