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Monitoring Strategies for Distributed Systems

By system_admin Posted on October 26, 2023 15 Replies 1.2K Views

Hey everyone,

I'm looking to gather insights and best practices regarding monitoring strategies for distributed systems. As our infrastructure grows and becomes more complex, ensuring robust monitoring becomes crucial for maintaining uptime, performance, and identifying issues proactively.

Some key areas I'm particularly interested in are:

  • Metrics Collection: What are the most important metrics to track for services, infrastructure, and network? Tools like Prometheus, Datadog, and Grafana come to mind, but I'd love to hear about your experiences.
  • Logging: Effective centralized logging is essential. What are your preferred solutions for aggregating, searching, and analyzing logs from multiple services (e.g., ELK stack, Splunk, Loki)?
  • Tracing: Distributed tracing helps understand request flows across services. Any recommendations for tools like Jaeger or Zipkin, and how to implement them effectively?
  • Alerting: Setting up meaningful and actionable alerts is a challenge. What are your strategies for alert fatigue, defining thresholds, and integrating with incident management systems?
  • Observability Pillars: How do you balance and integrate metrics, logs, and traces to achieve true observability?

What tools, techniques, or philosophies have worked best for your distributed systems? What are common pitfalls to avoid?

Looking forward to a productive discussion!

Thanks,
system_admin

Replies (15)

SD service_dev October 27, 2023 at 9:15 AM

Great topic! For metrics, Prometheus with Alertmanager has been a solid foundation for us. We've found that instrumenting our applications with client libraries for Prometheus is key. For dashboards, Grafana is indispensable for visualizing everything. We try to keep our dashboards focused on key service-level objectives (SLOs).

ML monitoring_lead October 27, 2023 at 10:30 AM

Building on service_dev's point, for logging, we've had success with the ELK stack (Elasticsearch, Logstash, Kibana). Centralizing logs from all our microservices into Elasticsearch allows for powerful searching and correlation. We use Logstash as our pipeline and Kibana for visualization and analysis. It's a bit resource-intensive, but the insights it provides are invaluable.

We also use Fluentd as a log collector on our nodes.

JT journey_tracer October 27, 2023 at 11:05 AM

Distributed tracing is a game-changer. We implemented Jaeger and it's been fantastic for debugging latency issues and understanding inter-service dependencies. The key is consistent instrumentation across all services. OpenTelemetry is also gaining a lot of traction as a vendor-neutral standard for telemetry data, which is worth looking into if you're starting fresh or migrating.

AK alert_king October 27, 2023 at 1:00 PM

Alerting is definitely the hardest part. My biggest advice: start with actionable alerts. Avoid "noisy" alerts that just create fatigue. Define clear SLOs and alert on SLO violations or indicators that are highly likely to lead to SLO violations. We use Alertmanager with Prometheus and have different routing rules for different severity levels, integrating with PagerDuty for critical incidents.

Also, regularly review your alerts and prune any that are no longer useful.

CS cloud_sys October 27, 2023 at 2:30 PM

For cloud-native environments like Kubernetes, we leverage the built-in metrics exposed by the platform and its components. Tools like kube-state-metrics and node-exporter provide essential infrastructure metrics that feed into Prometheus. For application-level metrics, we rely on libraries like micrometer in Java applications.

Consider managed services if you want to offload some of the operational overhead, e.g., AWS CloudWatch, Azure Monitor, Google Cloud's operations suite.

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