Thundra sponsored this post.
Observability is a one-stop solution and a key component of DevOps teams. It measures how well you can infer a system’s internal states from its external outputs. It is a continuous process that begins with your CI/CD pipelines and continues throughout an application’s lifetime.
An observable CI/CD pipeline enables you to proactively monitor for problems and track errors that occur during a CI/CD build. A fully observable CI/CD pipeline eventually allows for faster fixes and increases code confidence. Without visibility into the pipeline, tracing issues back to their root causes becomes extremely difficult, if not impossible.
In this post, we’ll discuss four techniques that improve observability.
1. Production Observability
Some errors only occur after an application is deployed to production, making them hard to replicate locally. Some occur only intermittently.
Traditional testing and monitoring fall short when it comes to these issues, as they were mainly built to inspect and monitor known errors or issues. If you build for failure and ensure your production systems are observable, however, you’ll get ahead of problems before they cause costly downtime.
Applications are dependent on a number of critical components, such as storage, queues and so on. Production observability allows you to ensure continuous uptime of your app as well as the critical components your app depends on.
There are two crucial parts of production observability — alerting and passive monitoring.
A monitoring system detects important system events and sends an alert to the responsible party. Most alerting systems are configurable, allowing you to send an alert whenever an application behaves in a way that exceeds a predefined threshold.
Alerts can be sent via SMS, email or even a Slack message — so developers and stakeholders both know when something needs to be fixed. Such an alert system assures developers that they’ll be notified if an application does not behave as expected, so they can focus on other tasks.
Passive monitoring is critical for gaining a comprehensive understanding of application efficiency and consumer use habits, allowing software teams to directly track the quality of the user experience using real data.
A passive monitor does not inject test data into the network in order to mimic user behavior. Instead, it collects actual user data from individual network locations. In most cases, an agent monitors the flow of data and gathers statistics based on usage patterns.
2. Optimizing Logs
When done correctly, logging improves application state monitoring and contains information about events that occur within a software system. When troubleshooting software, logs provide insight into what, when and why the system became faulty in the first place.
Without a doubt, log data provides DevOps teams with a higher level of visibility into the application or system being monitored. This allows them to identify the changes that resulted in error reporting and how frequently a specific issue occurs within an application.
However, if not optimized and centralized, log data can bloat until it becomes difficult to work with, especially in distributed architectures. When you collect extraneous and unstructured data, the challenges associated with log analysis increase, as do the time and cost associated with logging more data than you need.
A good logging practice prioritizes logging only the metrics that are critical for application performance and ensures that log messages are structured, descriptive and contain helpful information. This information should include:
- Unique user IDs
- Session IDs
- Resource usage information
Logs should also be managed in a centralized, accessible location. That way, you can easily correlate different logs, tie them to a particular session or user, troubleshoot faster and understand what’s happening across an entire infrastructure.
3. DevOps Culture
It’s not enough to gather logs or monitor production applications. To achieve a reasonable level of observability across cross-functional teams, you also need to align people and processes around shared goals. Intangible as it may seem, organizational culture is critical. An organization may not be able to implement a strategic initiative if its employees don’t support the idea. As such, a DevOps cultural transformation can be a strategy for building highly observable applications.
The easiest way to create a DevOps environment is to combine the operations and development teams so they have to communicate and collaborate more. To truly achieve an observability-driven DevOps culture, you’ll need to:
- Foster a collaborative environment
- Impose end-to-end responsibility
- Encourage continuous improvements
- Focus on customer’s needs
- Embrace failure and learn from it
- Automate almost everything
From the beginning of software development to the end, software teams should write debuggable code and own its entire lifecycle. That code should also be wrapped with proper KPIs, metrics and logging. This improves the application’s overall observability and gives the operations team more data with which to detect failures and predict ones that might occur in the future.
Achieving observability isn’t the job of software engineers and developers alone. It’s the collective responsibility of a cross-functional team. The team who builds it should be responsible for running it and ensuring it continues to run.
An observability-driven DevOps culture transforms the way organizations think about their development process and injects an operational mindset into their daily practices. Eventually, this increases the performance and availability of cloud applications while improving teams’ productivity and satisfaction — in turn also streamlining work processes and collaboration.
4. Preproduction Observability
There’s a lot of focus on achieving observability in production systems, but less emphasis is placed on making applications observable right from the development phase. Yet a successful deployment includes an organized preproduction environment.
Preproduction observability plays a big role in many day-to-day activities, including deciding what to build or how to ship new features, write application codes, optimize critical code and plan architectural changes to an application. Preproduction observability allows DevOps teams to proactively fix application issues that could go wrong when their code goes into production.
Every developer hopes their hard work is deployed to production, a dream that requires functional code with few bugs to realize. Observing application predeployment doesn’t automatically make a software perfect, but it minimizes the number of errors that eventually find their way to production, and it gives developers confidence that their code is working well.
Remote debugging tools like Thundra offer another sense of security. These tools allow developers to debug an application running outside their local environment without interfering with the app’s normal operation, sifting through massive log files, or replicating a production environment locally. With remote debugging, developers can use non-breaking breakpoints to effortlessly debug errors in any environment, including cloud native development and staging environments, Lambda, Kubernetes, on-premise monoliths and a wide range of deployments and technologies.
When done correctly, remote debugging can save a lot of money, headache and time for the development team, and it’s particularly useful for organizations that rely on cloud platforms, services and infrastructure.
While all four of these best practices are beneficial, preproduction observability is the most economical approach to improving observability. It allows software developers to detect and fix issues in their code while the cost of remediation is minimal and it won’t affect users.
Production observability is relevant, but it’s costly and won’t always save you. Anything can happen in a production environment, and many unpredictable factors can break your application or make it unavailable. Application logging is also important, but logs can be expensive to manage and difficult to analyze — especially when trying to trace production issues to their root causes in a highly distributed system. Finally, DevOps culture should be embraced by any enterprise that wants to achieve full observability, but this process takes time and the buy-in of your entire organization.
With remote debugging tools, software teams can increase development velocity by saving the time they would have spent reproducing production issues locally.
When it comes to solving problems in production systems, the best approach is to avoid the problem in the first place. Improve your observability and see errors at a glance. Get started with Thundra Sidekick.
Featured image via Pixabay.