Hot Table Hash Structure With Cold Data Self-Eviction
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Solution Overview
Problem
Existing data structures like hash tables face inefficiencies in memory utilization and performance due to the need for explicit deletion mechanisms and resizing, especially when dealing with data that quickly becomes irrelevant, leading to high CPU usage and memory overhead.
Innovation Solution
The implementation of hot tables with multiple hash tables and a kicking mechanism that automatically evicts cold data, using dual indices and probabilistic distribution to manage data retention and memory efficiently, allowing for constant-size data structures that adapt to changing data relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a hash table is overloaded with data to improve memory utilization, then memory utilization is improved, but operation speed deteriorates due to increased memory jumps and page faults
Solution Approach 1:
The data structure is divided into multiple independent hash tables (first hash table, second hash table) with separate buckets and slots. This segmentation allows the system to distribute data across multiple tables, reducing the number of memory jumps required for operations while maintaining high memory utilization through controlled overloading of individual tables.
Solution Approach 2:
The patent introduces a temporal dimension through indices (valid index, least valid index) to track data age and relevance. This allows the system to manage overloaded hash tables by evicting cold data (data with invalid indices) while preserving hot data, thereby maintaining both high memory utilization and operation speed.
2Reliability
If traditional hash tables retain data until explicit deletion, then data availability is improved, but memory overhead increases due to retained irrelevant data
Solution Approach 1:
The system performs preliminary actions by assigning indices to data upon insertion and automatically invalidating indices when data becomes cold or irrelevant. This preliminary indexing and invalidation mechanism enables the hash table to proactively identify and evict cold data before it consumes excessive memory, balancing data availability with memory efficiency.
Solution Approach 2:
The data structure performs self-service through automatic eviction of cold data based on index validation. When data is inserted or accessed, the system automatically updates indices and evicts data with invalid indices, eliminating the need for explicit deletion operations and manual memory management while maintaining optimal memory utilization.
3Ease of operation
If explicit deletion mechanisms are implemented, then data management control is improved, but CPU overhead increases due to deletion operations
Solution Approach 1:
The system eliminates explicit deletion operations by implementing self-service eviction through index validation. Data with invalid indices is automatically evicted during insertion or access operations, removing the need for separate deletion mechanisms and significantly reducing CPU overhead associated with explicit deletion control.
Solution Approach 2:
The system performs preliminary index invalidation when data becomes cold or irrelevant, rather than waiting for explicit deletion requests. This preliminary action allows the system to automatically manage data lifecycle without requiring CPU-intensive deletion operations, maintaining ease of operation while reducing overhead.
Data Source
AI summary
A hot table is a data structure designed to manage hot data while efficiently discarding cold data. The base layer of it are hash tables with predetermined and preallocated number of buckets and slots, unlike tables based on linked lists. A hot table has complexity of O(1) for SET/GET/DEL commands and no overhead by harnessing the kicking mechanism to discard cold data, acting as a garbage collector. The metadata kept with the key allows for complex eviction policies such as LRU and LFU. Hot tables impose minimal performance and memory overhead which makes them suitable for deployment in both hardware and software development environments.