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With the continuous popularity of cloud computing and container technology, microservice architecture has also attracted widespread attention as a new software architecture approach. Compared with traditional monolithic application architecture, microservice architecture has better scalability and maintainability, and can better adapt to rapidly changing needs. However, as the number and complexity of microservices increases, problem troubleshooting becomes increasingly difficult. Therefore, how to track and diagnose services has become an important issue that must be solved in the microservice architecture.
In traditional monolithic application architecture, service tracking and diagnosis are relatively easy. Because all services run in the same application process, operations such as logging, exception capture, and performance indicator collection can be integrated in the same library, making it easier to troubleshoot problems. However, in the microservice architecture, each service runs independently. Each service has its own logs, exceptions, performance indicators and other information. This information is scattered among different services, and different services also need to communicate with each other. Record. Therefore, how to collect this information and manage and analyze it in a centralized manner has become an important issue that must be solved in the microservice architecture.
In the microservice architecture, service tracking and diagnosis mainly include three aspects: logging, exception capture and performance indicator monitoring.
In microservices, each service needs to record its own log information. These log information include the content of requests and responses, as well as error and exception information that occur during service operation. In order to better manage logs, it is usually necessary to collect and process the log information of each service in a unified manner.
In order to achieve this operation, a log component needs to be added to each service to collect local log information. At the same time, it is recommended to use a centralized log service, such as ELK (Elasticsearch Logstash Kibana) to process and manage these logs. By collecting the log information of each microservice into the same log service, operations such as log search, analysis, and monitoring can be easily performed.
In a microservice architecture, errors and exceptions may occur in each service. In order to better catch these exceptions, exception handling components need to be added to each service.
Generally speaking, exception information can be notified through email, message queue, etc. At the same time, it is recommended to use a centralized exception monitoring service, such as Sentry, to record all exception information in this service. In this way, abnormal troubleshooting and analysis can be better conducted through centralized management.
In the microservice architecture, the operating performance of each service needs to be monitored, such as service response time, request success rate, etc. Similar to logging and exception capture, monitoring of performance indicators also requires centralized processing.
Common performance indicator monitoring tools include Prometheus, Grafana, etc. By collecting the performance indicators of each microservice into the same monitoring service, performance analysis can be easily performed and problems can be dealt with in a timely manner.
Summary
To track and troubleshoot services in microservice architecture, a series of measures need to be taken, including logging, exception capture, and performance indicator monitoring. These measures require the centralization of information from each service for better management and analysis. By following these steps, each service in a microservices architecture can be effectively monitored and scheduled to better meet user and business needs.
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