Method for quickly querying mass vehicle passing records within time-space range on basis of key value cloud storage

Abstract

The invention relates to a method for quickly querying mass vehicle passing records within a time-space range on the basis of key value cloud storage. The method comprises the steps of constructing a time and space segment point key value index table, constructing a time and space segment tree key value index table and constructing a time and space segment tree upgrading and deleting key value index table. By adopting the method, the quick vehicle passing record query efficiency based on a time-space single point is greatly improved by introducing the time and space segment point index table; the quick vehicle passing record query efficiency based on the time-space range is greatly improved by introducing the time and space segment tree index table; by introducing the time and space segment tree updating index table, the time and space segment index table can be dynamically reconstructed, and possibility is provided for supporting real-time querying of mass vehicle passing records; by introducing a key value cloud storage database to preserve the index tables and utilizing a Hadoop cloud platform to generate the index tables, the index construction and maintenance time is greatly shortened, and the query processing performance is improved.

Description

technical field [0001] The invention relates to the technical field of computer databases, in particular to the field of distributed indexing and query processing of cloud platform databases, and specifically refers to a method for quickly querying massive vehicle passing records within the time-space range based on key-value cloud storage. Background technique [0002] The fast query method of massive vehicle passing records in the time-space range based on key-value cloud storage mainly realizes the rapid query of massive vehicle passing structured records on the cloud platform based on the time-space range and time-space single point respectively. In recent years, with the in-depth research on Hadoop and Nosql key-value database technology of MapReduce and hdfs, the above two types of query problems are expected to be solved, and the quality of the indexing method directly affects the query performance of passing records. Nowadays, variant indexes such as B-Tree or R-Tree...

AI Technical Summary

Problems solved by technology

[0007] To sum up, most of the existing time-space range and quick query of time-space single-point vehicle passing records use the Hadoop platform to build B-Tree and R-Tree variant indexes, making full use of the distributed parallel processing of Hadoop and the efficient processing characteristics of key-value databases. However, it is not necessarily an appropriate technical solution to use variant indexes such as B-Tree or R-Tree based on time-space range and space-time single-point query for massive vehicle passing records.

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