K-D Tree Split Layout for Non-Overlapping Data Files
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data retrieval systems face inefficiencies due to overlapping file boundaries, requiring multiple files to be read to retrieve specific data, leading to increased response times.
Innovation Solution
A system that clusters data into non-overlapping files by recursively splitting data along multiple dimensions based on split points, ensuring each file has a predefined target size and stores metadata for quick lookup.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is split into multiple files for efficient storage and retrieval, then data storage efficiency is improved, but file boundary overlap causes multiple files to need to be read increasing response time
Solution Approach 1:
The patent divides the data space into multiple partitions along multiple dimensions (e.g., row keys, column families, time ranges), creating a multi-dimensional segmented structure. Each file corresponds to a specific partition range, and the segmentation is organized hierarchically with top-level partitions and sub-partitions, allowing precise location of data without scanning overlapping files.
Solution Approach 2:
The patent introduces multi-dimensional partitioning beyond simple horizontal splitting. Data is partitioned along multiple dimensions simultaneously (e.g., row key ranges, column family groups, time intervals), creating a multi-dimensional address space. This dimensional expansion allows the system to locate data through coordinate-based addressing rather than linear file scanning, dramatically reducing retrieval time while maintaining storage efficiency.
2Adaptability or versatility
If files are created with overlapping boundaries to provide flexibility, then adaptability is improved, but the number of files that need to be read increases reducing retrieval efficiency
Solution Approach 1:
The patent implements a nested partition structure where top-level partitions contain sub-partitions, which may further contain sub-sub-partitions. This hierarchical nesting provides multiple levels of granularity and flexibility. The system can adapt to different query patterns by selecting appropriate partition levels, while the nested structure ensures that data can be located at the most specific partition level without requiring scans across overlapping file boundaries.
Solution Approach 2:
Different partitions are optimized for different access patterns and data characteristics. Each partition can have customized properties such as compression settings, storage formats, and access policies tailored to its specific data type and access frequency. This local optimization allows the system to maintain high retrieval efficiency for each partition while providing overall system flexibility through diverse partition configurations.
3Loss of time
If data is clustered into non-overlapping files with target sizes, then retrieval efficiency is improved, but complex splitting logic is required increasing system complexity
Solution Approach 1:
The patent uses configurable parameters to control partitioning behavior, including target partition sizes, dimension weights, and split strategies. These parameters allow the system to adapt the splitting logic to different workloads and storage configurations without changing the underlying algorithm structure. By parameterizing the complexity, the system achieves efficient non-overlapping partitions while maintaining manageable system complexity through configuration rather than code changes.
Data Source
AI summary
A system for clustering data into corresponding files comprises one or more processors and a memory. The one or more processors is/are configured to: 1) determine to cluster a set of data into a set of files; 2) determine a set of split points in a corresponding set of dimensions of the set of data to determine the set of files, wherein each file of the set of files has an approximate target size; and 3) store one or more items of the set of data into a corresponding file of the set of files based at least in part on the set of split points. The memory is coupled to the one or more processors and configured to provide the processor with instructions.