Distributed Join Filters for Dynamic Data Processing
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Solution Overview
Problem
In dynamic distributed computing systems, existing methods for performing join operations across multiple interconnected nodes are inefficient due to lack of early data filtering, leading to excessive data transfer and poor performance, especially in scenarios with loosely coupled and dynamically changing network topologies.
Innovation Solution
The implementation of partial join filters, where each node builds a partial filter locally and transmits it to other nodes, allowing for asynchronous combination and application of these filters to reduce data flow and enhance processing efficiency without requiring synchronized operations between data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If distributed join operations are performed without early filtering, then all nodes can participate in the join operation, but excessive data transfer occurs and performance deteriorates
Solution Approach 1:
The patent applies preliminary action by having source nodes generate and transmit partial join filters to target nodes before the actual join operation executes. This allows target nodes to filter their local data early in the process, reducing the volume of data that needs to be transferred and processed during the join operation, thereby improving query performance while maintaining distributed node participation
2Ease of operation
If centralized filter generation is used, then filter application is simplified, but network synchronization requirements increase and system complexity grows
Solution Approach 1:
The patent applies segmentation by dividing the filter generation process into distributed partial filters created by individual source nodes, rather than requiring a single centralized filter generation point. Each source node independently generates partial filters that are transmitted to target nodes, eliminating the need for complex network synchronization while maintaining ease of filter application through independent parallel processing
Solution Approach 2:
The patent applies self-service by enabling each source node to autonomously generate its own partial join filters without requiring coordination or synchronization with other nodes. This self-service approach simplifies the overall system by allowing nodes to independently perform filter generation and transmission, reducing system complexity while maintaining operational simplicity
3Reliability
If all source data is transferred to target nodes, then complete join results can be achieved, but data transfer volume increases excessively
Solution Approach 1:
The patent applies the taking out principle by extracting only the essential filtering information (partial join filters) from source nodes and transmitting it to target nodes, rather than transferring all source data. This extraction approach allows target nodes to retain and filter their own local data, achieving complete and accurate join results while dramatically reducing data transfer volume and associated energy consumption
Data Source
AI summary
A method includes: a first node and a second node in a distributed computing system each creating a respective partial join filter; the first node and the second node each transmitting its respective partial join filter to a third node in the distributed computing system; the third node creating a final join filter by combining the respective partial join filters of the first node and the second node; the third node retrieving target data from a data source of the third node by applying the final join filter to the data source of the third node; and the third node transmitting the retrieved target data to a controlling node in the distributed computing system.