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Data Analytics, DevOps and SRE

What Is Observability in Software Engineering?

By Jennifer Marsh | July 7, 2021

In large-enterprise environments, engineers use observability to identify software behavior, ensure execution as designed, and detect any anomalies. Observability is often referred to as how logs provide output about infrastructure so that administrators and software engineers can monitor a system. It also gives insights to software engineers about the way users interact with their applications and provides information for future innovation and scalability.


What Is Application Observability?


The concept of observability first originated in control systems engineering, and only recently has it been applied to cloud computing and DevOps. In its simplest form, application observability is a measurement of the internal state of a complex system based on external output.

Full-stack observability refers to the strategies and tools used to aggregate performance data from cloud computing applications. A "stack" in this context is the technology used to run a system. For example, the LAMP stack stands for "Linux, Apache, MySQL, and PHP" technologies. These technologies together could run your web applications. Facilitating observability of cloud stacks, you can use logs and analytics tools to find the information necessary for determining if the application is running as intended, its performance is sufficient, and any anomalies are detected and reported. 


Key Differences Between Observability and Monitoring


While observability and monitoring are different phenomena in performance management and analysis, they do share some relation. Namely, monitoring is born out of observability because a machine or an instrument cannot be monitored if it isn't observable. And herein also lies some of their most significant differences.

When comparing and contrasting monitoring vs. observability, one of the main differences lies in the "how" and the "why" of app performance. That's because monitoring gives engineers a much more general view of their system's health and alerts developers to internal anomalies only after they've occurred. Full stack observability allows for continuous telemetry, giving engineers contextual insight that will enable them to answer the "why" behind an inconsistency.

Additionally, another notable difference between observability and monitoring is that monitoring is built around existing questions used to interrogate performance metrics. In contrast, observability allows developers to impart new questions onto their systems to understand response status and debug issues. 


Why Is Full Stack Observability Important in DevOps?


The use of observability in telemetry is not a new concept. It's key to understanding the internal states of complex instruments. So it's no surprise that observability has found its way into cloud computing and DevOps, which often rely on continuous cycles to keep up with the on-demand world we inhabit today.

This immediacy of modern software development has given rise to a need for closeness in performance management. Full-stack observability allows developers to test requests millisecond by millisecond, allowing for more precise telemetry and a quicker resolution of existing bugs.

DevOps teams use automation to scale code deployments, and observability gives insight into deployment success. Observability can determine scaling requirements, deployment changes to make, and any errors causing testing issues. It helps developers and operations better understand the way DevOps tools execute and provides metrics on the performance of deployment applications.

This dynamic operations model also creates greater demand for agile performance management and diversifies the types of questions developers ask throughout the debugging process. When considering the debate between monitoring vs. observability, asking better questions makes observability conducive to a performance analysis model. 


Observability Strategies for Continuous Performance Visualization


Building an observable system isn't complex, but it is beneficial. To create better full stack observability in DevOps, engineers can turn to these four pillars of performance metrics to establish KPIs and create an observable system that will help them uncover the "why" behind any anomalies in their applications operations. 


Using Metrics

The metrics displayed in observability tools can help developers with innovation in future product releases. Observability tools collect data from different endpoints and applications so that developers can spot trends over a specified duration. For example, developers could find trends in application performance and usage spikes for a month to determine the organization's busiest time of the day.  


Using Traces

Traces are necessary for tracking known issues. For example, developers can use traces to determine the cause of a specific error. These errors could be traced to particular code issues, infrastructure failure, or insufficient resources and scaling. 


Using Dependencies

Modern software uses numerous dependencies and libraries that perform application functions. External developers could manage these dependencies, so they must be monitored for changes and errors. Observability tools map dependencies to application architecture to track for updates, vulnerabilities, and code audits.


Using Logs

Logs play a significant role in full stack observability and precise debugging because they compile real-time insights that allow developers to quickly play back potential bugs and troubleshoot issues. 

Logs are essential in monitoring and observability in the cloud and on-premise environments. They contain all data collected from various locations within the environment for analytics, visualization tools, and other reporting applications.


Building an Observable System with Stackdriver Logging


Google Cloud's answer to the growing need for observable systems in DevOps Stackdriver Logging is a performance management tool that leverages logging and tracing to visualize performance issues more seamlessly and with greater context.

What makes Stackdriver Logging beneficial is that it takes the logged data collected across your cloud environment and turns it into visualizations like graphs and charts that give you easily recognizable insights. You also have trace abilities to identify anomalies and speed up bug fixing, should your application have errors.

Because Google Cloud Platform was made for analytics, Stackdriver Logging and Stackdriver Trace are complete observability tools that help developers and operations monitor cloud applications. They use advanced BigQuery techniques to pull data from logs that could have billions of line items for large platforms. These observability tools are designed to help you monitor cloud applications for performance and reliability if you use Google Cloud Platform.


Extra Credit

  1. https://thenewstack.io/monitoring-vs-observability-whats-the-difference/

  2. https://www.ibm.com/cloud/learn/observability

  3. https://www.sumologic.com/glossary/observability/

  4. https://newrelic.com/blog/best-practices/what-is-modern-observability 


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