Database Compression User-Defined Data Types
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Solution Overview
Problem
Conventional data compression techniques in database systems are inefficient, particularly for user-defined data types, as they often rely on brute force compression of entire data blocks or specific value compression, failing to optimize compression for all data values stored in tables.
Innovation Solution
The implementation of user-defined data types (UDTs) with associated compression routines, such as CompressUDT, which includes data structures like Kind and PCTCompressed, and methods like AlterCompression() and GetCompressionDensity(), allows for tailored compression algorithms to be applied to specific data objects, enhancing compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If brute force compression is applied to entire data blocks, then data storage space is reduced, but compression efficiency is poor for all data values
Solution Approach 1:
The patent applies different compression routines to different data values based on their specific characteristics rather than uniformly compressing entire data blocks. Each user-defined data type can have its own optimized compression routine, achieving local optimization for specific data patterns while maintaining overall compression effectiveness.
Solution Approach 2:
The patent changes the approach from fixed block-based compression to variable compression based on data type parameters. By associating compression routines with user-defined data types, the system dynamically selects appropriate compression parameters and methods based on the specific data being compressed, improving overall compression efficiency.
2Adaptability or versatility
If compression is applied to specific data values like null and most frequently used values, then compression is available for certain data values, but it is not available for other data values stored in tables
Solution Approach 1:
The patent creates a universal compression framework where user-defined data types can have associated compression routines. This allows compression to be universally applied across all data values by defining appropriate compression routines for each data type, rather than being limited to specific values like null or frequent values.
Solution Approach 2:
The patent introduces dynamic compression capability where the compression routine is selected based on the user-defined data type of the data being compressed. This dynamic approach allows the system to adapt compression methods to match the specific characteristics of each data type, ensuring both availability and efficiency.
3Productivity
If user-defined data types with compression routines are implemented, then compression efficiency is optimized for all data values, but system complexity increases
Solution Approach 1:
The patent segments the compression functionality into separate user-defined data type definitions and associated compression routines. This segmentation allows the complexity to be distributed and managed individually for each data type rather than implementing a single complex universal compression system, making the overall system more manageable despite the added functionality.
Data Source
AI summary
In a database system, compression user-defined data types (UDTs) are defined to enable compression of data. Each compression UDT is associated with one or more UDT methods to compress data according to one or more respective compression algorithms. Also, one or more data structures are associated with data stored according to each compression UDT to store values pertaining to the compression of the data.