Feedback loops are key to what we do as developers. They inform us and allow us to learn from our users’ past and present behaviors. This means we can work toward proactive development based on our reactive past.
Proactive Programming vs. Reactive Programming
Proactive programming pushes the concept of isolating variables that might be easy to mitigate. The idea is to eliminate problems before they become problems. That said, we are not talking about some weird psychic ability to determine what needs to be done before it happens. If anyone could do that, none of us would need jobs. This is about using tools like APMs and testing suites, alongside real user monitoring and direct user quality assurance to make things predictable.
Reactive programming is more commonly practiced. This is what we see with most modern, non-compiled programming languages, like Ruby, PHP, Python, and the various JavaScript variants. In reactive programming, the data we wish to manipulate or interact with controls the flow of the program. Quality feedback loops are important in both cases.
Some of the modern tools people use include monitoring, continuous integration, and continuous delivery. QA tools continue to grow as well alongside techniques like chaos engineering and a focus on DevOps as a practice.
Tooling and Observability
Observability is a key feature in proactive programming. This is the direct feedback from your application, infrastructure, and all other systems. This allows you to see how everything is functioning across sandbox, testing, and production environments.
With BugSnag, for example, users receive the insights they need to make application and performance improvements. All of this helps complete the loop.
Monitoring methods and tools are now more sophisticated and more widely adopted to handle increasingly distributed and complex systems. While it is now easier to observe code and tests, and extrapolate that behavior, all bets are off once your code hits the real world.
Redefining for Modern Needs
For the modern tech world, we’ve seen the need to redefine observability. Observability is the ability for teams to view information and investigate how a given system performs in real-time. This leaves room for adjustments to create better systems and identify threats in any complex computing environment.
The real-time insights observability offers are critical when it comes to application development and monitoring. Most tools focus on digesting information and kicking it back after a certain period. This may not be ideal for many teams, especially those deploying multiple times per week, or even per day. Whether a sandbox environment or a production cluster, systems need to be monitored in concise intervals, near instantaneously.
And we can’t see the full picture without making all aspects of what we try to observe readily available. Modern infrastructures generate large amounts of unstructured data, but often only sample small parts, due to hardware constraints or high licensing fees. Slow query speeds and long latency between ingest-to-search makes the data not available “fast” enough for quick analysis. Complex solutions that are difficult to use, query, deploy, or manage lead to limited usage and reduced satisfaction.
This results in outdated observability for just parts of a system. Removing these obstacles lets users and teams quickly query, analyze, and visualize all their data instantly. With the help of open-source solutions like OpenTelemetry, we can mitigate some of the time and toil needed to get enriching feedback.
Decisions Driven by Data
Data-driven observability means you leverage your log data and use real-time streaming capabilities for querying and dashboards. This powers live system visibility for all engineers, not just operations or ops-minded DevOps folks.
The goal of observability is to make systems better. The more we know, the more we can improve and adjust. It’s commonly said software development is never “finished.” Being able to observe and monitor what happens out in the world allows our teams to build better, smarter, and get closer to the goals we keep pushing forward.
Live system observability is about this data-driven, iterative process for teams that improves the overall health and resiliency of systems.
Conclusion
Mitigating the issue of live system observability is at the top of the heap for every modern company or organization developing an application whether it is web or mobile, fintech or funtech.
Successful tools must provide real-time system insights to developers, DevOps practitioners, security operations, system administrators, and others. And they must be built to scale linearly and efficiently store data, so users do not waste their compute resources.