Snowflake inc. (20240354315). MICRO-PARTITION CLUSTERING BASED ON EXPRESSION PROPERTY METADATA simplified abstract
Contents
MICRO-PARTITION CLUSTERING BASED ON EXPRESSION PROPERTY METADATA
Organization Name
Inventor(s)
Varun Ganesh of San Carlos CA (US)
Alvin E. Jou of Woodinville WA (US)
Donghe Kang of Columbus OH (US)
Ryan Michael Thomas Shelly of San Francisco CA (US)
Jiaqi Yan of Menlo Park CA (US)
MICRO-PARTITION CLUSTERING BASED ON EXPRESSION PROPERTY METADATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240354315 titled 'MICRO-PARTITION CLUSTERING BASED ON EXPRESSION PROPERTY METADATA
The method described in the abstract involves selecting micro-partitions for a clustering operation by storing table data in multiple micro-partitions, associating subsets of these partitions with expression property (ep) files, determining sub-ranges of the table data based on the ep data regions, selecting a subset of ep files for clustering, and performing the clustering operation on the associated micro-partitions.
- Storing table data in multiple micro-partitions of a storage device
- Associating subsets of micro-partitions with expression property (ep) files
- Determining sub-ranges of table data based on ep data regions
- Selecting a subset of ep files for clustering operation
- Performing clustering operation on micro-partitions associated with the selected ep files
Potential Applications: - Data clustering in databases - Data analysis and pattern recognition - Improving query performance in large datasets
Problems Solved: - Efficient data organization for clustering operations - Enhanced data processing and analysis capabilities - Optimized storage and retrieval of information
Benefits: - Faster data processing and analysis - Improved database performance - Enhanced scalability for large datasets
Commercial Applications: Title: "Enhanced Data Clustering Method for Improved Database Performance" This technology can be utilized in industries such as e-commerce, finance, healthcare, and research where large datasets need to be analyzed and clustered for various purposes. It can improve the efficiency of data processing, leading to better decision-making and insights.
Prior Art: Readers can explore prior research on data clustering methods, database optimization techniques, and storage organization for large datasets to understand the evolution of this technology.
Frequently Updated Research: Researchers are constantly exploring new algorithms and methodologies for data clustering and database optimization. Stay updated on the latest advancements in these fields to enhance the performance of clustering operations.
Questions about Data Clustering: 1. How does this method improve the efficiency of data clustering operations? 2. What are the key advantages of using micro-partitions and expression property files in the clustering process?
Original Abstract Submitted
a method for selecting micro-partitions for a clustering operation includes: storing table data in a plurality of micro-partitions of a storage device, wherein each of the plurality of micro-partitions comprises a portion of the table data, wherein subsets of the plurality of micro-partitions are associated with a respective one of a plurality of expression property (ep) files, and wherein each of the plurality of ep files comprises an ep data region that represents the portions of the table data of the subset of the plurality of micro-partitions associated with the ep file; determining sub-ranges of the table data based on the ep data regions of the plurality of ep files; selecting a subset of the plurality of ep files for a clustering operation based on the sub-ranges of the table data; and performing the clustering operation on the micro-partitions associated with the subset of the ep files.
