Snowflake inc. (20240134851). BUILD-SIDE SKEW HANDLING FOR HASH-PARTITIONING HASH JOINS simplified abstract
Contents
- 1 BUILD-SIDE SKEW HANDLING FOR HASH-PARTITIONING HASH JOINS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 BUILD-SIDE SKEW HANDLING FOR HASH-PARTITIONING HASH JOINS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Build-Side Skew Handling
- 1.13 Original Abstract Submitted
BUILD-SIDE SKEW HANDLING FOR HASH-PARTITIONING HASH JOINS
Organization Name
Inventor(s)
Xinzhu Cai of San Mateo CA (US)
Florian Andreas Funke of San Francisco CA (US)
BUILD-SIDE SKEW HANDLING FOR HASH-PARTITIONING HASH JOINS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240134851 titled 'BUILD-SIDE SKEW HANDLING FOR HASH-PARTITIONING HASH JOINS
Simplified Explanation
The patent application describes systems and methods for handling build-side skew in a join operation by partitioning build-side row sets using frequent hash values.
- Detect frequent hash values in a plurality of hash values sampled for a join operation.
- Partition build-side row sets using the frequent hash value to create partitioned build-side row sets.
- Distribute the partitioned build-side row sets to multiple hash-join-build instances executing on different servers.
Key Features and Innovation
- Detection of frequent hash values to optimize partitioning of build-side row sets.
- Efficient distribution of partitioned build-side row sets to multiple servers for parallel processing.
Potential Applications
This technology can be applied in database management systems, data warehouses, and big data processing platforms for optimizing join operations.
Problems Solved
- Addressing build-side skew in join operations to improve performance and efficiency.
- Enhancing parallel processing capabilities in distributed computing environments.
Benefits
- Improved performance and efficiency in handling join operations.
- Scalability in processing large datasets with build-side skew.
- Enhanced parallel processing capabilities for optimized data processing.
Commercial Applications
Optimizing join operations in database management systems for faster query processing and improved data analysis capabilities.
Prior Art
No prior art information available at the moment.
Frequently Updated Research
No frequently updated research available at the moment.
Questions about Build-Side Skew Handling
Question 1
How does the technology detect frequent hash values in a join operation?
The technology samples a plurality of hash values and identifies the ones that occur frequently, indicating potential skew in the data distribution.
Question 2
What is the significance of partitioning build-side row sets using frequent hash values?
Partitioning build-side row sets using frequent hash values helps distribute the data more evenly among multiple servers, reducing processing bottlenecks and improving overall performance.
Original Abstract Submitted
provided herein are systems and methods for handling build-side skew. for example, a method includes computing a plurality of hash values for a join operation. the join operation uses a corresponding plurality of row sets. the plurality of hash values are sampled to detect a frequent hash value. a build-side row set is partitioned using the frequent hash value to generate a partitioned build-side row set. the build-side row set is selected from the plurality of row sets. the partitioned build-side row set is distributed to a plurality of hash-join-build (hjb) instances executing at a corresponding plurality of servers.
