

Top 10 Modern Observability Best Practices
In the realm of modern software development practices, observability is no longer an optional add-on. It is a mission-critical capability. Like how control theory revolutionized industrial systems, and quality assurance redefined manufacturing processes, observability transforms the software systems and their development processes in many ways inspired by the brick-and-mortar industries.
This post explores the best practices in modern observability to help you leverage its full potential. Whether you are debugging code, optimizing performance, or improving the user experience, these practices will guide you toward building an efficient and reliable enterprise software product and ensure its long-term deployment viability.
Observability Practices Over the Years
Observability follows the concepts in the control theory to manage software system deployment. At a basic level, this starts with monitoring key metrics like CPU usage, memory, and network traffic. With the increasing complexity of software architectures over the years and distributed, modular deployment over multiple cloud environments, traditional monitoring approaches are grossly insufficient for diagnosing production issues.
Observability goes a few notches ahead of monitoring and captures deeper metrics and issues that are unknown during the software development phases. Observability has also been augmented in the last few years by several process-oriented and technological innovations. This includes DevOps, platform engineering, containerization, and the advent of artificial intelligence.
Thanks to these innovations, observability has evolved as a robust practice within software engineering. Today, its role spans the entire spectrum of depth and breadth around the technology stack and development process. It empowers developers, DevOps, and SRE(Site Reliability Engineering) teams to foresee issues in a software system faster. Considering its impact spanning the entire stack and across the development phases, let’s unravel the best practices of modern observability covering both these aspects.
Observability Best Practices Cutting Across Software Application Stack
1 – Adoption of Developer Observability
Observability gained popularity in the middle of the 2010s due to Google’s focus on site reliability engineering practices to provide deeper insights into cloud computing infrastructure. It was due to concerns about the stability of servers for hosting dynamic and scalable workloads. Over the years, the scope has moved up from the infrastructure layer.
Modern observability practices mandate full-stack observability, starting with the bottommost infrastructure level and going up to the top-level application layer. This is where developer observability comes into play. It helps software development teams capture dynamic telemetry data from the application runtime and pinpoint source code-level issues.
2 – Performing Comprehensive Logging
Logs have always been crucial to software debugging and analysis. With observability, logging takes a more refined approach. It captures granular events and the sequence of operations within an application and stores the information in a structured format. This method improves readability and searchability across distributed environments.
Additionally, modern observability practices benefit from conditional logging. This approach allows developers to add dynamic logs in live applications, providing real-time visibility into application behavior and filtering out the noise from unrelated logs.
3 – Leveraging GenAI for Autonomous Debugging and Remediation
Only 1% of software debugging time is spent fixing a bug. The remaining 99% is spent on RCA (Root Cause Analysis), figuring out where and why the bug exists. Observability plays a vital role in providing valuable data for RCA. With the evolution of AI-driven tools, these tedious RCA processes leap ahead of traditional debugging struggles.
Autonomous debugging is a hallmark of modern observability. It leverages machine learning to pinpoint errors in real-time, reducing the time and manual interventions involved in performing RCA. By gathering historical and real-time data, this technique can provide proactive recommendations and even enable auto-remediation for recurrent issues.
4 – Cost Effective Data Collection
One of the biggest bottlenecks in achieving effective observability is the tradeoff between storage costs for telemetry data and the insights within the captured data. More data leads to more storage, which, after some point, becomes unjustified for a high storage cost. At the same time, less storage budget risks missing critical insights in the captured data.
Therefore, it is recommended to use dynamic tracing tools to enable on-demand, real-time, and context-aware data collection that is specific to the application flow and can be erased after troubleshooting is complete. This way, the cost of data storage and the need for high-fidelity data can be balanced, ensuring that observability remains effective and economical.
5 – Real-time Health Monitoring
Dynamic instrumentation also assists observability in capturing specific metrics within the running application. By leveraging all the above best practices, it is possible to instrument the source code at critical points to gain deep insights into an application’s overall health and performance.
