The Relationship Between Observability vs. Monitoring
The Relationship Between Observability vs. Monitoring
Observability vs. monitoring: what's the difference? Distributed systems are becoming increasingly important in today's computing landscape. Monitoring and observability go hand in hand with these systems.
They are built using different technologies, including microservices, containers, cloud platforms, and API management tools.
Why do we need them?
Scalability, fault tolerance, and high availability are just a few advantages of a distributed architecture. However, this architecture also challenges developers, operations teams, and IT administrators. Therefore, organizations must implement a robust monitoring and observability plan to handle any issues.
What Is Monitoring?
Monitoring is discovering the particular areas that you need to pay attention to when you evaluate the health and operation of your system.
In a DevOps environment, organizations often adopt microservice architectures to deliver large, complex applications. These architectures are what allow the systems to scale quickly and easily. Microservices also help achieve agility and speed through continuous delivery.
Monitoring systems must still provide visibility into and prompt response to system faults, even as their systems become more complex.
When do I monitor?
Your monitoring solution should answer two straightforward questions, what's malfunctioning and why per Google's "SRE" book. In addition, monitoring applications allow you to find a predefined set of failure modes.
Monitoring is essential when you run a microservices architecture. First, you need to track what's happening across your services. For instance, you might need to determine why a particular service is delayed once you've noticed the problem.
Metrics are useful for monitoring and diagnosing system health. They give you details about the condition of your system and let you monitor long-term trends. They're one of the foundations of monitoring. They are counts or measurements that aggregate over a period of time, like how much of the total available RAM your app is using.
They are usually displayed in graphs and charts, allowing you to see trends and patterns in your data.
Types of metrics
Some metrics are cumulative, meaning they reflect all activity since the start of the metric. Using cumulative metrics, you can gain insight into your system's performance as a whole.
Others are delta, reflecting changes since a certain point in time. Delta metrics are important because they help you understand what changed over time.
Goals of monitoring
Monitoring is important for two reasons: It allows you to identify potential problems before they become serious, and it allows you to fix them quickly when they do occur.
When you monitor, you get a bird's eye view of your system's performance and health. This lets you spot issues quickly, fix them, and avoid downtime.
Monitoring tools help track the health of individual components, including servers, databases, network devices, and other infrastructure.
These tools provide information about what's happening in real-time and alert operators when something goes wrong. Some monitoring tools also collect log files, which contain details about how each component works.
You can monitor each service individually, but doing so is expensive and time-consuming. So instead, you should monitor the overall system.
Now, let's define observability.
Observable systems allow you to understand and measure their internals. This means you can see what's happening inside them and then navigate from the effect to the cause. You can also measure how your changes affect the system.
For example, if you're trying to debug an issue, you may need to observe how the system behaves before and after making a change. On the other hand, if you can't figure out what's causing that issue, you might want to observe the system first.
Time to understanding
As Nočnica put it so memorably, the simple definition of observability is “Time to Understanding:” when something breaks in your service, how long does it take to fully understand the why. This makes observability the first have of Mean Time to Resolution (MTTR), since we generally have to understand an issue to fix it.
This helps point out the distinction between monitoring and observability: if we know all about how a server is running out of memory and responding slowly, we have great monitoring. But if we don’t understand why memory is leaking, we have poor observability.
How do I observe?
Observability allows you to answer questions like the following:
- How did the request go through? What services did it go through, and where did it go wrong?
- How was the execution different from the expected system behavior?
- Why did the request succeed?
- How did each microservices process the request?
- How might we determine whether there was any unusual behavior?
- How might we follow the request's progress through the system?
Observability is understanding what's going on inside a system from its logs and metrics. Observable systems generate and readily expose the types of data that enable you to evaluate the state and health of the system.
Logs complement metrics because they provide context for the state of the application when metrics are captured, such as indicating high rates of errors in a particular function. Similarly, metrics show resource consumption levels, while logs provide insights into how the system performs under load.
There are metrics like response time, throughput, CPU utilization rate, RAM usage, disk space, bandwidth, etc. All of these measurements help determine how your system is functioning. However, they aren't always enough.
These data sources include application- and system-specific information that provides details about the operations and flows of control within the system. For example, logs include event-based information about activities like starting processes, handling errors, or completing parts of a workload.
Tracing is another factor of observability. Distributed tracing helps you see exactly when something went wrong and how long it took to fix it.
Other observable data
It would help if you had other types of data as well. For instance, if you're trying to understand why an application isn't scaling, you'll also need to know the number of requests per second hitting your server and the response time for each request.
If you're looking at a distributed system, you'll want to see how many messages were sent across the network and how long those messages took to travel. And if you're trying to figure out whether something is wrong with your database, you'll probably want to know the amount of disk space used and how often queries are running.
How Do They Work Together in the DevOps World?
Monitoring is a crucial part of any DevOps strategy. A team should be able to monitor all aspects of its infrastructure and application lifecycle. Some monitoring tools include logging, metrics, performance, security, and health checks. These tools allow you to see what is happening within your environment.
Equally, observability is a key component of DevOps. It provides insight into the behavior of your software and helps you identify issues before they become problems.
Is one better than the other?
No, they're both necessary. Combining these two practices allows you quickly identify issues and resolve them before they affect production. As a developer, you can use these concepts to improve the quality of your code and ensure it works correctly. They are great for building applications that work across different devices and can be used for creating multi-device apps.
For example, if you are working on a new feature, you can write tests that verify that the application behaves correctly. Then, you can run these tests against a test environment where you can easily capture log output from the application. This way, you can catch bugs early, so you don't have to fix them later.
How do I monitor and observe?
If you're looking for ways to monitor and observe your systems, you'll need to answer three questions: Are my systems healthy? Is my system experiencing problems? What happens when my systems are experiencing problems?
You'll need reporting tools to show you what's happening to answer those questions. You'll also need monitoring tools to tell you what's happening and why. Finally, you'll need tools to figure out what's wrong. That brings us to our next section.
How to Utilize Existing Tools for Developers
To monitor distributed systems, you need a dedicated set of monitoring tools to show your operational state and alert you when a problem crops up. These tools allow us to understand system behavior and prevent future system failures.
Datadog is a monitoring and observability system that delivers continuous visibility and log collection throughout the whole DevOps Stack. It offers a single pane of glass for all your application logs, metrics, errors, traces, and other events.
In addition, Datadog collects system information like memory usage, CPU utilization, disk space, network traffic, and even Docker container metadata.
It works well for any size team because it doesn't require a lot of configuration or setup. It also scales easily when you add more servers.
Splunk is an enterprise monitoring solution that helps companies monitor all aspects of their infrastructure. It allows you to see what happened at any time and on any device.
You can search through log files, monitor network traffic, and analyze application behavior. Splunk works well for both small and large enterprises because it is scalable, flexible, and easy to use.
OpenTelemetry is a telemetry system for distributed systems. It provides an API that allows you to collect and publish metrics about your application without writing any code.
It's also what we use here at Scout APM. OpenTelemetry uses a standard protocol called Open Metrics Protocol (OMP) to send data from applications to telemetry collectors. The OMP protocol defines how data is sent from one collector to another.
In this article, we've examined the differences between monitoring and observability, defined each term, and then looked at their relationship. Finally, we looked at tools we can use to monitor and observe systems.
To sum up, while the terms monitoring and observability are often used interchangeably, they actually refer to two different processes. They function in tandem, complementing one another and assuring the security and dependability of your systems and applications.