A Method for Big Data Query Based on Distributed Relation-Object Mapping Processing

A query method and mapping processing technology, applied in the network field, to achieve the effect of ensuring data integrity and query efficiency
CN103678609BActive Publication Date: 2017-05-17COMP NETWORK INFORMATION CENT CHINESE ACADEMY OF SCI
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
COMP NETWORK INFORMATION CENT CHINESE ACADEMY OF SCI
Publication Date
2017-05-17

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Abstract

The invention discloses a large data inquiring method based on distribution relation-object mapping processing. The method includes the following steps that firstly, m data processing nodes are selected, a Hibernate Session object is constructed for each data processing node, a distributed Hibernate framework is obtained and a Session resource queue is generated; secondly, a configuration file is set, a storage strategy and a routing strategy corresponding to a list of each type are set, an S list with a data volume is backed up at each data processing node, and an L list with a large data volume is stored to the m data processing nodes in a blocking mode; thirdly, an input inquiring request is analyzed and a corresponding processor is selected according to the type of the inquiring request; fourthly, the selected processor selects the corresponding node from the resource queue to process the inquiring request according to the inquiring request, the corresponding routing strategy, and a processing result is protocoled. The large data inquiring method based on distribution relation-object mapping processing can obviously improve the rate of inquiring large data lists.
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Description

technical field

[0001] The invention relates to a large data storage and query method, in particular to a method for processing large data storage and query using a distributed Hibernate architecture, and belongs to the network technology field.

[0002] technical background

[0003] Since the beginning of the new century, with the development and popularization of the network, the amount of data generated and processed by applications has increased. Take the data processed by Google every day as an example. In 2004, it needed to process 100TB of data per day (Jeffrey Deanand Sanjay Ghemawat .MapReduce:Simplified data processing on large clusters.InProceedings of the6 th Symposium on Operating System Design and Implementation (OSDI2004), 137-150, 2004), and its daily data volume reached 20PB by 2008 (Jeffrey Dean and Sanjay Ghemawat. MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51 (1): 107-113, 2008), it can be seen that with the explo...

Claims

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