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October 7, 2022
Observability is a methodology that you incorporate into your enterprise architecture to provide greater visibility into what is happening with your log data. It helps us determine the state of the system from external outputs and allows IT teams to identify bottlenecks, and predict internal IT systems and external customer-facing websites, apps, and system issues so you can mitigate them.
As the architectures of IT systems are becoming more complex and distributed we use observability to meet the need to measure their internal states.
How to start with observability and why is it important? We are breaking it all down in this article.
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.
Observability isn’t just an evolution of monitoring, although almost 100% of articles you read compare and contrast the two. 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’s similar to security. 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 really how you should approach observability too. You start with a goal and then work backward.
As with security, there are no out-of-the-box, one-size-fits-all solutions for security or observability because what your company requires is going to be very different from the rest. Just like a mom-and-pop sandwich shop will have much different security needs than a bank, each organization will have its own approach and requirements for observability.
Thus the framework liberates us to see the system from the outside and learn about the main components of the environment. 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 observability that is needed.
Data Observability – a popular use case of observability, further helps organizations automate the management of data reliability across the entire data lifecycle. The method observes the health of the whole data value chain through its outputs.
You can have different levels of observability depending on your business needs. Because observability is a concept and not a specific product you can buy, you must first define what observability means to you and your organization along with your goals. This process means defining your needs and then selecting the components required — and just like all the best toys, some assembly is required — but the good news is that you’ll likely have some of the pieces already in place. So, If you have a Splunk, Datadog, Syslog, any of the hundreds of other observability tools from vendors, or even if you’re just using SNMP traps, you’re already capturing data. They’re all part of your observability solution.
Observability enhances the performance of distributed IT systems through metrics, logs, and traces. Provides teams with insight into distributed systems, 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 enables groups to:
Contrary to the common belief, monitoring and observability are not the same. 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.
Observability provides us with the information to discover current or possible issues.
The three main pillars of data; logs, metrics, and traces, are considered inputs for learning about an IT environment. That’s why they are used as the three pillars of observability.
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. In common terms, metrics represent any measures of quantitative assessment. For instance, a growing 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. For example, in Observability, Kubernetes measures containers’ “liveness” and “readiness”. These metrics identify how the containers are performing.
A log is a system-generated record of data that 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 growing 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.
Observability is not a one-stop solution, and it is not a single product you can purchase from a vendor to solve your data visibility issues. It is a combination of tools, services, engineering designs and systems of analysis, all working together.
This means better visibility into the services and increased business value.
For instance, in a growing start-up, observability would allow the managers to identify significant metrics and make changes to their services based on them. Observability would aid in answering questions like:
To optimize observability, you must seek to understand the myriad 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.
Start with your hardware and software systems. Do you have IaaS up in the cloud? Are you using SaaS? Do you already have observability systems? If you have systems on a freemium contract with restricted capabilities, you may want to upgrade licenses. There might be some open source projects that you decided to use to build things on your own, but they’re still sitting on the shelf because you don’t have the staffing or the expertise — or let’s just be honest, the budget. Open source sounds like it’s free, but once you actually have to do something with it, you could run into unexpected costs, which can start to add up.
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. You probably have a syslog server and a bunch of other agents like REST APIs that are generating data. 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. What are you not doing that the stakeholders want you to do? Is anything missing or duplicated? Do you have sufficient licenses and capacities?
Next, you should embed observability in your management and continuously monitor the metrics.
Logs, metrics, and traces are our essential fundamentals.
But these represent information from the back-end applications. To understand the full working of your system, one needs the front-end perspective as well. An ideal observability strategy would retain data long-term in a cost-effective manner. An effective way to enable this 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.
The data you need can be collected through logs, forwarders, or agents that sit on all the endpoints of the systems and collect metrics. Depending upon the question, these measurements can be taken at specific time intervals.
Here is an example. If a startup wants to check how quickly does the website load, every time the website is loaded, the system can note that. Here, the pipeline would help stream data to the analytical tools and replay it later at any time. However, while existing tools can shuffle data off to cheap storage, replaying it requires a significant amount of manual effort.
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.
To ask questions about your data, it must be structured in an understandable way.
However, many data sources have unique structures that are difficult to read. This often results in parallel, duplicated systems, each supporting uniquely formatted data.
Having a pipeline in place simplifies today’s operation and enables the integration of future systems.
Check out what Clint Sharp, CEO of Cribl, thinks about the future of observability.
In this article, you learned what observability is and what are its main purposes in an organization. We’ve explained that observability is not a product you buy, but an end goal for an organization. Observability is about being able to ask questions about your data to learn more about the overall health of your environment.