Traffic sampling method, computer equipment and computer readable storage medium

Abstract

The invention relates to a traffic sampling method, computer equipment and a computer readable storage medium, and the traffic sampling method comprises the steps: receiving an access request; obtaining identifier information corresponding to the access request; determining a hash value of the identifier information by using a hash method with a uniform hash characteristic; determining a proportion of the hash value in a hash integer value domain range to obtain a proportion value corresponding to the hash value; comparing the proportion value and a sampling rate; if the proportion value is less than or equal to the sampling rate, accepting the access request; and if the proportion value is greater than the sampling rate, refusing the access request. According to the method, rapid trafficsampling is realized, and a large quantity of storage space and computing resources are not needed.

Description

technical field [0001] The present application relates to the technical field of Application Performance Management (APM for short), and in particular to a traffic sampling method, computer equipment, and a computer-readable storage medium. Background technique [0002] When the application server receives a large number of requests from the client, it often performs flow control or limits the number of devices on the client's requests according to the specified sampling rate. Assuming that the specified sampling rate S (0<=S<=1), the total request volume within a period of time is N, after filtering by the sampling algorithm as required, the system accepts the request volume (sampling volume) N s =N*S. [0003] In order to achieve the purpose of flow control, the proportion of flow is filtered by configuring the sampling rate. The sampling rate is represented by S (0<=S<=1). The results of sampling and filtering are divided into "accept" and "reject". "Accept...

AI Technical Summary

Problems solved by technology

[0009] The above improved solution can achieve the transactionality and idempotence in the above requirements, but due to the need to maintain a complete user list in the cache, a large storage space is required, especially for mobile App applications with a large user base, a single App The number of users is tens of millions or even hundreds of millions, and the concurrent access is also very large. Under high concurrency, it is necessary to realize fast retrieval and sampling filtering, which consumes a lot of storage space and computing resources

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