Live Buffer Data Structure for Variable Length Serialization
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Solution Overview
Problem
Current data serialization formats face challenges with overhead, lack of forward and backward compatibility, and limited support for variable length objects or fields, making them inefficient for storing and transmitting large amounts of rich data in distributed computing environments.
Innovation Solution
The Live Buffer format provides a flexible data structure and format for efficient serialization and deserialization, allowing for variable length fields and objects, with zero bit amortized schema overhead, enabling both forward and backward compatibility through a header that includes a schema identifier, version identifier, and stream flags, allowing for efficient storage and transmission of data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If fixed-length data structures are used for serialization, then storage and transmission overhead is reduced, but variable length fields cannot be efficiently represented
Solution Approach 1:
The patent implements dynamic field lengths by using variable-length encoding schemes where each field can adapt its size based on the actual data being serialized. The format includes length prefixes and type indicators that allow fields to be compact when small and expand when necessary, resolving the contradiction between fixed-size efficiency and variable-size flexibility.
Solution Approach 2:
The serialization format changes parameters dynamically by using different encoding schemes for different field types and sizes. The system adjusts the representation of data based on the actual values being serialized, using compact encodings for common cases and expanded encodings for edge cases, thereby optimizing both size and adaptability.
2Reliability
If schema overhead is included for each record, then data integrity and versioning are maintained, but storage and transmission efficiency is reduced
Solution Approach 1:
The patent merges schema information across multiple records by using a shared schema definition that is referenced rather than repeated. The schema identifier appears once per record, pointing to a global schema registry, thereby maintaining data integrity through schema validation while eliminating redundant schema overhead in the serialized data.
Solution Approach 2:
Instead of copying the full schema definition for each record, the system uses lightweight schema identifiers that reference the actual schema in a shared registry. This copying approach maintains reliability through schema enforcement while dramatically reducing the storage overhead compared to embedding complete schema definitions with each record.
3Adaptability or versatility
If legacy data formats are supported for backward compatibility, then older systems can process new data, but the format becomes more complex
Solution Approach 1:
The patent introduces an intermediary layer in the form of a schema registry and versioning system that mediates between old and new formats. The schema registry stores multiple version definitions, and the serialization process automatically selects and applies the appropriate schema version, enabling backward compatibility without requiring the data format itself to become more complex.
Solution Approach 2:
The system segments compatibility handling by separating the data format from the schema interpretation layer. The core binary format remains simple and efficient, while schema versioning and compatibility rules are handled in a separate, modular schema registry that can be extended without changing the fundamental data structure, thereby maintaining format simplicity while achieving backward compatibility.
4Quantity of substance
If compression is applied to reduce data size, then transmission efficiency is improved, but decompression time and processing overhead increase
Solution Approach 1:
The patent applies preliminary action by performing data grouping and sorting before serialization, organizing data in a way that naturally compresses better. By pre-processing data to group similar values together and eliminate redundancy at the source, the system achieves compression benefits without requiring intensive decompression algorithms, thereby reducing the time penalty typically associated with compression.
Data Source
AI summary
Data structures for the transmission or storage of records and the efficient serialization and deserialization of such records are disclosed. Embodiments of such a data structure offer a large and flexible data structure and format that may include an object format and a stream format. These data structures may be a packed sequence that may include fields of varying types and encodings with an order determined by the schema of a particular type of record being serialized.