Data flow monitoring method and device for network service

Abstract

The invention discloses a data traffic monitoring method and a data traffic monitoring device for network service. A specific embodiment of the method comprises the steps of: acquiring data traffic related information, which includes request information of a client and business state parameters; monitoring whether an abnormal request exists in the network service based on the business state parameters; responding to the existence of the abnormal request in the network service, and identifying type of the abnormal request based on the request information; and conducting corresponding data traffic control according to the type of the abnormal request. The implementation method achieves the automatic monitoring of data traffic in network service and more targeted traffic control, effectively increases the fault restoration speed when business is damaged, and enhances the stability of the network service.

Description

Technical field [0001] This application relates to the field of computer network technology, in particular to the field of database flow control, and in particular to a method and device for monitoring data flow of network services. Background technique [0002] In network communication, the database is used as a storage medium for Internet data, and its capacity has certain limitations. When the client's access volume or access frequency increases abnormally, the request for data traffic in the process of processing the database access request by the server increases abnormally, resulting in excessive consumption of CPU and memory resources of the database, and the number of database connections is full. At this time, the server cannot process the client's data access request, which affects the business processing speed and success rate. Therefore, it is necessary to monitor and control abnormal requests with excessive traffic or sudden increases in traffic. [0003] Existing so...

AI Technical Summary

Problems solved by technology

On the one hand, the timeliness of manual investigation is poor, and the fault recovery time is longer. On the other hand, because the database cannot control the flow of abnormal requests with sudden increase in data traffic during the manual investigation process, the client keeps sending abnormal requests, which further lengthens the recovery time

In addition, the manual troubleshooting method lacks a comprehensive analysis of the current data, resulting in low accuracy of fault identification, and may misjudge the cause of business damage during the fault process

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