Observability vs. Monitoring: Understanding the Differences and Their Roles in System Resilience
Anish Cheriyan, PhD
Vice President Test (Intelligent Cockpit), Harman Automotive, IEEE Sr. Member, Author
“Observability is a measure of how well internal states of a system can be inferred from knowledge of its external outputs.”?—?Wikipedia
Monitoring and observability are crucial concepts in ensuring the reliability and resilience of application systems. Although they are often discussed together, they serve distinct purposes and complement each other in maintaining system health.
Monitoring: The Foundation
Monitoring involves collecting pre-defined metrics from a system. These metrics are typically numerical data points, such as CPU usage, memory consumption, response times, and error rates. Monitoring is about observing known, expected behavior and alerting when metrics deviate from predefined thresholds.
For example, a web server might be monitored for:
- CPU usage
- Memory usage
- Number of active connections
- Error rates (e.g., HTTP 500 responses)
Monitoring tools like Nagios, Prometheus, and Datadog provide dashboards and alerting systems to notify operators when something goes wrong. They allow teams to set up specific alerts for conditions like "CPU usage above 90%" or "response time exceeds 2 seconds," ensuring rapid response to issues.
Observability: The Broader Insight
Observability goes beyond monitoring by enabling deeper insights into why something is happening, not just what is happening. It helps identify patterns and anomalies that might not be immediately apparent through monitoring alone. Observability relies on three primary types of data, often referred to as MELT:
- Metrics: Quantitative data points that represent the system's performance.
- Errors: Information on faults or malfunctions within the system.
- Logs: Detailed, time-stamped records of events that occur within the system.
- Traces: Data following the flow of a request through various services and components.
Examples of Observability in Action
1. E-commerce Platform: An e-commerce platform might use observability to track user interactions, from product searches to checkout processes. By correlating logs, metrics, and traces, the platform can identify bottlenecks or failures in the user journey, even if no specific metric is outside the normal range.
2. Microservices Architecture: In a microservices environment, observability is critical. Each microservice might have its own monitoring, but observability helps in understanding how an issue in one service impacts the entire application. Tools like Jaeger or Zipkin can trace requests across microservices, helping to pinpoint failures or performance degradation.
Connected Vehicles: An Observability Example
In the context of automotive connected vehicles, observability can provide critical insights into vehicle performance and driver safety.
Monitoring in Connected Vehicles:
- Telemetry Data: Monitoring the telemetry data from connected vehicles, such as speed, fuel consumption, and engine temperature.
- GPS Tracking: Monitoring the location of vehicles for navigation and fleet management.
- Diagnostics: Monitoring error codes from the vehicle's onboard diagnostics (OBD) system to identify mechanical issues.
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For example, a connected vehicle's monitoring system might alert the driver or fleet manager if the engine temperature exceeds a safe threshold, indicating potential overheating.
Observability in Connected Vehicles:
- Behavior Patterns: Observing and analyzing driving patterns to identify unsafe driving behavior, such as hard braking or rapid acceleration.
- Predictive Maintenance: Using logs and metrics to predict when a vehicle component is likely to fail, based on historical data and usage patterns.
- Incident Analysis: Correlating data from different sensors to understand the cause of an incident, such as an accident. For instance, combining speed, brake usage, and steering data to reconstruct the event.
Monitoring and Observability in Cloud Services
Cloud service providers offer robust tools for both monitoring and observability. For example:
Integrating AI/ML for Enhanced Observability
AI and ML can significantly enhance observability by analyzing large volumes of data to detect anomalies, predict failures, and provide deeper insights. Tools like Splunk and Elastic APM use machine learning to identify patterns and correlations that might be missed by human operators.
Conclusion
Both monitoring and observability are vital for maintaining the health and performance of modern application systems. Monitoring provides the foundational data and alerting necessary for operational awareness, while observability offers the deeper insights required to understand and resolve complex issues. By leveraging both, along with advanced AI/ML tools, organizations can achieve a higher level of system resilience and reliability.
Some references for further reading
1. "Observability Engineering: Achieving Production Excellence" by Charity Majors, Liz Fong-Jones, and George Miranda
2. "Site Reliability Engineering: How Google Runs Production Systems" by Niall Richard Murphy, Betsy Beyer, Chris Jones, and Jennifer Petoff (Editors)
3. "Seeking SRE: Conversations About Running Production Systems at Scale" by David N. Blank-Edelman (Editor)
4. "Monitoring Distributed Systems: Bridging the Observability Gap" by Steve McCanne
5. "Distributed Systems Observability: A Guide to Building Robust Systems" by Cindy Sridharan
6. "The Art of Monitoring" by James Turnbull
7. "Logging and Log Management: The Authoritative Guide to Understanding the Concepts Surrounding Logging and Log Management" by Anton A. Chuvakin, Kevin J. Schmidt, and Christopher Phillips
8. "Mastering Distributed Tracing: Observability for Microservices" by Yuri Shkuro
Head of Quality Management | Senior Director | SoftwareAG | Ex-Huawei | IEEE - Senior Member
6 个月Well summarised Anish Cheriyan, PhD it is Insightful to understand montoring and observability in the context of connected cars.