Balanced Chunked Tree Collections for Targeted Document Access
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Solution Overview
Problem
Current data structures in collaboration applications require entire versions of documents to be downloaded or uploaded frequently, leading to high storage and bandwidth costs, as well as increased latency and processing demands, especially when dealing with lists or ranges of data.
Innovation Solution
Implementing scalable collections within a balanced chunked tree data structure, such as SPICE trees, allows targeted access to specific data elements, reducing the need to download unnecessary data and enabling efficient management of varying data sizes and shapes, while maintaining persistent data structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If entire versions of the document are downloaded and uploaded periodically, then data consistency is maintained, but storage and bandwidth costs increase significantly
Solution Approach 1:
The patent segments the document data structure into hierarchical chunks (root chunk, reference chunks, and leaf chunks). Instead of downloading entire document versions, clients only download specific chunks containing the data they need to access or modify. This segmentation enables partial updates and reduces bandwidth consumption while maintaining data consistency through version vectors and chunk-level tracking.
2Reliability
If entire versions of the document are downloaded and uploaded periodically, then data consistency is maintained, but processing requirements increase
Solution Approach 1:
By dividing the document into hierarchical chunks, the patent reduces the processing burden on clients. Instead of parsing and managing entire document versions, clients only process the specific chunks they need to access or modify. The server manages chunk-level operations and version tracking, distributing processing requirements and reducing client device complexity.
3Ease of operation
If B-tree data structures are used to break down data into branches and leaves, then data organization is improved, but entire branches and surrounding leaves must still be downloaded
Solution Approach 1:
The patent extends the B-tree concept by further segmenting branches into independent reference chunks that can be selectively downloaded. Each reference chunk contains metadata and references to specific leaf chunks, enabling clients to download only the minimal set of chunks needed to access particular data, rather than entire branches as in traditional B-trees.
Solution Approach 2:
The patent introduces an additional hierarchical dimension between the traditional B-tree branches and leaves by inserting reference chunks. This creates a three-level hierarchy (root chunk → reference chunks → leaf chunks) that enables more granular control over data retrieval, allowing clients to skip downloading unnecessary intermediate branches while maintaining efficient navigation.
4Productivity
If binary blobs are uploaded and downloaded separately, then data transfer efficiency is improved, but the underlying data structures have no incrementality and do not scale well
Solution Approach 1:
The patent segments binary blob data into hierarchical chunks that can be independently managed and transferred. Each chunk is assigned a version vector and can be updated, added, or removed independently. This segmentation provides incrementality, allowing the system to scale efficiently by adding or modifying individual chunks without requiring complete data structure replacements.
Solution Approach 2:
The patent implements dynamic chunk management where the data structure can adapt to changing requirements. Chunks can be dynamically added, removed, or modified based on actual usage patterns and data changes. The version vector system enables dynamic tracking of chunk states, allowing the structure to scale flexibly as data grows or evolves without requiring predetermined fixed-size allocations.
Data Source
AI summary
Systems and methods for using a scalable collection within a balanced tree data structure are provided herein. In particular, techniques for generating scalable collections and using scalable collections for fetching data corresponding to an application using balanced chunked data tree structures are provided herein. In an example, the method may include determining a document defined by a balanced tree data structure having a root chunk, reference chunks, and chunks each having nodes each corresponding to a document attribute. The nodes may include a scalable collection containing a list node and child nodes associated with the list node, and a placeholder node that provides a reference to another chunk based on a position of the placeholder node within a respective chunk. The method may also include navigating to content within the document based on the balanced tree data structure.