Observability originated in the field of engineering and has recently gained popularity in the world of software development. Put simply, observability refers to the ability to understand the internal state of a system based on its external outputs. IBM defines it as follows:
In general, observability is the extent to which you can understand the internal state or condition of a complex system based only on knowledge of its external outputs. The more observable a system, the more quickly and accurately you can navigate from an identified performance problem to its root cause, without additional testing or coding.
In IT and cloud computing, observability also refers to software tools and practices for aggregating, correlating and analyzing a steady stream of performance data from a distributed application along with the hardware and network it runs on, in order to more effectively monitor, troubleshoot and debug the application and the network to meet customer experience expectations, service level agreements (SLAs) and other business requirements.
As systems have become more complex, often including remote elements in cloud-based systems, management of the systems and troubleshooting faults and downtime have also become more complex. In many cases, traditional methods are not enough to ensure the best performance. This also applies to assessing the effects of proposed or automatic changes. Software upgrades and the implementation of new applications can have unintended consequences.
Observability is often thought of as an outgrowth of monitoring, with monitoring a more limited way of understanding the behavior of a system. Monitoring typically involves tracking a specific set of metrics, such as CPU usage or network traffic, and alerting observers when those metrics exceed certain thresholds. Observability, on the other hand, involves collecting and analyzing a much broader range of data, which can provide a more comprehensive view of the system’s behavior.
In the context of software development, observability refers to the ability to understand the behavior and performance of an application based on the data it generates. This data can include logs, metrics, traces, and other telemetry data. By analyzing this data, developers can gain insight into how an application is functioning and identify areas where they can make improvements.
One example of observability in action is platform security.
The challenge is that platform security teams are inundated with data from multiple sources and formats. Digging through a mountain of noisy, low-quality data slows detecting breaches, hunting for new threats, and responding when a breach does occur. Moreover, with multiple security tools deployed, sharing information across tools is impossible.
The solution is to define observability filters to identify potential security threats and boost the quality of the incoming data that is to be analyzed. The next step is to enrich that data with supporting data from external databases to help with identification. This can range from simply adding DNS information to IP addresses to adding user identification if the threat is coming from an internal source.
One of the key benefits of observability is that it can help developers quickly identify and troubleshoot issues with an application. By analyzing the telemetry data generated by the application, developers can gain insight into how it’s functioning and identify areas where performance could be improved. This can help reduce downtime and boost the overall user experience.
This helps enhance the user experience because it reduces the unavailability of application systems. Thanks to automation, the timeliness and accuracy of monitoring and control will improve. At the same time, you’ll be able to reduce your monitoring overall and lower your maintenance costs.
Hopefully, this short description gives you a feel for observability and why it will be beneficial to you in the management and operation of your systems. Once you decide to implement it, you’re likely to wonder how much it will cost and how to go about it.
Observability is generally considered to be built upon three pillars:
Many processes can already create logs of their activities. In general, they’re useful in observability but in some cases need to be tweaked to increase the level of detail shown in the log.
It’s all very well having the log, but backward and forward traceability is essential to see why an event happened and its later effects.
Metrics are how we measure the unusual and, if necessary, kick off remedial actions. Simply put, you need to know the normal and detect deviations from it. Having metrics that define the normal is essential.
You can use some older tools to develop observability, but they tend to have some limitations in their applicability and reach. There are some new tools on the market that work much better: Netreo and Stackify. Logs and traces are Stackify’s strong suit, whereas Netreo’s strength lies in metrics. By using the two platforms together, users can get closer to true observability. The same company now owns Netreo and Stackify, and they are being integrated together. Once the two solutions are fully integrated, we’ll have an all-in-one observability platform.
Full integration may be a little in the future, so if what you’re really looking for is infrastructure monitoring, then you should look at Netreo right now.
To implement observability, you’ll need a toolbox that includes techniques as well as the tools themselves. And you’ll need to cover all three pillars of observability: logging, tracing, and metrics.
The tools allow managers, monitors, and developers to collect and analyze data from various sources, including application code, infrastructure, and user behavior. By using these tools in combination, systems managers can gain a comprehensive view of an overall and individual system’s behavior and performance, which can help them identify and resolve issues more accurately and quickly.
To implement observability, you will need to take the following steps:
The first step is to identify and implement the tools that allow you to measure the performance of your overall and individual systems. The tools will need to cover logging, metrics, and tracing. This allows you to capture data about your system’s behavior and performance.
Linking your network management and control systems improves observability. For example, identification and notification of deviations from normal traffic patterns will allow you to detect a potential malicious attack on your network or internal systems earlier.
Once you have instrumentation in place, you need to collect the data that your system is generating. Tools such as logging frameworks, metric collection systems, and tracing libraries collect the data.
You need to look at the data provided by each measurement tool and see what you need to store and what you can safely ignore or discard.
The next step is to define how to store the collected data. You may want to store data in a centralized location such as a database or a data lake. This allows you to query and analyze the data later.
Cloud storage is useful in this regard. Many businesses use a triage system, with new data available immediately and historical data for a set period still held online but in an online storage vault. Automated retrieval systems can help you access older data held offline.
Regular backup of data is part of normal operational routines. The event horizons defining the breakpoints between immediate, online, and offline storage will vary according to business requirements.
You can begin analyzing the data you’ve collected to gain insights into the behavior and performance of your system. This can involve using tools such as dashboards, alerting systems, and machine learning models.
You can analyze data immediately to identify and manage changes in usage, perhaps to observe the effects of a marketing campaign on an e-commerce application. And you can analyze historical trends over time. For example, the peak period for buying carpets in the Northern Hemisphere is in the autumn, usually around early October. A historical analysis will highlight similar patterns in your business.
Visualization is where the rubber meets the road. You can present the data in various forms, such as charts and graphs. That can help you identify trends and patterns in your system’s behavior. Plenty of visualization tools, even Microsoft Excel, are available to help you with this process.
Overall, implementing observability involves a combination of tools, processes, and best practices that allow you to gain insights into the behavior and performance of your system at both overall and granular levels. This helps corporate and departmental decision makers identify and resolve issues more quickly.
In summary, observability is a powerful concept that can help developers gain deeper insights into the behavior and performance of their applications. By collecting and analyzing telemetry data, developers can quickly identify and troubleshoot issues, which can improve the overall user experience and reduce downtime.
This post was written by Iain Robertson. Iain operates as a freelance IT specialist through his own company, after leaving formal employment in 1997. He provides onsite and remote global interim, contract and temporary support as a senior executive in general and ICT management. He usually operates as an ICT project manager or ICT leader in the Tertiary Education sector. Also, he has recently semi-retired as an ICT Director and part-time ICT lecturer in an Ethiopian University.
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