Observability Engineering: A Beginner's Guide

Traditional monitoring methods become inefficient as organizations shift from legacy software systems to complex cloud-native architectures. This transition renders these methods less effective, as they no longer provide the critical insights needed. In response, observability engineering has emerged as an important discipline, offering a more comprehensive understanding of modern software systems.

This article will take you through the definition, importance, and processes of observability engineering. Observability engineering helps speed up incident resolution. Furthermore, it provides many other benefits despite the challenges in implementing and maintaining observability systems.

What is Observability Engineering?

Observability engineering is the process of building and maintaining highly observable software systems. It helps understand the system state at any time. An observable system has the following key characteristics.

• Knows the intricate details of the application.
• Using external tools for observation and questioning to know the inner workings and system state.
• The ability to get information about every possible application state, including unfamiliar and unpredictable ones.

The term “observability” was introduced by Rudolf E. Kálmán to describe mathematical control systems in 1960. Observability in control theory refers to the ability to understand the internal states of a system based on what can be observed from its external outputs.

Numerous observability tools have been developed today. They include AI-driven tools that automate the root cause analysis and continuously improve the processes.

The Importance of Observability Engineering

Traditional IT monitoring practices could handle debugging for simple legacy systems. However, with the development of current modern software systems with complex infrastructure and architecture, debugging their issues has also become complex. Thus, observability has become important for spotting unusual patterns and behaviors. It also enables gaining insights into user interactions with complex modern systems. For instance, observability helps understand the dynamics of microservices, containers, and pods in cloud-native environments like Kubernetes.

Nowadays, observability greatly impacts the entire software development lifecycle and managing software at scale. It helps continuously improve the system by providing insights into its behavior, making it more reliable and efficient over time. Analyzing observability data allows engineers to identify performance bottlenecks and improve the system's efficiency.

Key Components & Tools in Observability Engineering

An observable system uses several practices to provide an idea of the internal workings of the application state at any time. The following key processes involve modern observability engineering practices and related observability tools.

Realtime monitoring & alerting

It is essential to set up monitoring systems with tools like Splunk Infrastructure Monitoring. This type of tool can continuously monitor and collect system metrics, including resource utilization, error rates, synthetic journeys, and performance metrics. Alerting systems are leveraged to alert engineers when there are deviations from normal patterns.

Dashboards

Visual representations of data collected from various monitoring tools, logs, and traces help understand the system performance and spot any issues quickly.

Structured events

Anything interesting and important within the system is emitted as events. These events comprise details such as a unique ID, headers, variables, and execution timestamp which is helpful for debugging.

Application performance monitoring (APM)

Tools like Splunk Application Performance Management provide a comprehensive view of application performance, including application dependencies and user experience.

Distributed tracing

Used in complex microservices architectures where a single request interacts with multiple services across different machines or data centers. Traces have unique identifiers, and applications are instrumented to emit tracing data.

Logging

Logging is another fundamental part of observability. It includes logging messages, creating repositories, and determining the log levels. Observability engineering uses log management tools like Splunk Cloud Platform and Splunk Enterprise.

Telemetry instrumentations

Applications are instrumented to send event data to a central location using Open Telemetry Standards. That data is helpful for tracking user journeys and troubleshooting any errors in them.

SRE & DevOps integration

Observability is integrated into DevOps and Site Reliability Engineering (SRE) practices, providing the necessary data to practice them effectively. Examples include techniques like feature flagging, incident analysis, blue-green deployment, and chaos engineering. Thus, observability engineering involves improving the system's automation, continuous delivery, and reliability.

Traditional Monitoring vs. Observability Engineering

Focus

Traditional monitoring focuses on systems checking the system health and performance using a set of pre-defined performance metrics. Thus, monitoring involves addressing familiar questions and verifying the condition of established variables.

In contrast, observability engineering goes beyond establishing procedures to identify the internal state of the system from external outputs. Thus, it provides insights into the unknown variables and focuses on questions that will arise without prior knowledge.

Approach

Alerts will be triggered if they cross the thresholds of pre-defined metrics. Thus, monitoring takes a reactive approach as it allows organizations to identify issues and apply remediations once they have occurred.

