The Rise of Developer Native Dynamic Observability
Observability has emerged as a crucial concept in modern software development, enabling teams to gain deep insights into the performance, health and behavior of complex systems. Traditional monitoring approaches, based on predefined metrics and static thresholds, have limitations in addressing the dynamic and distributed nature of modern applications. As a result, a new paradigm called dynamic observability has gained traction, offering real-time visibility into dynamic systems by combining metrics, logs and traces.
With the ability to adapt to changing environments and capture contextual information, dynamic observability enables development teams to proactively identify and resolve issues, reducing mean time to resolution (MTTR) and enhancing overall system reliability, as well as developer productivity.
Nowadays dynamic observability drives the evolution of development practices with the integration of runtime debugging into the development life cycle, encouraging teams to embrace a proactive approach to system monitoring and troubleshooting. With the insights provided by dynamic observability, development teams can optimize system performance, detect anomalies and make data-driven decisions throughout the development process.
The rise of dynamic observability represents a transformative shift in the software development landscape. By embracing dynamic observability, organizations can foster a culture of continuous improvement, enhance system reliability and deliver high-quality software products in an increasingly complex and dynamic digital ecosystem.
Observability: The Good, the Bad and the Overwhelming
Observability is the ability to understand the behavior of a system by looking at its outputs. This includes logs, traces and metrics. The main goal is to be able to diagnose and debug issues by having a clear overview of the system’s health and state. In simpler terms, it is the ability to look inside a system, understand how it works and actively debug it.
Observability = Metrics + Logs + Traces
While in the past, the mix of application performance-monitoring (APM) tools and logging tools with built-in integrated development environment (IDE) debugging tools could suffice to identify issues in applications, today’s reality is more complicated. The transition to microservices and cloud native, serverless apps, progressive delivery workflow and other advanced architectures put a new level of challenge on developers and Ops.
From a Lightrun experience perspective, the above are a representative subset of commonly seen developer challenges around troubleshooting in production environments.
For a variety of reasons, it is challenging to access a remote instance of an application or service deployed in a Kubernetes cluster or to debug a serverless Lambda function from a developer machine. To start with, the access itself from the developer machine toward a production deployment is either forbidden or so complex that it cripples developers and slows down the debugging process. Assuming that such an obstacle has been figured out, being able to obtain runtime telemetry from a production environment without stopping the app, changing its state and re-deploying it to gain these additional logs is an extremely lengthy and error-prone process.
These are only a few examples that led to the rise of dynamic observability and its shift toward being part of the software development life-cycle tool stack. Giving developers more tools and power to troubleshoot their own applications regardless of where they run and how complex they are is the new normal from a requirements perspective.
The Case for Dynamic Observability: Key Business Values
As we defined earlier, developers fall short when they need to troubleshoot remote and complex workloads from their IDEs using traditional debugging tools and APM solutions. Dynamic observability comes to address and solve these challenges.
Basically, as opposed to static logging, with dynamic observability developers enjoy end-to-end observability across app deployments and environments directly from their IDEs. This translates into reduced MTTR, enhanced developer productivity and overall cost optimization since developers debug and consume logs and telemetry data where and when they need it rather than monitoring everything.
Dynamic observability has emerged as a pivotal approach in modern software development, enabling teams to gain deep insights into system behavior and make informed decisions. It goes beyond traditional testing and monitoring methodologies, offering a comprehensive understanding of system patterns, strengths and weaknesses.
The rationale and benefits of adopting dynamic observability can be summarized into the following:
Enhancing System Understanding
Dynamic observability empowers developers to observe system states and behaviors, providing them with detailed insights. By monitoring real-time metrics, logs and traces, developers can gain a holistic view of their systems, identify potential issues and make data-driven decisions.
Complementing Test-Driven Development
While test-driven development (TDD) focuses on writing tests to ensure code correctness, dynamic observability goes further to observe and analyze the behavior of the system as a whole. It recognizes that testing alone may not capture all possible scenarios and encourages developers to embrace observability for comprehensive system understanding.
Strengthening Behavior-Driven Development
While behavior-driven development (BDD) focuses on collaboration between stakeholders to define behavior expectations, dynamic observability takes it a step further by actively observing and detecting system behavior in runtime across environments (production, staging, CI/CD, QA), uncovering patterns and potential risks that might otherwise remain hidden.
Complementing the Role of Monitoring
Dynamic observability and developer native observability do not replace monitoring; rather it complements it. Monitoring ensures system stability and performance, while dynamic observability provides a deeper level of understanding. By combining monitoring and dynamic observability, development teams can create a reliable and streamlined system, reducing vulnerabilities and risks.
Bottom Line: Dynamic Observability Is the New Normal
Dynamic observability represents a paradigm shift in software development, enabling developers to gain a detailed understanding of system behavior and make informed decisions. Using tools and practices that go beyond traditional testing, it empowers teams to create robust and reliable systems. It works in harmony with existing methodologies such as DevOps, TDD and BDD, augmenting their principles with a focus on system behavior. Through the adoption of dynamic observability, organizations can enhance their development processes and build more resilient and efficient software systems.
What to look for within a dynamic observability solution?
- Live debugging: Developers must be able to add logging statements and metrics to their code in real time, without having to redeploy or restart their applications.
- On-demand snapshots: Dynamic snapshots provide developers with virtual breakpoints without stopping the execution of your application. Having the ability to add conditions, evaluate expressions and inspect any code are key features in this category.
- Custom metrics: Right from the IDE, developers should be able to create custom metrics to track specific aspects of the system’s behavior, such as response time, method duration or error rates — on the variable level. All tasks should be completed without the need to redeploy any code.
- Integration with existing developers and enterprise tools including cloud native tools such as Prometheus, Grafana, VSCode, public cloud providers and more.
- Cost optimization: Being able to analyze observability-related inefficiency and optimize to reduce costs, such as by replacing static logs with dynamic logs, is a significant capability of such tools.
Dynamic observability empowers developers to gain a deep understanding of their live applications, significantly enhances developers’ productivity, reduces operational and infrastructure costs, and simplifies the process of debugging and fixing problems.
As a result, developers can spend more time doing creative coding by handing off complex troubleshooting to dynamic observability solutions.
