Binary XML Incremental Encoding for Lower Server Load
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Solution Overview
Problem
Current database systems face inefficiencies in handling binary XML data due to the need for server-side translation, which consumes network bandwidth and server resources, and lacks scalability when converting between binary and textual formats.
Innovation Solution
Implementing client-side translation and incremental encoding techniques, including the use of client-side caches and on-demand encoding, to reduce server workload and improve scalability by performing translations locally and only encoding data in response to specific requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If server-side translation is used to convert binary XML to textual XML, then the data can be meaningfully accessed by applications, but network bandwidth is consumed and server resources are overloaded
Solution Approach 1:
The patent implements client-side translation where the client application performs the conversion from binary XML to textual XML locally, without requiring server-side translation. This self-service approach eliminates network bandwidth consumption for translation operations and reduces server resource usage, while still providing meaningful data access to applications
2Ease of operation
If server-side translation is implemented for binary XML conversion, then data can be accessed by applications, but server computational overhead increases
Solution Approach 1:
The translation functionality is moved from the server to the client application. The client application loads translation information from the database and performs binary-to-textual XML conversion locally, eliminating the computational overhead on the server while maintaining full data accessibility for applications
3Productivity
If translation information is stored in the database, then binary XML can be encoded efficiently, but additional storage space is required
Solution Approach 1:
The patent stores translation information in a dedicated translation information table with a localized schema that maps tokens to replacement values. This localized storage structure enables efficient encoding operations by providing quick access to translation mappings, while the actual translation data is stored separately from the binary XML content, optimizing both encoding efficiency and storage utilization
Data Source
AI summary
Various techniques are described hereafter for improving the efficiency of binary XML encoding and loading operations. In particular, techniques are described for incrementally encoding XML in response to amount-based requests. After encoding enough binary XML to satisfy an amount-based request, the encoder stops encoding the XML until a subsequent request is received. The incremental encoding may take place on the client-side or the server-side. Techniques are also described for reducing the character set conversion operations by having a parser convert tokens in text XML into one character set while converting non-token text in the text XML into another character set. Techniques are also described for generating self-contained binary XML documents, and for improving remap operations by providing a binary XML document on a chunk-by-chunk basis.