Intelligent Push-Down Joins for Database Query Processing
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Solution Overview
Problem
Existing data source join technologies face inefficiencies in processing queries that require data from multiple sources, as they often involve moving and joining data in separate locations, leading to slower processing speeds due to the need to transfer larger datasets.
Innovation Solution
Implementing intelligent push-down joins that dynamically determine the direction of data movement based on the query requirements, moving smaller datasets to the location with the larger dataset, and joining data within that source to optimize processing speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is joined in separate locations using traditional methods, then data from multiple sources can be processed, but processing time increases due to moving larger datasets
Solution Approach 1:
Instead of moving data from multiple sources to a central location for joining (traditional approach), the patent inverts the approach by pushing the join operation down to the data sources themselves. The system determines which data source should perform the join operation and pushes the appropriate join logic to that source, eliminating the need to move large datasets across the network.
Solution Approach 2:
The patent segments the join operation into separate push-down join operations at different data sources. Rather than performing a single centralized join, the system divides the join logic and executes it at the appropriate data source locations, allowing parallel processing and reducing data movement requirements.
2Adaptability or versatility
If data is moved between separate locations for joining, then queries requiring multiple data sources can be executed, but data movement overhead increases processing complexity
Solution Approach 1:
The system dynamically determines the optimal location for push-down join operations based on query characteristics, data distribution, and data source capabilities. The join strategy is not fixed but adapts to different query scenarios, selecting whether to push joins to source systems or perform them centrally based on real-time conditions.
Solution Approach 2:
The system performs preliminary analysis of the query and data sources to determine the optimal join strategy before execution. It pre-determines which data source should perform the join operation and pushes the appropriate join logic in advance, avoiding runtime data movement and reducing processing complexity.
3Quantity of substance
If larger datasets are moved for joining in traditional methods, then complete data can be processed, but network bandwidth consumption increases
Solution Approach 1:
The patent extracts only the necessary join logic and minimal required data from data sources, rather than moving complete datasets. It pushes down specific join operations to the source systems, extracting only the joined results rather than transferring all source data, thereby significantly reducing network bandwidth consumption.
Data Source
AI summary
Database report generation technology for handling a single logical query that requires data that is physically located in two different sources. The system uses intelligent push-down joins, which move data from one source to the other and join data within that source. The system dynamically determines which direction to move data depending on how the data is used in a query.