Canonical Byte Stream Translation for Database Index Comparison
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Solution Overview
Problem
Distributed databases face inefficiencies in data retrieval operations due to the complexity of comparing index data, particularly in non-relational databases that store data without a schema, where index data often includes references to other documents and arrays, complicating searches and impacting performance.
Innovation Solution
A system and method that translate index data into a canonical byte stream format for efficient byte-by-byte comparison, using a translation component to map data types and encode data elements into a standardized format, allowing for faster and more efficient data retrieval by reducing the need for complex comparison logic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is stored in non-relational format with references and arrays, then data flexibility and schema-less storage are improved, but data comparison complexity and retrieval efficiency deteriorate
Solution Approach 1:
The patent segments the data comparison process into two distinct phases: (1) translation of non-relational data into canonical byte stream format, and (2) byte-by-byte comparison of the translated data. This segmentation allows the system to maintain flexible non-relational data storage while achieving efficient comparison operations through the standardized canonical format.
Solution Approach 2:
The patent introduces a translation component as an intermediary between the non-relational data storage format and the comparison operation. This intermediary translates the flexible but complex non-relational data into a canonical byte stream format that enables efficient byte-by-byte comparison, thus resolving the contradiction between data flexibility and retrieval efficiency.
2Reliability
If complex comparison logic is used to handle references and arrays, then data comparison accuracy is improved, but operation speed and scalability deteriorate
Solution Approach 1:
The patent changes the fundamental parameter of data representation from non-relational format with references and arrays to canonical byte stream format. This parameter change transforms the comparison operation from complex logic-based comparison to simple byte-by-byte comparison, maintaining accuracy while dramatically improving operation speed and scalability.
Solution Approach 2:
The patent substitutes complex comparison logic (mechanical system) with a simpler byte-by-byte comparison mechanism. By translating data into canonical format, the system replaces the need for complex interpretation of references and arrays with straightforward binary comparison operations, achieving both accuracy and speed.
3Stability of the object's composition
If index data is maintained in original format, then data structure integrity is preserved, but memory usage and processing overhead increase
Solution Approach 1:
The patent creates a translated copy of the index data in canonical byte stream format while maintaining the original non-relational data structure. This copying approach allows the system to preserve data structure integrity in the original format while using the compact canonical copy for efficient comparison operations, optimizing memory usage through selective translation of index data.
Data Source
AI summary
According to one embodiment, a translation component is configured to operate on document encoded data to translate the document encoded data into a canonical format comprising a plurality of canonical types that fold together into a byte stream. The translation component is configured to accept any storage format of data (e.g., column store, row store, LSM tree, etc. and/or data from any storage engine, WIREDTIGER, MMAP, AR tree, Radix tree, etc.) and translate that data into a byte stream to enable efficient comparison. When executing searches and using the translated data to provide comparisons there is necessarily a trade-off based on the cost of translating the data and how much the translated data can be leveraged to increase comparison efficiency.