Application service index monitoring method and device, computer equipment and storage medium

Abstract

The invention relates to an application service index monitoring method and device, computer equipment and a storage medium. The method comprises the following steps: collecting time sequence index data of each application service in a plurality of application services; performing aggregation processing on the time sequence index data according to a corresponding preset aggregation rule through a Flink calculation engine to obtain aggregated index data; and judging whether the aggregation index data triggers an alarm rule in an alarm rule base, and if so, generating alarm information and outputting the alarm information. The method is adopted to replace a full-link monitoring framework which uses the call chain data to calculate the indexes, server resources of an old framework which uses the call chain data to calculate the aggregation indexes can be greatly reduced, the occupied computing resources are less, and more server resources are released; and meanwhile, the performance of the application service can be monitored more accurately and efficiently in real time.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to a method, device, computer equipment and storage medium for monitoring application service indicators. Background technique [0002] With the popularity of microservice architecture, services are split according to different dimensions, and a request often needs to involve multiple services. Internet applications are built on different sets of software modules. These software modules may be developed by different teams, may be implemented using different programming languages, and may be deployed on thousands of servers, spanning multiple different data center. Therefore, some tools that can help understand system behavior and analyze performance problems are needed, so that when a failure occurs, the problem can be quickly located and resolved. The full link monitoring component was created under such a problem background. [0003] The call chain is the core of th...

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Problems solved by technology

However, there are also many shortcomings, for example: due to the large number of business visits, it is impossible to place the information every time it is called, and the sampling rate of the placement needs to be set, so the calculation of business indicators from the call chain information is not completely accurate; due to the use of call chain data The amount of calculation for calculating indicators is very large, which leads to the occupation of too many server resources and computing resources; if the calculation is not timely during traffic peaks, it is easy to cause delays in indicator data

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