Snowflake inc. (20240232189). 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 Join Operation Optimization
- 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 20240232189 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 a build-side row set using frequent hash values.
- Computing multiple hash values for a join operation
- Sampling hash values to detect frequent ones
- Partitioning build-side row set using frequent hash value
- Distributing partitioned row set to multiple servers for processing
Key Features and Innovation
- Detection of frequent hash values to optimize join operations - Efficient partitioning of build-side row sets for improved performance - Distribution of partitioned 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 where join operations are common.
Problems Solved
- Addressing build-side skew in join operations - Optimizing performance of hash-join-build instances - Enhancing efficiency of distributed computing environments
Benefits
- Improved performance in handling skewed data sets - Enhanced scalability in processing large volumes of data - Optimized resource utilization in distributed computing environments
Commercial Applications
Title: "Optimizing Join Operations in Distributed Computing Environments" This technology can be utilized in cloud computing platforms, data analytics services, and enterprise data processing systems to improve query performance and scalability.
Prior Art
Readers can explore prior research on hash join algorithms, distributed computing, and query optimization techniques to understand the background of this technology.
Frequently Updated Research
Researchers are constantly exploring new methods for optimizing join operations in distributed computing environments, including advancements in parallel processing and data partitioning strategies.
Questions about Join Operation Optimization
How does this technology improve the efficiency of join operations in distributed computing environments?
This technology improves efficiency by detecting frequent hash values and partitioning build-side row sets for optimized processing.
What are the potential applications of this technology beyond database management systems?
This technology can be applied in various fields such as data analytics, big data processing, and cloud computing for enhanced performance and scalability.
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.
