Graph data storage and query method and device and computer readable storage medium

Abstract

The invention relates to a big data technology, and discloses a graph data storage and query method, which comprises the following steps of receiving a graph data set, creating a graph storage model through an adjacency list in a database, and taking the graph storage model as a storage mode of the graph data set; based on the storage mode, performing graph segmentation processing on the graph data set, and performing distributed storage on the graph data set after graph segmentation to obtain a target graph data set; and establishing a graph data index for the target graph data set, receivingquery content input by a user, querying the target graph data set by utilizing the graph data index according to the query content, and returning an obtained graph data query result to the user. Theinvention further provides a graph data storage and query device and a computer readable storage medium. According to the invention, efficient storage and query of graph data are realized.

Description

technical field [0001] The present invention relates to the technical field of big data, in particular to a graph data storage and query method, device and computer-readable storage medium. Background technique [0002] In recent years, with the rapid development of Internet applications and the popularization of social networks based on Web2.0 technology, the number of web pages on the Internet has increased exponentially. The scale of China’s webpages has reached 100 billion, and the annual growth rate of webpage data is over 80%. Global social networks such as Facebook, Twitter, and domestic Qzone, WeChat Moments, Sina Weibo, etc. are also developing very fast. According to Tencent’s data, the number of simultaneous online users of QQ has already exceeded 100 million, while the number of simultaneous online users of WeChat is even higher. The rapid development of social networks has led to a rapid increase in physical data in the real world along with the increase in virt...

AI Technical Summary

Problems solved by technology

Distributed cluster system is an important method to solve massive data processing, and it is also an important method for large-scale graph data processing. However, most current graph data algorithms need multiple iterations to converge, which also leads to the existence of performance calculation and expression capabilities. Insufficient, and a large number of intermediate results are generated during the graph data processing process, and messages and data need to be transmitted between different nodes in the distributed system. The graph data processing process is a typical IO-intensive calculation, so efficient graph data storage and queries have become an important part of large-scale graph data processing

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