Data query method and device of time interval and index construction method and device of time interval

Abstract

The embodiment of the application discloses a data query method and device of a time interval and an index construction method and device of a time interval. The data query method includes: acquiring a to-be-queried time interval; using the to-be-queried time interval to obtain a first result set of index interval objects in intersection and containing association relationships with the to-be-queried time interval by querying in a first sorted index constructed on the basis of key value pairs of the index interval objects; using the to-be-queried time interval to obtain a second result set of index interval objects in an overlaying association relationship with the to-be-queried time interval by querying in a second segment tree index constructed on the basis of key value pairs of the index interval objects; and combining the first result set of the index interval objects and the second result set of the index interval objects obtained by querying, and then using the combined index interval objects as a query result of the to-be-queried time interval. According to the embodiment of the application, a reverse indexing manner and a segment-tree-type indexing manner are combined, retrieval requirements of the high expansibility and the high concurrency of big data based on a cloud platform can be met, and the retrieval efficiency can also be greatly improved.

Description

technical field [0001] The present application relates to the technical field of data retrieval based on cloud storage, in particular to a time interval data query method and device and an index construction method and device. Background technique [0002] With the advent of the era of big data, people not only began to explore new IT technologies and architectures, but also paid more attention to the mining and utilization of massive data. The rapid development of various industries and fields has led to more and more accumulated data, and the growth rate of data volume is getting faster and faster. According to statistics from the International Data Corporation (IDC), the total amount of data created and copied globally in 2015 reached nearly 8ZB, more than seven times that of 2010 and four times that of 2011. This growth trend is particularly evident in social media and web page history archives. The total amount of data is growing exponentially. How to quickly and effic...

AI Technical Summary

Problems solved by technology

Different data sets have different interval density and distribution. The performance difference of the above query algorithm in data-intensive and scattered areas can reach about 10 times, and the query performance is unstable.

[0004] At present, the indexing technologies for time interval queries mainly use local disks as storage media, such as Time Index technology, Multi-Version B-Tree technology, etc., but the time interval retrieval technology in the prior art The data volume is small, the scalability is low, and the query throughput is very limited, which cannot be applied to the current big data environment and high data concurrency scenarios

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