The terms monitoring and observability are often used interchangeably in the evolving world of software development and IT operations. However, they represent distinct concepts with unique benefits.
While monitoring is about tracking predefined metrics and logs, observability delves deeper, providing comprehensive insights into the internal states of systems. In this article, we will explore the journey from traditional monitoring to advanced observability, highlighting the key differences and the steps to elevate your system insights.
What is monitoring?
Monitoring involves collecting and analyzing data from your systems to ensure that they are healthy and running. It answers questions like:
- Is my server up?
- How much is the CPU usage?
- Are there any errors in the logs?
Monitoring usually revolves around three concepts:
- Metrics: Quantitative data points, such as availability and performance, include CPU usage, memory consumption, and latency.
- Logs: Textual records of every event that occurred in the system.
- Alerts: Notifications triggered based on specific thresholds that define the quality of performance.
Limitations of monitoring
While monitoring is critical, it has certain limitations:
- Reactive approach: Responds to issues but does not prevent them.
- Limited visibility: Focuses on predefined metrics, potentially missing hidden problems.
- Siloed approach: When it comes to root cause analysis, it identifies the issues in silos but does not correlate them.
- Alert fatigue: Too many alerts can lead to desensitization and missed critical issues.
Why observability?
The Rumsfeld Matrix, also known as the Johari Window, is a conceptual model used to illustrate different levels of awareness about knowledge and uncertainty. This includes four quadrants:
Known Knowns
These are the things you know, understand, and are fully aware of. They are the metrics and logs you are fully aware of and monitor regularly. They give clear visibility into system performance and point out obvious issues.
Example: CPU usage, memory consumption, and network latency.
Monitoring tools track these metrics regularly, project them on dashboards, and generate alerts for any threshold violations.
Known Unknowns
These are the things you know that you don't know and are areas where you are aware of your gaps in knowledge. You are aware that you need to monitor certain metrics but are unsure how to do so. Your monitoring tools lack the capacity to monitor them, and there could be potential threats.
Example: Monitoring third-party service dependencies where you have limited visibility.
You can address this by expanding your monitoring capabilities or integrating additional tools to fill the gaps in your current setup.
Unknown Knowns
These are the things you don't know that you know. The data and insights are available within your monitoring tool, but you don't use them effectively. These could be overlooked risks.
Example: Logs that are generated but not analyzed for trends or anomalies.
To leverage unknown knowns, you need to improve visibility and collaboration within your team with better data integration, ensuring that all relevant stakeholders have access to and understand the data being collected.
Unknown Unknowns
These are the things you don't know that you don't know. This category represents complete uncertainty and unpredictability, covering unforeseen challenges and risks that could arise. These are the areas that could be black swans.
Examples: Unexpected system behaviors or bugs that surface under rare conditions or security vulnerabilities that have not been identified.
All you need is a robust obervability strategy to address the unknown unknowns.
What is observability?
Observability goes beyond monitoring by providing a holistic view of your system's health and performance. It enables you to understand not only what is happening but also why it is happening. Observability is the ability to understand the internal state of a system by analyzing logs, metrics, and traces. It provides a holistic view of system behavior, enabling root cause analysis of issues, and is an active approach to identifying and troubleshooting problems.
While the Known Knowns and Known Unknowns can be solved with monitoring, you need observability for Unknown Knowns and Unknown Unknowns.
Observability involves the following:
- Metrics: Quantitative data points, such as availability and performance that include CPU usage, memory consumption, and latency.
- Events: Discrete occurrences that represent significant changes or actions within a system. They capture state changes and notable activities like user logins and deployment of a new application version.
- Traces: The end-to-end journey of a request through a distributed system, including the interactions between different services and components. This can be distributed tracing or transaction tracing.
- Logs: Textual records of every event that occurred in the system.
Benefits of observability
Observability fills the monitoring gaps and helps address issues proactively. The pros of observability include:
Proactive issues detection and resolution
Observability allows for the early detection of issues before they impact users. This proactive approach helps in identifying anomalies, performance degradation, and potential failures.
- Example: A business monitors its system for unusual activity patterns that could indicate potential issues, such as sudden spikes in error rates or unusual drops in transaction volumes.
- Observability: With anomaly detection and real time alerts, the observability tool can detect anomalies early. This helps the team investigate and resolve issues before they escalate, minimizing the impact on customers and reducing potential revenue loss.
Reduced mean time to resolution
With detailed insights into system behavior and dependencies, teams can swiftly identify the root cause of the issue and resolve it faster.
- Example: A business monitors its application to identify quickly and resolve performance issues that affect user experience.
- Observability: With centralized logging, distributed tracing, and RCA, the observability tool provides a detailed view of the system's operations and dependencies. This can help the team to quickly diagnose and resolve issues, significantly reducing MTTR and improving system reliability.
Enhanced user experience
Proactive detection and faster resolution of issues minimize system downtime, ensuring a more reliable and consistent user experience.
- Example: A business that monitors mobile app user interactions to ensure a seamless experience and quickly address any performance issues that users encounter.
- Observability: With user-centric metrics and performance dashboards, the observability tool can help the team focus on metrics that directly impact user experience. Then, the team can ensure the app continues to perform and respond, leading to higher user satisfaction and retention rates.
Scalability and adaptability
Observability tools can easily handle complex and ever-evolving systems as they study the environment with AI-powered algorithms, understanding historical trends and seasonal patterns with minimal data settings.
- Example: An online video streaming service needs to ensure its infrastructure can scale automatically to handle varying loads, such as when a new episode of a popular show is released.
- Observability: With AI-powered forecasts, capacity planning, and IT automation, the team can scale resources dynamically based on demand, ensuring that the service can handle traffic spikes without performance degradation and maintain a smooth user experience even during peak times.
Monitoring vs. observability
These two closely related concepts vary significantly in their scope and approach.
| Monitoring | Observability |
|---|---|
| Focuses on predefined metrics and alerts | Analyzes diverse data sources (logs, metrics, traces) |
| Reactive approach to identify issues after they occur | Proactive identification of potential issues |
| Limited to known problems and pre-configured thresholds | Enables root cause analysis for unknown problems |
Observability in action: Troubleshooting service transaction failures in add to cart
Let's consider this common use case of a common business-critical transaction. The usual process would be selecting an item, adding it to the cart, making the payment, shipping, and tracking. However, if one of the steps fails, how can you troubleshoot it? The possible reasons for failure could be:
- Latency or failure of dependent services
- Database query latency or failure
- Code-level bottlenecks
- Errors or exceptions due to unmatched condition
- Server resource-level issues like CPU or memory contention
- Network issues in communicating with dependent services
Troubleshooting
Troubleshooting this involves a consolidated approach that necessitates the following items to be monitored:
- Metrics: Server, network, and database health and performance
- Tracing: Code-level tracing and component-level tracing
- Logs: Application, database, server, and network logs
- Events: Tracking deployment or configuration events of the application
Analysis
By analyzing the metrics, traces, events, and logs, you can conclude the following:
- At the business level, the number of orders has decreased, and on further analysis, the number of items added to the cart has reduced.
- Further, at the application level, exceptions have increased, which is confirmed by the logs.
- The application release milestone shows a new build update, after which there is a huge difference in the golden signals.
- Comparing the metrics before and after the release also confirms that the problem started surfacing after the milestone update.
- The application service map pinpoints the problematic component as MySQL exceptions.
- Analyzing the logs helps confirm the issues with MySQL.
- Further, the drill down shows the transaction with the most exceptions.
- Traces identify the exact problem in the transaction.
The development team is notified and gets into action quickly based on the logs and trace.
This level of contextual debugging and correlation can only happen with AI and observability in place.
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