Distributed Graph Clustering With Atomic Transactions at Scale
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Solution Overview
Problem
Analyzing large graphs poses challenges due to high computing costs, time constraints, and data storage requirements, particularly in distributed computing environments with billions of nodes or trillions of edges.
Innovation Solution
Implementing distributed graph analysis using atomic transactions across multiple computing devices, utilizing remote direct memory access and ACID-compliant operations to ensure data consistency and prevent conflicts, with parallel processing and duplicate data structures to enhance efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed processing is used to analyze large graphs, then productivity is improved, but data consistency and conflict prevention become problematic
Solution Approach 1:
The patent divides the graph data into multiple partitions distributed across different computing devices. Each partition is independently processed by worker processes, enabling parallel computation while maintaining data consistency through controlled access boundaries. The graph data structure is segmented into manageable chunks that can be processed concurrently without interfering with each other.
Solution Approach 2:
The patent introduces an intermediary data structure layer that mediates between multiple worker processes and the underlying storage system. This intermediary layer provides atomic read-modify-write operations that ensure data consistency across distributed workers, acting as a buffer that reconciles concurrent access conflicts without requiring complex inter-worker communication.
2Speed
If parallel processing is implemented across multiple computing devices, then processing speed is improved, but latency increases due to coordination overhead
Solution Approach 1:
The patent implements self-service mechanisms where each worker process independently manages its own operations using atomic read-modify-write primitives. Workers do not require explicit coordination or communication with each other, eliminating coordination overhead and reducing latency. Each worker autonomously processes its assigned partition and commits changes independently to the distributed data structure.
3Quantity of substance
If large graphs are stored in distributed memory systems, then storage capacity is improved, but computing costs increase
Solution Approach 1:
The patent segments the large graph data into distributed partitions stored across multiple computing devices. This segmentation enables efficient localized processing where workers only access and process their assigned partitions, reducing the computational overhead of managing monolithic large-scale graphs and lowering overall computing costs through parallelized operations.
Data Source
AI summary
A method can include a plurality of iterations. An iteration can include performing, by a computing system comprising one or more computing devices, for each node of a plurality of nodes of a first graph, one or more atomic transactions. An atomic transaction can include retrieving, from one or more distributed data structures that are distributed over a plurality of computing devices, neighbor data indicative of one or more neighbor nodes connected to the node by one or more edges. An atomic transaction can include retrieving, from the one or more distributed data structures based on the neighbor data, cluster data associated with the one or more neighbor nodes. An atomic transaction can include writing, to the one or more distributed data structures based on the neighbor data and the cluster data, cluster assignment data assigning the node to one or more clusters of a plurality of clusters.