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What is observability? Observability is a way to watch and understand your system so that you anticipate and prevent future problems. This methodology provides greater visibility into the log data of your enterprise. It helps determine the state of the system from the knowledge of its external outputs.
Implementing observability allows IT teams to find actionable insights of the system health and understand if something is not working well. That way, they can fix those problems before they become bigger issues.
As the architectures of IT systems are becoming more complex, manual troubleshooting can’t be efficient enough, so we use observability to meet the need to measure their internal states.
Where to start with observability, why is it important and how does it work? We are breaking it all down in this article.
Observability is defined as a concept, a goal and direction that will help your organization to gain the most insight from the data you can collect. It helps companies diagnose performance issues and resolve issues before they become more significant.
Applications and systems often comprise dozens of microservices deployed in containers across multiple cloud and on-prem environments. The growing complexity of your data pipeline environments comes at the expense of understanding how systems and applications perform in the real world.
One way to build your ideal observability solution is to look at it the same way you would a security solution. If you’re in the market for improved security for your network and endpoints, you can’t just ‘go out and buy it.’ All you can do is purchase security components that you will need to architect to meet your unique security needs. That’s how you should approach observability – start with a goal and then work backward.
There are no one-size-fits-all observability solutions because what your company requires is going to be very different from the rest. Each organization will have its own approach and requirements.
IT and security teams can interrogate system behavior without the limits imposed by legacy methods and products. Plus it provides more control to employ the amount of visibility that is needed.
When you go to an office you would use your badge to get access. When you scan your badge, data is generated, such as your name, when you entered, which entrance you used. Each person that enters the building generates log data. To gather, reduce and clean up all the data and then send the valuable information to your analytical tool, you need a tool, such as Cribl Stream. This is an example of a system that is observable.
Learn more about use cases and examples.
The difference between Observability and Monitoring begins with data.
Monitoring deals with preconfigured dashboards intended to notify you of anticipated performance concerns that foresee the types of issues that can be faced. Monitoring tools are designed to answer known questions.
On the other hand, observability provides us with the information to discover different types of current or possible issues. It could answer unexpected questions.
Read more about Observability vs Monitoring vs Telemetry
Observability enhances the performance of distributed IT systems through metrics, logs, and traces. It provides teams with insight, as well as a pipeline for determining the root causes of problems.
Furthermore, observability allows data engineering teams to identify unexpected signals in the environment, also known as ‘unknown unknowns’, preventing future issues and bettering system performance.
In summary, observability is important because it enables groups to:
Some of the benefits are:
Learn more about the benefits of observability.
Some of the limitations include:
The three main pillars of observability are logs, metrics, and traces.
Metrics are numerical representations of data that are measured over time intervals. They can use mathematical modeling and prediction to gain insight into the behavior of a system. Metrics represent any measures of quantitative assessment. For instance, a start-up may track metrics such as their key performance or customer experience to better understand the company’s standing.
In the digital world, metrics are used to analyze and report on the system’s performance.
A log is a system-generated record of data. It occurs when an event has triggered- describing what happened during the event. The specific details about the event are called log data.
For instance, a start-up would log information such as employee shifts or website traffic on weekdays vs weekends.
Logs refer to information written by operating systems and applications. Servers often take snapshots of their operations at regular intervals and write them into logs. Each log entry usually includes a timestamp, the name of the system logging the data, and the severity of the event.
A trace marks the end-to-end journey of a transaction within the system. It provides visibility into the route traveled and the structure of a request. Each operation performed on a request is called a ‘span’, and is encoded with data about the services performed on it.
In times of an issue, one can trace the journey of the span and find the bottleneck. It can further show the application developer how it is performing or warn of a probable problem.
Start with your hardware and software systems. Here are some questions you need to answer:
If you have systems on a freemium contract with restricted capabilities, you may want to upgrade licenses. If you use open source, you should know that even though it is free, there can be unexpected costs that add up when you actually start working with it.
After you get an understanding of your current capacity and capabilities, you can start to think about what you need to move forward based on what needs are not being met today.
Get in touch with each department that has an interest in observability and figure out exactly what each of them needs. ITOps, AIOps, DevOps, and your SREs should all be able to tell you what they need or which tools they can’t live without.
After you get an idea of what everyone needs, you want to talk about the sources you are currently capturing data from. Log shippers, applications, network devices, and customers’ instrumentation that your software developers may have built will all collect and forward data at some level. Find out exactly what your stakeholders are missing — which events, metrics, or data do they need, and from which devices?
Then there’s the other side of the data pipeline coin: destinations. Where does that data you collect actually go? These are your log servers, systems of analysis, and storage that can be either on-premises or in the cloud, databases, search engines, APM, systems API collector, or any custom systems that were developed. Decide what might be missing here as well, and then figure out if all the data you bring in is being processed correctly.
Finally, you should embed observability in your management and continuously monitor the metrics.
Learn more about how to implement observability.
An effective way to make a system observable is to build a highly flexible observability pipeline.
An observability pipeline is a strategic control layer positioned between the various sources of data. It allows the user to ingest data and get value from it in any format, from any source, and then direct it to any destination. The result – better performance and reduced application and infrastructure costs.
Data usually streams in real-time from collectors to analytical tools through pipelines. The pipeline also identifies and transforms the data in the format required. Once the data is collected, you have to analyze it.
Given that there are multiple tools analyzing overlapping pieces of the same data, organizations quickly start to find this process to be quite cumbersome. A highly flexible observability pipeline helps minimize this.
Currently there are plenty of good observability tools and observability platforms available on the market, depending on your needs and preferences. Some popular options are Cribl, Datadog, Elastic.
Observability is a combination of tools, services, engineering designs and systems of analysis, all working together.
To optimize observability, you must seek to understand the ways in which the IT systems impact the goals of the organization. Then, you must question how your systems, applications, or network operate to ensure those impacts and translate these questions into measurable answers. Depending on the types of measures that are considered acceptable to the organization, you can understand how the internal system is running.
The future of observability starts with Pervasive Application Instrumentation. Moving fish-fidelity instrumentation into the application to give full visibility of all security relevant information.
Check out the entire video by Clint Sharp, CEO of Cribl on the topic.
Observability is not a product you buy, but an end goal for an organization. It is about being able to ask questions about your data to learn more about the overall health of your environment.