How Important Is Observability For Modern Applications

You need to have the necessary insights into an issue in order to develop a workable solution. Because unexpected faults and malfunctions frequently occur in distributed systems, observability in modern applications makes it possible to identify the root causes and develop workable solutions.

Operating a distributed system is challenging due to the complexity of the system as well as the unpredictable nature of failure mechanisms. The number of potential failure scenarios is growing as a result of rapid software delivery, continuous build deployments, and current cloud architectures. Regrettably, standard monitoring technologies are no longer able to assist us in overcoming these obstacles.

Modern Application Problems

The monolithic architecture was first used by IT behemoths to construct their apps since it was more practical at the time. They all faced the same difficulties and ultimately decided that microservices and event-driven architecture patterns were a superior option since they could be created, scaled, and deployed individually. The speed and scalability of application delivery have grown dramatically as a result, but on the downside, managing these microservice installations has added a new level of operational complexity. Working with older technology has the advantage of only having a small number of failures. It is simpler to design these complicated systems by using application programming interfaces (APIs) to expose fundamental business functions and facilitate service-to-service communication.

These four fundamental concerns are being addressed by any business or organization that is using these microservices and API-based architectures:

• Do the services and APIs offer the functionality for which they were created?
• Are the APIs and services secure?
• Do you, as a business, comprehend how people utilize APIs?
• Are the services/APIs giving the user the best performance possible?

What Is Observability?

Observability is the extent to which you can understand a complex system’s internal state or condition only based on your understanding of its external outputs. The more visible a system is, the faster and more precisely you can pinpoint a performance problem’s root cause without additional testing or coding.

The word “observability” derives from control theory, a branch of engineering that deals with automating control of a dynamic system based on input from the system, such as the flow of water through a pipe or the speed of a car across hills and valleys.

Why Do We Need Observability?

Application Performance Monitoring (APM) systems routinely sample and collect telemetry—application and system data—that is known to be associated with application performance problems. In order to notify operations and support teams of anomalous situations that need to be addressed in order to resolve or avoid difficulties, it analyzes the telemetry in relation to key performance indicators (KPIs) and compiles the results in a dashboard. Monolithic applications or conventional distributed applications, where new code is issued on a regular basis and processes and dependencies between application components, servers, and associated resources are well-known or simple to trace, may be monitored and troubleshot using APM systems.

In recent days advanced development practices and cloud-native technologies are being adopted by organizations due to the adoption of modern applications and faster time to market. Some of the examples are Docker containers, Kubernetes, serverless functions, agile development, continuous integration, continuous deployment (CI/CD), DevOps, multiple programming languages, etc. They are now releasing more services than ever as a result. APM’s once-a-minute data sampling, however, is unable to keep up with how frequently they are deploying new application components, in how many different places, in how many different languages, and for how vastly different amounts of time (for seconds or fractions of a second, in the case of serverless services).

How Does Observability Work?

Application observability solutions integrate with existing instrumentation built into application and infrastructure components and provide tools to add instrumentation to these components in order to continually identify and gather application performance telemetry. The four primary telemetry kinds, the three observability pillars of logs, metrics, traces, and dependencies are the emphasis of application observability.

• Logs –  Logs are discrete, comprehensive, timestamped, unchangeable records of application events that might be in binary, structured, or plain text forms. A high-fidelity, millisecond-by-millisecond record of every event, replete with context, may be made using logs, among other things, so that engineers can “playback” the record for troubleshooting and debugging.
• Metrics – Metrics, also known as time-series metrics, are basic indicators of the performance of an application or system over a specified time period. Examples of metrics include how much memory or CPU a program uses over the course of five minutes, as well as how much latency it experiences during periods of high usage.
• Traces – Every user request’s whole traversal from the UI or mobile app through the fully distributed architecture and back to the user is recorded via traces.
• Dependencies – Dependencies, often known as dependency maps, show how each application component depends on other apps, other components, and IT resources.

Summary

Modern application designs greatly improve scalability and resilience while streamlining the procedures for system deployment and change. DevOps teams must now more than ever achieve end-to-end observability due to the increasing complexity these systems bring.

Learn More: Application Modernization Services of Metaorange Digital