DevOps Observability from Code to Cloud
Modern software delivery practices have evolved over time. We have things like GitOps, AIOps, DataOps, etc., that enterprises employ and experiment to see which one fits well to their needs. It is not just about being agile or speedy these days; companies focus more on the stability of the infrastructure.
While the advancements in DevOps and cloud native practices are in full bloom, it is highly essential to strictly monitor and observe metrics, logs, analytics, and datasets associated with the infrastructure performance to optimize system dependability.
Things were pretty siloed in the age of waterfall software development; nobody knew what others were doing in the software development life cycle. While developers worked on building new features, the testing team and operations tested the features separately, and hence there was a communication and collaboration gap. The monitoring aspects were far beyond the development team’s control. The features were built keeping only success in mind while ignoring the chaos engineering and infrastructure dependencies since the developers less understood these.
More than just another buzzword bingo, observability helps organizations by providing them with solid indicators that let them pinpoint their infrastructure and system problems so they can begin resolving them during the most efficient phase possible. Monitoring was given less importance, and nobody knew what was happening between and within these systems. This made companies have less visibility over their own infrastructure. Hence, monitoring and observability have taken the prime spots in the cloud native space today.
Observability can be divided into three fundamental pillars:
- Logs capture a large number of immutable events of a system along with time that is used to understand the irregular behavior of the system to know what went wrong. The format of the logs should be in a structured way, such as JSON.
- Metrics form the foundation of monitoring; they are nothing but a measurement approach used to know the amount of memory used by the system, method, and the number of requests handled by a service per second.
- Traces are those minute details of a request that let you know what caused the system errors and present the bottlenecks in the system performances.
Monitoring vs. Observability
Monitoring is a subset under observability.
While monitoring allows you to track the overall health of an application with metrics such as network traffic, resource utilization, etc., Observability is the inherent system property that provides visibility and awareness of what is happening within the system. Through observability, you will be able to analyze and visualize the data collected. It helps in improving the application lifecycle management. Observability will enable the teams to see what is happening and share the solution to fix it.
Monitoring is more limited compared to observability, which can reveal why something is happening and presents detailed, actionable insights. Observability includes monitoring and extends its scope. It receives monitoring data, converts that into enriched and visualized information. In contrast, monitoring doesn’t provide you with the enhanced data and solutions to fix things. Observability is intended for deep and granular insights, context, and debugging capabilities. In comparison, monitoring is not for deep root causes and analysis.
When is observability necessary? Here are some scenarios:
- When your application is critical and performs a high volume of work, you cannot afford to miss any data produced as an output. Any company that is doing business and making money cannot afford to upset its customers; hence observability becomes vital to see in a single pane of glass about what is happening within the application.
- When your application has dynamic traffic behavior, with traffic spikes. Things happen very quickly, so you need observability in place to make things work smoothly without downtimes.
- When you have hundreds and thousands of microservices communicating with one another, it can be pretty complex at times to see what went wrong when something happens. Here also you need the observability in place to make sure the user requests are handled well without affecting the workflow.
- Observability is absolutely required when you are rolling fast updates into production via an automated CI/CD toolchain, and we need to know whether the recent update is all good or we need to roll back to the previous stage because of any issues.
DevOps Observability from Code to Cloud
DevOps has transformed itself in the last few years, completely changing from what we used to see as siloed tools connected together to highly integrated, single-pane-of-glass platforms.
Collaboration systems like JIRA, Slack, and Microsoft Teams are connected to your observability tools such as Datadog, Dynatrace, Splunk, and Elastic. Finally, IT Service management tools like PagerDuty are also connected in. Tying these high-in-class tools together on one platform, such as the JFrog Platform, yields high value to the enterprises looking for observability workflow.
The security folks also need better visibility into an enterprise’s systems, to look for vulnerabilities. A lot of this information is available in Artifactory and Amazon Web Services‘ Xray, but how do we leverage this information in other partner systems like JIRA and Datadog?
It all starts with JFrog Xray’s security impact, where we can generate the alert to Slack and robust security logs to Datadog to be analyzed by your Site Reliability Engineer. A PagerDuty incident that’s also generated from Xray can then be used to create a JIRA issue quickly.
Not every application requires observability, but it becomes crucial for critical applications to gauge performance, stability, and analysis. As a result, observability has become a necessity in the DevOps industry. It is good to see so many initiatives and organizations around this that have come forward to make sure the software development journey goes as smoothly as possible. Observability helps us know everything in a single pane of glass so we can take immediate action on the things that aren’t going well and are impacting the performance of our application.