Monitoring method and device based on java process and computer equipment
A computer program and process technology, applied in computing, hardware monitoring, instruments, etc., can solve the problems of few monitoring indicators, large impact on system performance, and complicated use, and achieve the effects of high availability, easy deployment, and easy expansion.
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Embodiment 1
[0061] Build unhealthy detection standards for java processes based on different indicators, such as Figure 6 shown.
[0062](1) In order to fully detect the unhealthiness of the current process service, construct unhealthy detection standards for different dimensions.
[0063] (2) Refer to quantitative thresholds such as too frequent FULLGC, blocked threads, excessive heap memory usage, etc., and construct an indicator filter chain to filter the collected process indicators.
[0064] (3) The process that triggers the measurement threshold in the filter chain indicates that the current service process is unhealthy, and may cause service failure or unsatisfactory service effect, and it is necessary to intervene in the current service process.
[0065] (4) Send SMS and email alarms to remind the system administrator in time.
Embodiment 2
[0067] According to the information feedback of the current Java service process, the assignment and scheduling of tasks are carried out:
[0068] (1) Evaluate the service capability of the current service process according to the collected process monitoring index information.
[0069] (2) If the performance of the current service process is good, follow-up service tasks can be allocated. The allocation method is as follows: Figure 7 As shown, the present invention makes full use of existing started service processes.
Embodiment 3
[0071] Coordinate the monitoring of service processes of various types and architectures:
[0072] Most of the existing monitoring methods are built inside the process to automatically realize the indicator monitoring. It is bound to cause different architectures, different service types, and processes in different languages to occupy machine system resources respectively, but it is impossible to implement monitoring as a whole. For example, the deployment of microservice projects and underlying platform system processes such as spark, flume, and hive run on a certain machine at the same time. At this time, the monitoring of each technical architecture itself cannot perceive the running status of the service processes of other frameworks.
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