What is observability? Everything a beginner needs to know

#The term observability comes from the field of engineering and has become increasingly popular in the field of software development in recent years. Simply put, observability is the ability to understand the internal state of a system based on external outputs. IBM defines observability as:

## Generally, observability refers to the degree to which the internal state or condition of a complex system can be understood based on knowledge of its external output. The more observable the system is, the faster and more accurate the process of locating the root cause of a performance issue can be without the need for additional testing or coding.

In cloud computing, observability also refers to the software tools and practices for aggregating, correlating, and analyzing data from distributed application systems and the infrastructure that supports their operation in order to analyze Monitor, troubleshoot, and debug your application systems more effectively to achieve customer experience optimization, service level agreements (SLAs), and other business goals.

# As IT architecture becomes more complex, system management and troubleshooting also become more complex. In many scenarios, traditional approaches are no longer sufficient to ensure optimal performance. Observability is often considered a derivative of monitoring. Monitoring often involves tracking a specific set of metrics, such as CPU usage or network traffic, and raising alerts when those metrics exceed thresholds. Monitoring has certain limitations, whereas observability involves collecting and analyzing a wider range of data, providing a more comprehensive view of system behavior.

In software development, observability refers to the ability to understand application behavior and performance based on the data generated by the application, including logs, metrics, traces and other data. By analyzing this data, developers can understand how their application is performing and identify areas for improvement.

Platform security is a practical application case of observability.

Platform security teams receive large amounts of data in multiple formats from multiple sources. Analyzing messy, low-quality data slows down the ability to detect vulnerabilities, find new threats, and respond when breaches occur. In addition, with the deployment of multiple security tools, there is also the problem of being unable to share information between different security tools.

The solution is to define observability filters to identify potential security threats and improve the quality of incoming data to be analyzed. The next step is to enrich the data with supporting data from external databases to help analyze and identify security threats. Everything from DNS information to IP addresses to user identifiers can be added.

One of the major benefits of observability is that it helps developers quickly identify and troubleshoot problems with their applications. By analyzing the telemetry data generated by an application, developers can understand how it performs and identify directions in which performance can be improved. This helps reduce downtime and improves the overall user experience.

With automation, the timeliness and accuracy of monitoring and control will be improved. At the same time, it will help you reduce overall monitoring and maintenance costs.

Observability is generally considered to be built on three pillars:

Many processes can create logs of their activities. Generally they are useful for observability, but in some cases need to be adjusted to increase the level of detail displayed in the logs to be useful.

Logs are very useful, but forward and backward tracing are also necessary to see why an event occurred and its consequences.

Metrics are how we measure anomalies and trigger corrective action if necessary. Simply put, you need to know the normal state and detect deviations from the normal state. So having indicators that define normal status is a must.

Observability can also be implemented using some older tools, but they have some limitations in applicability and coverage. Achieving observability requires a toolbox of techniques and tools itself, covering the three pillars of observability: logs, traces, and metrics.

These tools allow managers, monitors, and developers to collect and analyze data from a variety of sources, including application code, infrastructure, and user behavior. By using these tools together, system administrators can gain a complete view of the behavior and performance of an entire system or a single system, which can help them identify and resolve problems more accurately and quickly.

Instrumentation

The first step is to deploy tools that measure the performance of the entire system or individual systems. These tools need to cover logs, metrics, and traces to collect data about system behavior and performance. Connecting network management and control systems improves observability.

Collect

After you install the dashboard, you need to collect the data generated by the system. Tools such as logging frameworks, metric collection systems, and tracing libraries can be used to collect data.

You need to review the data provided by each tool and determine which data is stored, safely ignored, or discarded.

Storage

Defining how to store your phone’s data is the next step. Storing data in a centralized location, such as a database or data lake, makes it easier to query or analyze the data later. Cloud storage is very useful in this regard. Many businesses use classification systems where new data is immediately available, while historical data remains in an online repository for some time. Automatic retrieval systems can access older data saved offline.

Regular backup of data is part of daily operating procedures. How you define the demarcation point between immediate, online, and offline storage will vary based on business needs.

Analysis

Next you can start analyzing the collected data to understand the behavior and performance of your system. The analysis process involves the use of tools such as dashboards, alerting systems, and machine learning models.

You can instantly analyze your data to identify and manage changes in usage, such as observing the impact of marketing campaigns on your e-commerce application. You can also analyze historical trends. For example, the peak carpet-buying season in the Northern Hemisphere is usually in the fall, around early October. Historical analysis will reveal similar patterns in the business.

Visualization

Visualization is the key point. Presenting data comes in various forms such as charts and graphs. Visualization helps identify trends and patterns in system behavior. There are many visualization tools, even Microsoft Excel can complete this process.

Overall, achieving observability requires a combination of tools, processes, and best practices that allow you to understand the behavior and performance of your system at both a holistic and granular level. This helps corporate and departmental decision-makers identify and resolve problems faster.

Finally

Observability is a powerful concept that can help developers gain insights into the behavior and performance of their applications. By collecting and analyzing telemetry data, developers can quickly identify and resolve issues, improving the overall user experience and reducing downtime.

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