A fast statistical method for mobile applications

Abstract

The present invention proposes a fast statistical method for mobile applications, including: the server receives the application data; the server splices the received application data and stores them in the redis buffer; obtains the reported data, and determines the reported data in combination with the information recorded in the database Whether the equipment is a newly added equipment, the acquired reported information and equipment type will be put into the file of the day according to the format; at the preset time, the new reported data of the day will be combined with the intermediate file of the one-dimensional calculation calculated on the previous day to generate a one-dimensional Aggregate result file, generate the one-dimensional calculation result file of the day at the same time; then perform two-dimensional total calculation, generate a two-dimensional total result file according to the new reported data of the day combined with the intermediate file of two-dimensional calculation calculated on the previous day, Generate the two-dimensional calculation result file of the current day. The invention can greatly reduce the time required for traversing historical data, thereby significantly reducing the time required for daily statistics. It is convenient for users to see daily data updates faster.

Description

technical field [0001] The invention relates to the technical field of mobile applications, in particular to a fast statistical method for mobile applications. Background technique [0002] The statistics of mobile applications play an important role in the operation of mobile apps, which is convenient for users to understand the installation volume, activity level, geographical location information of user groups, and device information. The traditional server-side statistical method is to log each reported original message, and perform statistics based on all message information every day. However, when there are many historical messages, it takes a long time to calculate and analyze; another solution is to process this kind of big data through distributed computing. Although this solution can speed up the processing speed, it does not reduce the overall calculation and analysis. , while the distributed computing of multiple machines itself increases the complexity of the...

AI Technical Summary

Problems solved by technology

However, when there are many historical messages, it takes a long time to calculate and analyze; another solution is to process this kind of big data through distributed computing. Although this solution can speed up the processing speed, it does not reduce the overall calculation and analysis. , while the distributed computing of multiple machines itself increases the complexity of the overall system

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