For observability to make a valuable impact, this technique must be implemented across the technology stack by setting up alerting and automated responses for anomalies that continuously monitor business logic and application performance-related metrics to enable real-time and holistic health monitoring for the entire application.
Observability Best Practices Covering Software Development Processes
1 – Shift Left Observability
The “shift left” movement in software development empowered developers to move testing phases earlier in the development cycle, allowing them to identify and fix bugs earlier. Along similar lines, shifting observability left also brings productivity and efficiency gains.
Shift-left observability enables developers to monitor and troubleshoot issues during development phases, helping them resolve issues early, avoid costly fixes in production, and improve MTTR. The best strategy for exercising shift-left observability is to empower developers within their native working environments, such as IDE and CI/CD automation tools.
2 – Tracing for End-to-end Visibility
Traces follow a request’s journey across services within the entire stack. They also help track downstream events that may cause slowdowns or bottlenecks in the deployment environment.
End-to-end visibility empowers development and DevOps teams with linked traces across the length and breadth of the deployment to expedite the RCA for production-level issues. Such traces can also be added dynamically for targeted debugging without affecting system performance or causing storage overhead.
3 – Enhanced Platform Engineering and DevOps
Platform engineering benefits software development in many ways. Indirectly, it also has a positive impact on observability. By centralizing and managing observability infrastructure and tooling, platform engineering teams create a more accessible and standardized approach to observability for developers. It also augurs well with best practices such as shift-left observability, autonomous debugging, and efficient data collection while ensuring that observability tools integrate seamlessly across development pipelines.
Together, these practices foster an observability driven deployment environment that aligns with the day-to-day development tasks and DevOps challenges, leading to enhanced productivity and faster troubleshooting.
4 – Autonomous Observability
Observability is like a data firehose for MELT (Metrics, Events, Logs, Traces) data. With so much data available, AI and machine learning have become inevitable in detecting patterns, offering predictive insights and automated root-cause analysis.
This strategy helps bridge gaps between logs, metrics, and traces, automatically identifying anomalies that might otherwise be missed. Therefore, AI-based observability is a must.
5 – Customized Observability for Mixed Workloads
Many enterprise software environments often mix legacy systems with modern architectural patterns such as microservices and containerized workloads, which makes tracking issues complex. Instead of adopting a one-size-fits-all observability approach, modern practices emphasize the need for customizable observability frameworks.
This is achieved using specialized tools like sidecar services and log forwarders to extract telemetry data from legacy components. Further, the captured data must be run through a data transformation pipeline to normalize and structurize the legacy logs before ingesting them into the observability platform. This way, the observability data collection is tailored to focus on specific workflows involving the legacy components, reducing unnecessary noise and enabling more precise insights.
Observability Done Right
Modern observability practices, inspired by control theory, emphasize continuous monitoring and feedback to maintain software stability. The monitoring happens at a deeper level to query the system’s internal state through logs, traces, and metrics, while feedback mechanisms include alerting, anomaly detection, and auto-tuning.
Given the complexity of software architectures and the diverse nature of today’s deployments, a hybrid strategy for observability is the most effective approach. This strategy integrates multiple observability platforms to enhance developer observability while utilizing application performance management tools that monitor the lower layers of the technology stack.
Lighrun is the only developer observability platform that offers real-time, dynamic instrumentation and AI-enabled autonomous debugging and remediation within the IDE. It also supports integrations with some of the most popular APM tools. This combination allows observability data from APM platforms to be merged with real-time debugging from Lightrun, ensuring a holistic approach to monitoring, debugging, and performance optimization. This hybrid strategy empowers teams to troubleshoot more efficiently, address issues at all levels of the stack, from source code to compute infrastructure, and ultimately improve system reliability and user satisfaction.
It’s Really not that Complicated.
You can actually understand what’s going on inside your live applications.