Index segmenting equalization based big data cloud search platform and method thereof

Abstract

The invention provides an index segmenting equalization based big data cloud search platform and a method thereof, which can solve the problem that in an Apache Lucene engine based big data cloud search platform, it is difficult to reasonably and efficiently allocate index segments among nodes of a cluster. The big data cloud search platform and method can perform index segment allocation based on the load equalization principle, can allocate index segments with high content relevancy to different nodes to be supported according to the content relevancy of the allocated index segments. The big data cloud search platform and method can achieve equalization allocation of computing loads due to the fact the index segments are queried and invoked, among all the nodes, can void the problem of delay due to overload of a part of nodes in the cluster, and can suppress the phenomenon that a part of nodes are too idle.

Description

technical field

[0001] The invention relates to a big data information search technology implemented by applying a cloud computing platform, in particular to a big data cloud search platform and method based on index slice balance. Background technique

[0002] In the big data era of network information explosion, it is a common demand to build efficient, easy-to-use, and accurate search functions and platforms. Not only professional search sites such as Google and Baidu need to be continuously optimized and upgraded, but even in the services of ordinary portals, forums, social networking or business websites, it is also hoped to embed powerful, resource-saving and easy-to-implement intranet and network-wide Search tools for the convenience of target customers.

[0003] Apache Lucene is an open source, highly scalable search engine architecture, focusing on the indexing and searching of network information, and can build search functions for various websites and application...

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