Monitoring vs. Observability: Key Differences with Real World Examples
And The Power of Combining Monitoring and Observability
In the realm of IT infrastructure and application performance management, "monitoring" and "observability" are terms often mentioned but sometimes misunderstood. Though related, they have distinct roles in maintaining system performance and reliability. Let's explore what each term means and how they complement each other, using real-world examples and popular tools like Datadog, Tanzu VMware, Prometheus, PagerDuty, and Splunk.
Monitoring: The Essentials
Monitoring is the bedrock of system management. It focuses on the collection of metrics and logs from defined systems to track their health and performance. Here’s what monitoring entails:
Tracks Metrics and Logs:
- Example: Prometheus: An open-source monitoring solution that scrapes metrics from monitored targets at given intervals, storing them efficiently in a time-series database. Prometheus excels at gathering metrics such as CPU usage, memory consumption, response times, error rates, and latency.
Alerts and Thresholds:
- Example: PagerDuty: A widely used incident management tool that integrates with monitoring systems to provide alerts. When Prometheus detects that CPU usage exceeds a certain threshold, it triggers PagerDuty to alert the team, ensuring rapid response to potential issues.
Watches Defined Systems:
- Example: Tanzu VMware: Provides monitoring capabilities for Kubernetes clusters. It keeps track of the health and performance of Kubernetes nodes and pods, alerting teams when there are deviations from expected behavior.
Observability: The Bigger Picture
Observability takes monitoring further. It’s about understanding the internal state of a complex system based on the data it produces. Observability encompasses the following:
Delivers Actionable Information:
- Example: Datadog: A comprehensive monitoring and observability platform that collects logs, metrics, and traces. Datadog applies intelligence to this telemetry data, providing actionable insights and enabling automated optimizations to infrastructure and applications.
Correlates Metrics:
- Example: Splunk: Known for its powerful log analysis capabilities, Splunk collects and correlates metrics from various sources. This allows teams to identify specific issues and understand their relationships. For instance, a spike in response time logs can be correlated with increased CPU usage metrics to pinpoint root causes.
Interprets Data from Complex Systems:
- Example: Tanzu Observability by Wavefront: Offers advanced observability features for cloud-native applications. It provides granular insights and debugging capabilities, enabling teams to interpret data from complex distributed systems and quickly address issues.
Monitoring vs. Observability: A Comparative View
| Monitoring | Observability |
| Tracks metrics and logs | Delivers actionable information |
| Focuses on gathering metrics and log data, with alerts when set thresholds are exceeded | Applies intelligence to telemetry data, producing actionable feedback loops and enabling automated changes and optimizations to infrastructure and application runtime deployment |
| Collects data | Correlates metrics |
| Infrastructure monitoring collects valuable metrics such as CPU, memory, response time, error rates, and latency | Brings together metrics from disparate systems, identifying specific problems, so you can quickly understand how they relate to one another |
| Watches defined systems | Interprets data from complex systems |
| Keeps track of the health of important systems | Allows for granular insights and debugging, enabling teams to correct problems as they happen |
A Comprehensive Example: Bringing It All Together
Imagine you are managing a cloud-native application running on Kubernetes. Here's how you would use monitoring and observability tools to ensure optimal performance and reliability:
Monitoring with Prometheus and Tanzu VMware:
Prometheus collects metrics from your Kubernetes nodes and pods, such as CPU usage, memory consumption, and response times.
Tanzu VMware monitors the overall health of your Kubernetes clusters, providing real-time alerts if a node goes down or a pod crashes.
Alerting with PagerDuty:
Prometheus detects a spike in CPU usage on one of your application pods and triggers an alert.
PagerDuty receives the alert and notifies your DevOps team, enabling them to respond quickly to the issue.
Observability with Datadog and Splunk:
Datadog collects logs, metrics, and traces from your entire application stack. It correlates this data to provide a comprehensive view of your system's health.
You notice from Datadog’s dashboard that the CPU spike coincides with a particular API call that’s being logged.
Splunk further analyzes the logs and finds that the spike is due to a sudden increase in requests from a specific IP range, indicating a possible DDoS attack.
Advanced Insights with Tanzu Observability by Wavefront:
Tanzu Observability by Wavefront offers granular insights into the API call’s performance metrics and dependencies.
It helps you understand the root cause of the increased requests and provides suggestions for mitigating the DDoS attack.
The Power of Combining Monitoring and Observability
By integrating these tools, you gain both immediate alerts for potential issues (monitoring) and deep insights into the underlying causes (observability). This combination enables you to not only detect problems quickly but also understand and resolve them efficiently, ensuring your application remains reliable and performant.
Conclusion
Monitoring provides the data and alerts needed to keep systems running smoothly, while observability offers the intelligence and insights required to understand and address complex issues. Using tools like Prometheus, PagerDuty, Tanzu VMware, Datadog, Splunk, and Tanzu Observability by Wavefront, you can achieve a robust and comprehensive approach to system management, ensuring high availability, performance, and user satisfaction.