Running Docker containers in production brings agility and consistency to software deployments. However, it also introduces complexity in observability — especially around monitoring, logging, and debugging distributed containers.

Without proper monitoring and logging, even minor issues can go unnoticed, potentially affecting uptime, performance, and user experience.

In this post, we’ll explore best practices for monitoring and logging Docker in production, with practical tools and techniques to achieve operational excellence.

Why Monitoring and Logging Matter

  • Monitoring provides real-time insights into container health, resource usage, and uptime
  • Logging captures runtime information and application-level events
  • Combined, they enable:
    • Faster root cause analysis
    • Effective alerting
    • Security auditing
    • Better performance optimization

Monitoring Docker: Best Practices

1. Use Prometheus and Grafana for Metrics
  • Prometheus collects container metrics via cAdvisor, node-exporter, or Docker Engine API
  • Grafana visualizes these metrics with dashboards and alerts

Sample Prometheus job for Docker:

- job_name: 'docker'
  static_configs:
  - targets: ['localhost:9323']
    ```

Enable Docker metrics endpoint:

```bash
dockerd --metrics-addr 127.0.0.1:9323 --experimental
2. Monitor Container-Level Metrics

Track:

  • CPU and memory usage
  • Disk I/O
  • Network traffic
  • Container uptime and restarts

Prometheus queries:

container_memory_usage_bytes
rate(container_cpu_usage_seconds_total[1m])
container_network_receive_bytes_total
3. Use Health Checks

Define health checks in your Dockerfile or Compose:

healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:8080/health"]
  interval: 30s
timeout: 10s
retries: 3

This helps orchestrators (like Docker Swarm or Kubernetes) restart failing containers proactively.

4. Centralized Monitoring with Tools Like Datadog, New Relic

For a plug-and-play solution:

  • Use agents to collect Docker metrics
  • Auto-discover containers and services
  • Get alerting and anomaly detection out-of-the-box

Logging Docker: Best Practices

1. Use a Centralized Logging Solution

Popular logging stacks:

  • ELK (Elasticsearch + Logstash + Kibana)
  • EFK (Fluentd instead of Logstash)
  • Loki + Grafana
  • Graylog

Benefits:

  • Centralized search
  • Log retention policies
  • Security and audit trails
2. Avoid docker logs in Production

While docker logs is useful for debugging, it lacks persistence and filtering. Redirect logs to a central backend instead.

3. Use JSON Logging Drivers

Use structured logging with the json-file driver:

docker run --log-driver=json-file my-app

Recommended for compatibility with log collectors like Fluentd and Logstash.

4. Tag and Enrich Logs

Add metadata:

  • Container name
  • Service name
  • Environment (prod, staging)
  • Timestamp and trace ID

This improves filtering, correlation, and troubleshooting.

Example with Fluent Bit:

[FILTER]
Name    record_modifier
Match   *
Record  env production
Record  app web-api
5. Avoid Logging to STDOUT Only

While Docker captures STDOUT/STDERR by default, use proper logging libraries (e.g., Winston, Log4j, Bunyan) to control formats and error levels.

Combine STDOUT logs with application-specific logs (database errors, security alerts) for full coverage.

Additional Tips

  • Set log rotation to avoid filling disk: 
    --log-opt max-size=10m --log-opt max-file=3
  • Monitor log pipeline health (dropped logs, memory pressure)
  • Use distributed tracing tools like Jaeger or OpenTelemetry to correlate logs and metrics
  • Use labels and annotations for traceability in orchestrated environments

Conclusion

Proper monitoring and logging are essential to running Docker in production reliably and securely. By leveraging tools like Prometheus, Grafana, Fluentd, and ELK, teams can gain full observability into container behavior and application performance.

A well-monitored and logged container environment helps teams debug faster, scale smarter, and deliver resilient services in complex, cloud-native ecosystems.