Big data processing method and device, equipment and medium

Abstract

The invention relates to the field of big data, and provides a big data processing method and device, equipment and a medium. The method comprises the steps of receiving an epidemic situation big datacluster containing multiple instruction data at a current moment in real time; dividing all the instruction data into mobile query data, webpage query data and update data; synchronously executing amobile query operation, a webpage query operation and a data updating operation; recording the same mobile query statement and webpage query statement at the current moment as a common query statement; judging whether the mobile query result is consistent with the webpage query result or not through the shared network management center; if not, performing proofreading and updating through the shared network management center according to the updating time; sending a mobile query result to the first client through the shared network management center, and sending a webpage query result to the second client at the same time; and confirming that epidemic situation big data cluster processing is completed. According to the invention, automatic proofreading is realized, the database performanceis optimized, and the accuracy of a query result is improved.

Description

technical field [0001] The present invention relates to the data processing field of big data, in particular to a big data processing method, device, equipment and medium. Background technique [0002] During the epidemic, users all over the country need to pay attention to the epidemic data all the time. Users query or update the current epidemic data in real time through various methods. The query methods include querying through the application software in the mobile phone, and also by visiting the web page, and Medical staff across the country also need to update local epidemic data in real time, so there will be a large number of query data and update data operations for epidemic data at the same time. MYSQL database is very difficult to face large-scale query data and update data operations , which greatly reduces the performance of the MYSQL database, which often leads to delays in querying data or updating data, and even causes downtime, resulting in epidemic data no...

AI Technical Summary

Problems solved by technology

[0002] During the epidemic, users all over the country need to pay attention to the epidemic data all the time. Users query or update the current epidemic data in real time through various methods. The query methods include querying through the application software in the mobile phone, and also by visiting the web page, and Medical staff across the country also need to update local epidemic data in real time, so there will be a large number of query data and update data operations for epidemic data at the same time. MYSQL database is very difficult to face large-scale query data and update data operations , which greatly reduces the performance of the MYSQL database, which often leads to delays in querying data or updating data, and even causes downtime, resulting in epidemic data not being provided to users in time, and users will complain again and again, seriously satisfying the user experience degree difference

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