Observability lets engineers understand the internal behavior and potential issues before they occur. Therefore, it takes a proactive approach compared to monitoring. Additionally, alerts will be generated if any issues occur, along with details to understand the reason behind them.

Debugging methods

Traditional monitoring uses metrics and dashboards, depending heavily on the experience and deep knowledge of the senior staff for debugging the issue. As a result, this method introduces some biases and addresses the symptoms rather than the actual root cause. In the past, where limited data were collected from simple legacy systems, this dependency on human expertise was a standard practice. However, this approach became highly unreliable as the complexity and scale of the systems grew.

On the other hand, Observability provides information to debug issues in detail, allowing engineers to ask open questions and systematically trace system data to find the real cause of problems. Therefore, organizations do not have to rely on prior expert knowledge and subjective guesses, leading to more objective analysis. Thus, observability engineering improves confidence in debugging and finding the root cause of issues. Furthermore, it allows us to identify deeply hidden problems.

Scope

The scope of monitoring is limited to observability since traditional monitoring focuses more on application performance monitoring. Thus, it is not possible to capture complex interactions in distributed systems.

Observability engineering empowers systems with tools to retrieve detailed information on interactions between different components of complex systems. It is especially useful in microservices as it enables tracking interactions between components.

Data Volume

Since traditional monitoring focuses on a pre-defined set of anticipated issues, engineers must focus only on limited scenarios. It limits the collected volume and generated information.

Conversely, observability engineering allows the collection of a wide range of data, such as metrics, events, logs, traces, and telemetry data, providing a comprehensive data collection. Thus, observability provides a holistic approach to finding unforeseen issues.

Values of Observability Engineering

Leveraging observability engineering practices provides numerous benefits for organizations delivering complex cloud-native applications and systems.

• Faster and proactive incident resolution. Observability tools provide the required information in detail to troubleshoot issues to quickly resolve and minimize downtime. Observability allows teams to proactively identify and solve potential issues before they affect users rather than merely reacting to problems as they arise.
• Improve the system understanding. Deep insights provided by observability tools help organizations understand their complex interactions and behaviors.
• Improve the reliability of the system. Provides a holistic overview of the system performance and behavior through continuous monitoring and analysis. Thus, observability engineering increases the reliability of the system.
• Improve user experience. Organizations that leverage observability can fix issues faster and identify potential issues before they impact end users. Thus, observability helps provide a smoother, and more reliable user experience.
• Increasing debugging accuracy. Observability provides comprehensive data and analytics, reducing the need for human expertise. Thus, it improves the accuracy of debugging.

Challenges of Observability Engineering

Although observability engineering brings much value to organizations, there are several challenges in practicing it effectively. Organizations must consider these issues and the measures to address them.

• Challenges in data storage. Many modern software systems deal with a large volume of data, often involving billions of diverse events with thousands of dimensions. Thus, storing and retrieving such data for real-time debugging can be challenging. Therefore, it is critical to use a reliable and fault-tolerant data store.
• Challenges of network transmission of large volumes of data. Transmitting large volumes of telemetry and observability data over networks can be challenging due to bandwidth and infrastructure limitations. Thus, it is important to establish a robust network architecture for employee data sampling, compression, and optimization techniques to reduce the load.
• Cultural shift. Shifting to observability engineering practices from a reactive monitoring approach is a significant cultural shift within organizations. Thus, employees must be trained to embrace this change, providing knowledge about new tools and practices.
• Associated costs. Implementing and maintaining an observability infrastructure can be costly. Thus, organizations must assess their observability requirements and resources and choose the most cost-effective and reliable options.
• Security and privacy issues. Observability engineering requires collecting and storing detailed information about the systems and their users. It can lead to security and privacy concerns. Thus, organizations establish protocols to comply with data privacy regulations.

Conclusion

Observability engineering has become indispensable for modern and complex software production systems. It helps to provide an in-depth understanding of the system and allows for faster and more reliable troubleshooting of issues than traditional monitoring. As discussed in this article, current comprehensive observability systems comprise of several components. Leveraging observability engineering brings a lot of benefits. However, as mentioned in the final section, organizations must address the associated challenges to leverage it effectively.