Incremental Hash Collision Tables Using Bloom Filters
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Solution Overview
Problem
Hash collisions in relational databases require complex data structures and additional processing overhead, reducing the efficiency of hashing operations and increasing storage needs.
Innovation Solution
The implementation of incremental build functionality for hash collision tables using Bloom filters and resolution dictionaries to efficiently manage hash collisions, allowing for probabilistic determination of element presence and reducing processing overhead by incrementally updating data structures without rebuilding from scratch.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex data structures are used to resolve hash collisions, then hash collision resolution capability is improved, but storage overhead and processing overhead increase
Solution Approach 1:
The patent introduces a Bloom filter as an intermediary data structure between the hash table and collision resolution mechanisms. The Bloom filter probabilistically determines whether elements exist in the hash table before full collision resolution is performed, acting as a mediator that filters out non-colliding elements and reduces the burden on complex collision resolution structures.
Solution Approach 2:
The patent replaces traditional mechanical collision resolution methods (such as chaining or open addressing with complex probes) with a probabilistic filtering mechanism using Bloom filters. This substitution reduces the mechanical complexity of collision resolution by using bitwise operations and probabilistic membership testing instead of direct structural manipulation.
2Reliability
If complex data structures are used to resolve hash collisions, then hash collision resolution capability is improved, but processing overhead increases
Solution Approach 1:
The patent applies partial action by using the Bloom filter to perform a preliminary, partial check for element existence before initiating full collision resolution procedures. This partial filtering action eliminates many non-colliding elements early in the process, reducing the overall processing overhead required for complete collision resolution.
Solution Approach 2:
The Bloom filter performs preliminary filtering before actual hash collision resolution is needed. By pre-computing and storing bit patterns for elements in the hash table, the system prepares in advance to quickly determine whether new elements are likely to collide, reducing processing overhead during actual operations.
3Productivity
If incremental build functionality is used for hash collision tables, then processing overhead is reduced, but implementation complexity increases
Solution Approach 1:
The incremental build mechanism uses preliminary action by pre-computing Bloom filter bit patterns for elements as they are inserted into the hash table. Rather than rebuilding the entire collision table when collisions occur, the system performs preliminary filtering using the Bloom filter, reducing processing overhead while managing implementation complexity through staged updates.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for incrementally building hash collision tables. In some embodiments, hashes and hash collision tables may be used to improve efficiency of relational operations, such as those used in relational databases. An embodiment operates by determining hash collisions between data entries, then storing newly determined hash values corresponding to collisions in a hash collision table. The storing can be done incrementally, without needing to rebuild hash tables or hash collision tables for all data entries.