A hybrid distributed graph data storage and computing method
By adopting a hybrid distributed graph data storage and computation method, the problems of insufficient graph storage capacity and low query performance under the domestically developed and controllable environment are solved, realizing high-performance graph data storage and computation, and improving the storage and query performance of graph databases.
CN117112692BActive Publication Date: 2025-10-24CHINESE PEOPLES LIBERATION ARMY UNIT 91977
Patent Information
- Application Number
- CN202310889520.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-07-19
- Publication Date
- 2025-10-24
- Estimated Expiration
- 2043-07-19
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Figure CN117112692B_ABST
Abstract
The application discloses a kind of mixed type distributed graph data storage and computing method, comprising: container arrangement and scheduling are carried out, shield software and hardware infrastructure to the influence of application running;Transaction lock and high-performance graph storage structure fusion are carried out;Through scheduling strategy, the resource conflict of transaction analysis task is simultaneously solved when running, meet graph database transaction analysis mixed processing function and performance;Through coding optimization, graph computing fault tolerance, adaptive model selection optimization improves parallel processing model, so that the mainstream graph algorithm is accelerated;Through openCypher development and decomposition into multiple parallel tasks, execution plan optimization, distributed parallel execution optimization, depth query optimization;High-performance cross-graph model query, cross-cluster query is carried out;Mixed transaction analysis integrated graph database prototype system is established and test verification is completed. The storage capacity of the scale graph data of 100 billion nodes and 100 billion edges is realized, which provides underlying storage support for high-performance graph query, transaction processing and high-throughput graph data loading.
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Citation Information
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