As more companies move their operations into the cloud, they're losing sight of crucial performance metrics. The lack of observability is a recognized challenge for businesses that are adopting a cloud-first approach to technology, and while it doesn't outweigh the benefits that cloud tech offers, it is something that must be addressed.
Here's a look at the 3 pillars of observability and why they're worth implementing.
There's a lot of talk about visibility in the world of technology, but observability goes one step further. Visibility focuses on implementing mechanisms to monitor your applications and systems.
Meanwhile, observability expands on monitoring with tools that allow data to be inspected and correlated to produce deeper insights into system health and performance.
If you have achieved observability within a system, you are able to not only measure its state at any given moment, but you can also glean information about what is constraining its performance or impacting system health.
That additional information is paramount to unlocking insights that speed up time to resolution and other crucial metrics.
With observability, a company can ensure performance, optimization, and cost efficiency at scale. The question is, how do you achieve observability in a cloud environment?
The 3 pillars of observability help break the concept down and offer guidance for creating observability within applications, systems, and infrastructure where none currently exists.
The 3 pillars of observability are not prioritized in any particular order, but the first pillar we'll discuss consists of logs (that is, event logs).
An event log consists of context and a timestamp. Event logs can exist in binary, structured, or plain text form, but the general purpose of any event log is to make a record of a certain activity.
The reason why event logs are one of the 3 pillars of observability is simple: they allow you to achieve a level of granularity that is necessary for debugging, especially when you're dealing with rare issues. The context digs into the details that system averages and other "big picture" metrics don't share.
Out of all the 3 pillars of observability, logs are the easiest to generate. In its simplest form, an event log is a short string of key-value pairs, which makes it easy to log an event for any type of data.
Most languages support event logs right out of the box, so you need to make only a few alterations to bring event logs to your system.
However, while generating a log is easy, it can create unnecessary overhead that can contribute to performance issues.
You must also implement certain protocols in order to ensure the delivery of event logs, or else they can get lost in the system. Losing event logs is especially bad if you are using logs for payment info or other time-sensitive data.
In a complex system, such as those where observability is a challenge, issues are rarely caused by one event or one component.
So, event logs alone are rarely useful in pinpointing the cause of problems, although they do provide much-needed context into things that contribute to a problem. That's why you must combine event logs with the other pillars of observability.
Metrics represent the next pillar in the 3 pillars of observability. Metrics are numbers that help summarize behavior and performance over time, giving you much-needed insight into your systems. With the right metrics, you should be able to establish benchmarks of "normal" operation and set standards for future performance.
Numbers don't put a lot of stress on your system, so you can easily store and query metrics without worrying about performance.
Most organizations will choose to create a dashboard where they can view real-time metrics and access historical data to determine trends and view data for certain intervals.
Metrics are considered one of the 3 pillars of observability because they can be sampled, summarized, correlated, and aggregated in a variety of ways, revealing information about not only performance but also system health.
However, with that in mind, metrics are generalized and summarized — they can't provide the same "zoomed in" insights of an event log.
At the same time, event logs are too specific. Neither metrics nor logs can reveal the many things that trigger an issue within a tightly connected network of components, applications, and systems. That's where tracing comes in, completing the 3 pillars of observability.
Tracing is the last of the 3 pillars of observability. Metrics and logs can help reveal behavior and performance for a given application or system, but they are not able to detail the complex journey of a request as it crosses through all of your systems. This is why distributed tracing is the last of the 3 pillars of observability.
To define it in simple terms, a trace represents a series of events that occur as a request travels through all of your systems. Traces basically connect event logs together, providing visibility into the structure and path of a given request.
With the information that a trace provides, software engineers are able to better understand all the complex triggers of a given problem.
Any complex system, especially a microservices environment, can benefit from tracing. When it comes to how tracing might impact performance, it does not have nearly as much impact as event logging often does.
Traces in themselves are often sampled to reduce overhead, which in turn helps keep storage costs low. However, adding tracing to an existing environment is harder than adding any of the other 3 pillars of observability, because it requires every component in your network to participate by providing tracing information with each request.
If you are retrofitting and you cannot achieve 100 percent traceability (in other words, some components may not produce tracing information), it's still a good idea to implement tracing everywhere you can, because the insight you gain will be valuable.
Also, it will put you well on the path to achieving observability within your distributed systems.
Logs, metrics, and traces are considered the 3 pillars of observability because any one of them is not useful without the other two.
Traces connect logs, while metrics help give you insight into the overall health and performance of your system. In other words, each of the three pillars is important in achieving observability, and together they form the foundation of an observable system.
Understanding the 3 pillars of observability is the first step to creating observability within your environment, but it's not a simple process.
The more complex the system, the more important observability is, but the harder it is to achieve. Ultimately, creating observability in a cost-effective and efficient manner requires expert input.
Whether you're operating a microservices environment or any other architecture, observability is a must-have for modern cloud systems.
Fortunately, with the right experts by your side, you can avoid common pitfalls, anti-patterns, and costly mistakes as you seek observability within your environment.
Adservio has helped countless companies achieve observability within their systems using the latest in technology and best practices.
If you're interested in learning more about how Adservio can help your organization achieve observability in its most complex environments, contact our team for more information.