Detail data query method and device

Abstract

The invention discloses a detail data query method, which comprises the following steps of: in response to an aggregation query instruction, sending an aggregation query request to an analysis type database management system; obtaining an aggregation query result corresponding to the aggregation query request, and determining the aggregation query result by the analysis type database management system according to the aggregation query request; in response to a detail query instruction for the aggregation query result, sending a detail query request to an ad hoc query engine; and obtaining detail data corresponding to the detail query request, the detail data being determined by an ad hoc query engine according to the detail query request. According to the method provided by the invention,the efficiency of data analysis and detail data query can be improved, and the query cost of the detail data is reduced, so that the user experience of detail data query is improved.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a query method and device for detailed data. Background technique [0002] Kylin is one of the widely used OLAP tools. It has the ability to query and analyze tens of billions of data, and it can provide a standard SQL interface for interactive real-time data analysis. Therefore, Kylin’s performance is better than that of ordinary data warehouses (such as Hive ) is much higher. However, Kylin lacks a key detail query feature in the application. Therefore, when using Kylin, users have to take various methods to enable Kylin to support detailed query. [0003] At present, the commonly used Kylin-based detail query scheme is to use an index to store all the detailed data under the dimension. When the user chooses to view the details under the specified dimension value, then use Kylin's common index query method to query the detailed data. . Specifically, since Kylin itsel...

AI Technical Summary

Problems solved by technology

[0004] However, since the detail field is generally a super-high cardinality, the calculation of Bitmap will be relatively large

Therefore, the use of the above-mentioned existing technical solutions will cause detailed data query to consume more bandwidth and computing resources, and the data expansion rate will be higher. That is to say, the above-mentioned existing technical solutions will result in longer Kylin data construction time, Details query is slower and the proportion of details data storage is higher

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