Dictionary Encoding Conversion for Dense and Sorted Database Codes
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Solution Overview
Problem
Current database systems face inefficiencies in generating dense and sorted codes for dictionary encodings, which affect query processing and resource utilization, particularly in multi-node database systems where sparse and composite codes require significant storage and processing resources.
Innovation Solution
The implementation of relational approaches using specialized SQL operators like ENCODE_FIRST(), ENCODE_FLAG_FIRST(), and ENCODE_SPARSE_CODE() to generate dense and sorted codes through a series of database queries that create unsorted-encoded, unsorted-to-sorted, and encoded base tables, optimizing code assignment and reducing storage needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If sparse and composite codes are used in multi-node database systems, then storage capacity is increased, but storage requirements and processing resources are significantly increased
Solution Approach 1:
The patent segments the code generation process into multiple specialized SQL operators (ENCODE_FIRST, ENCODE_FLAG_FIRST, ENCODE_SPARSE_CODE) that operate in a pipeline fashion. Each operator handles a specific transformation step, dividing the complex encoding task into manageable segments that can be processed efficiently across database nodes.
Solution Approach 2:
The patent performs preliminary actions by pre-generating unsorted-encoded tables and unsorted-to-sorted mappings before final encoding. This allows the system to prepare code assignments in advance, reducing processing resources needed during actual query execution and enabling more efficient storage utilization.
2Manufacturing precision
If dense and sorted codes are generated through traditional methods, then code assignment accuracy is improved, but query processing efficiency is reduced
Solution Approach 1:
The patent generates unsorted-to-sorted mappings and code assignments in advance through preliminary SQL queries. This pre-computation ensures accurate code assignment while eliminating the need for complex sorting operations during query processing, thereby maintaining both precision and efficiency.
Solution Approach 2:
The patent introduces an intermediary unsorted-encoded table that serves as a bridge between raw data and final sorted codes. This intermediary structure allows accurate code assignment to be performed separately from the encoding process, enabling both high precision and efficient query processing.
3Quantity of substance
If traditional encoding methods are used, then storage capacity is maintained, but storage requirements increase
Solution Approach 1:
The patent discards intermediate unsorted representations and recovers only the essential sorted code mappings needed for final encoding. This approach maintains full storage capacity while reducing storage requirements by eliminating redundant unsorted data structures after they serve their purpose in the encoding pipeline.
Data Source
AI summary
Unsorted sparse dictionary encodings are transformed into unsorted-dense or sorted-dense dictionary encodings. Sparse domain codes have large gaps between codes that are adjacent in order. Unlike spare codes, dense codes have smaller gaps between adjacent codes; consecutive codes are dense codes that have no gaps between adjacent codes. The techniques described herein are relational approaches that may be used to generate sparse composite codes and sorted codes.