Data query method and device

Abstract

The invention discloses a data query method, the method is applied to a data query system, the data query system comprises a plurality of different types of database management systems, and the methodcomprises the following steps: obtaining a data query request; determining the number of dimensions of query condition fields corresponding to the data query request; determining a target database management system according to the number of dimensions of the query condition field; and determining a data query result corresponding to the data query request based on the target database managementsystem. According to the method, a scheme that a plurality of different types of database management systems are matched for use can be utilized, so that the pain point that Kylin cannot normally process and analyze the data query request under the condition that the dimensions of the data query request are too many can be improved, and the convenience of querying and reading the data in the database based on the data query request can be improved; the development efficiency of the database is improved, and the user experience of data query is improved.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a data query method and device. Background technique [0002] Kylin is one of the widely used OLAP (Online Analytical Processing) tools. It has more than 10 billion data query and analysis capabilities, can provide standard SQL interface, and can conduct interactive real-time data analysis. Its performance Compared with ordinary data warehouses (such as Hive), the performance is higher. At present, many companies have chosen Kylin for OLAP tools. However, Kylin, as a leader in OLAP tools, has certain limitations in the number of dimensions of data processed (for example, the current upper limit of the dimensions of data processed by Kylin is 64), that is, the number of dimensions supported by Kylin is very limited. Big bottleneck. Usually, the amount of data to be statistically analyzed is not large, but the data has many dimensions (for example, more than one hundred ...

AI Technical Summary

Problems solved by technology

However, this way of using Kylin to split dimensions may need to split dozens or even hundreds of Cubes (defined internally by Kylin to represent data cubes) for ultra-multidimensional query analysis requests. A data structure), and at the same time, it must be able to accurately locate the specified cube when performing data query, otherwise the query data will be inaccurate, resulting in untrustworthy query results

Therefore, there is an urgent need for an effective solution to the situation that there are too many dimensions in the query analysis request, which makes the analysis of big data analysis software such as Kylin too complicated or impossible to analyze.

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