Graph database construction method supporting super-large-scale relational network

Abstract

The invention discloses a graph database construction method supporting a super-large scale relation network. According to the method, carrying out modeling through graph elements based on a key-valueformula; cutting the graph element into a plurality of different graph element storage fragments, and storing the graph element storage fragments to different servers; meanwhile, copying a pluralityof copies of each graph element storage fragment and respectively storing the copies to different servers; keeping data synchronization of the same graph element storage fragment in different serversthrough a Raft protocol. According to the method, the constructed graph database can support a larger graph scale, the graph database can be completely and horizontally expanded, the larger the numberof servers is, the larger the supported graph scale is, meanwhile, the fault resistance of the whole graph database is high, and services are not affected when a single server breaks down.

Description

technical field [0001] The invention relates to the technical field of big data processing, in particular to a method for constructing a graph database supporting a super-large-scale relational network. Background technique [0002] With the rapid development of big data and artificial intelligence, super-large-scale relationship networks are gradually being widely used in social recommendation, risk control, Internet of Things, blockchain, and security prevention and control. As the technical cornerstone of all these applications, large-scale distributed The storage and computing platform of the relational network has attracted more and more attention from academia and industry. This kind of relational network is usually based on the graph theory (Graph) in the data structure. There are two core elements that constitute the graph: a point (vertex or node, also called a node) and attributes on the point, and an edge (edge, Also known as associations) and attributes on edges...

AI Technical Summary

Problems solved by technology

This type of framework can provide better horizontal expansion capabilities, but its main problem is that the HBase storage module is not natively friendly to the graph structure, and usually loads too much irrelevant data during query, resulting in a large network throughput.

At the same time, because the HBase component depends on multiple Master-type nodes, the failure of such nodes will lead to the unavailability of system services

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