Problem Statement
What are best practices for monitoring processes in production environments? Discuss proactive monitoring and alerting strategies.
Explanation
Implement automated monitoring using tools like Prometheus, Nagios, or Datadog that continuously collect metrics (CPU, memory, disk, network) and alert when thresholds are exceeded. Set up alerts for critical metrics: CPU usage > 80%, memory usage > 90%, disk usage > 85%, load average > number of cores, and process count anomalies.
Monitor process-specific metrics: check that critical services are running with pidof or systemctl status, monitor process restart counts indicating instability, track resource consumption trends for capacity planning, and set up health checks for application endpoints. Use cron jobs or systemd timers for periodic checks if dedicated monitoring tools aren't available.
Log aggregation and analysis: centralize logs with ELK stack, Splunk, or CloudWatch, monitor error rates and patterns, set up alerts for critical log patterns like OutOfMemoryError or connection timeouts, and retain historical data for trend analysis and incident investigation.
Proactive strategies: establish baseline metrics for normal operation to detect anomalies, implement resource limits with ulimit or cgroup to prevent runaway processes from affecting the system, use process supervisors like systemd or supervisord to automatically restart failed processes, and regularly review and tune monitoring thresholds based on actual incidents to reduce false positives.
Document runbooks for common issues: high CPU resolution steps, memory leak troubleshooting, process stuck in D state handling, and zombie process cleanup. Automate remediation where safe: automatic service restart on failure, clearing temp directories, or releasing file handles. Balance automation with safety to prevent cascading failures from automated actions.
Practice Sets
This question appears in the following practice sets: