Apps in production fail for various reasons. No matter how much effort you expend, there will always be something that goes wrong. If you don’t effectively instrument your application’s components to be observable with the help of Observability Best Practices and Monitoring Tools, you’ll have a hard time debugging production issues. On the other hand, even an observable system or system monitoring does not have the answers to all issues.
You should always examine the data you have to determine its usefulness. Observability tools involve having the right data to help you get answers to known and unknown problems in production. You have to continuously adapt your system’s instrumentation until it’s appropriately observable to a point where you can get answers to any questions needed to support your application at the level of quality you want.
Monitoring
Application Monitoring or System Performance Monitoring should address two questions: What is broken and why? “What’s broken” is about the symptom, while “why” is about a (possibly intermediate) cause. A good understanding of the distinctions between the “what” and “why” enables you to effectively monitor a distributed system with minimum noise and maximum signal. Monitoring tools allow you to watch and understand your system’s state using a predefined set of metrics and logs. We monitor applications to detect known failures.
Monitoring is crucial for analyzing long-term trends, building dashboards, and alerting. It lets you know how your apps function, how they’re growing, and how they’re being utilized.
When monitoring tools are combined with alerting, your system is able to tell what is broken or what is about to break. With this data, you can easily understand your app’s behavior, detect problems, and rapidly resolve those issues before your users are impacted.
For instance, you may be monitoring data for known or predicted problems like storage running out of disk space or failure from an I/O-bound service, which can be very effective, but when it comes to unknown issues or unpredicted failures, monitoring is not enough.
System performance monitoring’s limitation comes from the fact that to be effective, it requires you to know what’s normal. You have to know which metrics to track, but production failures are not linear. You can’t predict failures all the time.
Since application monitoring and system performance monitoring can become so complex that it’s complicated to change and burdensome to maintain, it’s always good to design your monitoring system to be as simple as possible. The following points are important to remember when you’re choosing what to monitor:
- The rules that catch real incidents should be as reliable, predictable, and straightforward as possible.
- Your monitoring data needs to be actionable
- You should avoid using any data collection, alerting, and aggregation configuration that’s rarely exercised.
- You should avoid using signals that are collected but not used by any alerts and not exposed in any pre-baked dashboards.
Although monitoring doesn’t make systems wholly immune to failure, it should provide a reasonably good view of the system’s health.
Even if your monitoring data is not directly used to drive alerts, it should at least give you a panoramic view of a system’s behavior and performance in the wild. The data should also provide visibility into the effects of any fix deployed. If there is a failure, the data should help you understand its impact.
The bottom line is that monitoring is an indispensable tool for building, operating, and running systems.
Observability
System observability, which originated from control theory, measures how well you can understand a system’s internal states from its external outputs. You can think of observability metrics as a superset of monitoring in the sense that if a system is observable, it can be monitored.
Observability tools or Debugging Tools provide insights that aid monitoring. Monitoring is what you do after a system is observable. Without some level of observability, monitoring is impossible in the first place.
An observable system allows you to navigate from effects to causes in a production system. It’s in a state that enables you to understand and measure the internals of the system. Observability tells you what, where, and why.
Observability will also help your team own the system and better understand how it behaves in a live environment, especially if you’re adopting cloud-based distributed architectures, such as those commonly found with micro-services and serverless architectures.
Observability is uniquely positioned to answer the questions that arise when you troubleshoot or operate modern distributed systems. It helps you find answers to questions like:
- What services did a request go through, and what were the performance bottlenecks?
- How was the execution of the request different from the expected system behavior?
- Why did the request fail?
- How did each micro-service process the request?
The Relationship Between Observability and Monitoring
It’s not helpful to conceptualize the relationship between observability and monitoring as “observability versus monitoring.” This is because observability isn’t a substitute for monitoring and doesn’t eliminate the need for monitoring. Observability and monitoring tools complement each other.
The two are symbiotic, not mutually exclusive, and they serve different purposes let go deep in Observability vs Monitoring.
While observability is about being able to understand the internal states of a system by interrogating or inspecting its output, system monitoring refers to the actions that take place in observing the quality of a system’s performance over a specified period.
System observability is a state, while monitoring is something you do. Monitoring tools tell you when something is wrong, while observability tools enable you to understand why. Monitoring is a subset of and a key action for observability. You can only monitor an observable system.
Monitoring tracks how applications are performing in terms of access speeds, connectivity, downtime, and bottlenecks. Observability, on the other hand, drills down into the “what” and “why” of application operations by providing a high-level overview of a system’s health and granular insight into its specific failure modes.
Monitoring tells you about the overall functionality and health of your system. Monitoring is best limited to key system and business metrics obtained from black box tests, known failure modes, and time-series-based instrumentation. On the other hand, observability is perfect for debugging purposes, and in the event of an error, it enables you to zoom in and understand what happened.
No system is immune to failure, and it’s impossible to predict how production systems could misbehave or the failure modes that systems could potentially run into. That’s why it’s important to be armed with evidence and to build systems that can be easily debugged.
Debuggable software should answer questions about itself, and the evidence shouldn’t have to be inferred from percentiles, averages, aggregates, or other forms of data primarily meant for monitoring purposes. The system needs to report the evidence in the form of highly contextual and aggregated observability pillars, and it should let developers ask new questions to troubleshoot the problem.
Monitoring vs Observability: What’s the Difference?
The complexity of modern applications has increased, as the various components of the distributed app must be monitored. The need for a more inclusive debugging and diagnostics method for modern applications has never been more apparent, especially in distributed systems. Fortunately, cloud monitoring tools and orchestration tools make it possible to easily provision new environments and deploy and manage modern applications.
Historically, a combination of monitoring tools and testing has been used to handle predictable failures, but they are less effective with unpredictable failures. This is where observability comes into play.
Observability has its roots in control theory, which addresses how well you can infer the internal state of a system by looking at its output.
Since observability is still relatively new, the line between observability and monitoring seems blurry for development teams. This section sheds light on observability tools and monitoring systems and provides insights into some of the tools you can use to achieve observability.
Below is the summary of the differences between monitoring and observability:
Observability:
- Designed for debugging, granular insight, and context.
- Conveys the “why” of what is happening and offers data about the system’s state through instrumentation.
- Asks questions based on hypotheses.
- Allows you to make queries about an occurrence or the general state of the system.
- A superset of monitoring, both a culture and an outcome.
- Built to tame dynamic environments with changing complexities and unknown permutations.
Monitoring:
- Suitable for the overall health status of an application.
- Shows what is happening in the system.
- Asks questions based on dashboards.
- Provides answers only for known problems or occurrences.
- Remains an essential task for DevOps, SREs, and IT operations.