What parameters and tools should be checked to determine the cause of a microservice failure?

To determine the causes of a microservice failure, it is important to check the following parameters and use appropriate tools:

• Logs: examine the microservice logs (e.g., via ELK Stack, Fluentd, or simply log files) for errors, exceptions, and warnings.
• Performance metrics: CPU, memory, disk, and network usage (Prometheus, Grafana).
• Dependency status: check the availability of databases, external APIs, and other services that the microservice depends on.
• Request tracing: using distributed tracing (Jaeger, Zipkin) helps understand where exactly the failure occurs in the call chain.
• Configuration and environment: ensure environment variables and configuration files are correct.
• Container and orchestration monitoring: if the microservice runs in a container (Docker, Kubernetes), check the status of containers and orchestration events.

Example of log checking in Python:

import logging

logging.basicConfig(filename='service.log', level=logging.ERROR)

try:
    # microservice code
    pass
except Exception as e:
    logging.error(f'Error: {e}')

Using these tools and parameters will help quickly localize and resolve the cause of the failure.