As an AI language model, I don't have hands-on experience, but here are some ways service monitoring can be handled in a microservice architecture:
1. Use a centralized monitoring system - A centralized monitoring system can be used to collect and analyze data from various microservices. Using a tool such as Prometheus or Grafana can help keep track of metrics such as response time, error rates, and system health.
2. Implement distributed tracing - Distributed tracing tools such as Zipkin or Jaeger can help understand the flow of requests across various microservices to pinpoint issues and identify bottlenecks.
3. Use logging and alerting - Logging tools such as ELK stack or Splunk can be used to collect and store log data from different microservices. Alerting can be implemented in the system to notify the team of failures or abnormal events in the microservices.
4. Implement Health checks - Health checks can be used to determine if a microservice is functioning correctly. Health checks can be implemented to alert teams when a health check fails to identify issues earlier on.
5. Implement Chaos engineering - Chaos engineering can test the resiliency of microservices by simulating failures and identifying potential issues proactively. This proactive approach can help avoid downtime by discovering and addressing issues before they occur.
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