Efficient mass data indexing method in cloud computing

Abstract

The invention discloses an efficient mass data indexing method in cloud computing. According to the method, the node splitting history is recorded by use of a log on the basis of a distributed B-tree and the distributed B-tree is efficiently and simultaneously accessed based on the node splitting history, and therefore, the accessing and indexing efficiency of mass distributed data in the cloud computing environment can be effectively improved. The efficient mass data indexing method in the cloud computing is superior to an affair method because distributed locks need to be added to all nodes in the path from the root to a leaf node to be accessed in the affair method, and the efficient mass data indexing method only needs to lock the leaf node to be accessed; the efficient mass data indexing method is superior to a link method because the number of nodes to be traversed in the efficient mass data indexing method is smaller than that of the link method.

Description

technical field [0001] The invention relates to the field of cloud computing, in particular to an efficient indexing method for massive data in cloud computing. Background technique [0002] At present, a large number of Internet applications based on massive data and providing various information services have emerged in the cloud computing environment. [0003] The data of these applications is characterized by massive volume and rapid growth. The system distributes all data to multiple storage nodes by hashing the key of each data, so as to realize the scalable storage of rapidly growing massive data. The hash method has higher efficiency for query using keywords, but it cannot improve the efficiency of query using non-keywords, and does not support range queries. For users, in addition to using keywords to query data, they also like to use other attributes to query data or perform range queries. For example, in an online video system (such as Youtube), each video cont...

AI Technical Summary

Problems solved by technology

[0011] The technical problem to be solved by the present invention is: in view of the massive and distributed characteristics of data in the cloud computing environment, and the problem of low access rate in the existing distributed B-tree index method, the present invention proposes a cloud computing environment The high-efficiency indexing method for massive data uses logs to record the split history of nodes on the basis of distributed B-trees, and accesses distributed B-trees efficiently and concurrently based on the split history of nodes, which effectively improves the massive and distributed data in the cloud computing environment. Data Access and Indexing Efficiency

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