How to overcome the cloud observability wall

As cloud environments become increasingly complex, legacy solutions can’t keep up with modern demands. As a result, companies run into the cloud complexity wall – also known as the cloud observability wall – as they struggle to manage modern applications and gain multicloud observability with outdated tools.

But what exactly is this “wall,” and what are the big-picture implications for your organization? Let’s explore this concept as we look at the best practices and solutions you should keep in mind to overcome the wall and keep up with today’s fast-paced and intricate cloud landscape.

What is the cloud observability wall?

Cloud applications are different from traditional monolithic applications – they are both ephemeral and dynamic. At any given time, the state of your application is undergoing rapid, automated changes in response to the environment. You may be using serverless functions like AWS Lambda, Azure Functions, or Google Cloud Functions, or a container management service, such as Kubernetes. Either way, you are spinning new resources up or down in response to the load, and a continuous delivery pipeline may be promoting a set of functions or containers to production.

These rapid changes — as well as the increasing volume and variety of data created — require a new approach to observability. Many customers try to use traditional tools to monitor and observe modern software stacks, but they struggle to deal with the dynamic and changing nature of cloud environments. As a result, they hit what the industry refers to as the cloud complexity wall – or cloud observability wall – and waste time, money, and resources trying to force their legacy toolsets to work in modern environments.

How observability works in a traditional environment

In contrast to modern software architecture, which uses distributed microservices, organizations historically structured their applications in a pattern known as “monolithic.” A monolithic software application has a few properties that are important to understand. Let’s break it down.

Centralized applications

Monolithic applications earned their name because their structure is a single running application, which often shares the same physical infrastructure. In a monolithic architecture, there is generally limited ability to evolve, upgrade, or enhance specific subsets of functionality without restarting or upgrading the entire application.

There are a few important details worth unpacking around monolithic observability as it relates to these qualities:

1. The nature of a monolithic application using a single programming language can ensure all code uses the exact same logging standards, location, and internal diagnostics. Just as the code is monolithic, so is the logging.
2. When an application runs on a single large computing element, a single operating system can monitor every aspect of the system. Modern operating systems provide capabilities to observe and report various metrics about the applications running.
3. The last aspect is the centralization of compute. As the entire application shares the same computing environment, it collects all logs in the same location, and developers can gain insight from a single storage area.

This centralization means all aspects of the system can share underlying hardware, are generally written in the same programming language, and the operating system level monitoring and diagnostic tools can help developers understand the entire state of the system.

Dynamic applications with ephemeral services

Modern cloud-native architectures leverage a completely different development paradigm compared to monolithic applications. The core of a microservice design pattern aims to make each discrete subset of system functionality into its own self-contained unit, known as a microservice. Each microservice, running as a discrete, completely self-contained, stateless application, runs inside a container or serverless function that shares no underlying operating system with any other microservice.

The components of partitioned applications generally communicate over a network call. This boundary is language agnostic, which means the service is compatible with all other microservices written in any language, so long as the network interfaces remain the same. As it relates to observability, logging practices don’t require singular technical enforcement because services don’t share code across microservice boundaries.

Another aspect of microservices is how the service itself relates to the underlying hardware. Serverless functions typically run on hyperscale clouds and so there’s no hardware to manage. Containers and container managers, such as Kubernetes, allow the hardware to be abstracted away from the application. In both cases, microservices are in a constant, ephemeral state of transition, scaling up and down in response to the environment. Because the state of the system is always in flux, there is no centralized log location that one can readily use to gain observability.

But it’s important to note, it’s not the case that traditional observability tools are bad; they’re simply not the right tools for multicloud observability.

Observability challenges of multicloud environments

Multicloud microservices-based environments bring a new set of challenges, many of which are around the velocity and volume of data generated. In many cloud-native deployments, there can be hundreds or thousands of containers and serverless functions running at any given time. With this volume of data, not only do traditional tools break down due to technical differences, but the sheer volume of data generated is also orders of magnitude greater.

Furthermore, the large data quantities generated, on top of the increasing need to sift through millions of events to uncover actionable patterns and unexpected discrepancies to optimize application performance and availability, is much more than a single person – or even a single legacy observability system – can manage to gain any insight from. As a result, organizations are looking to AI as the modern observability capability to address these challenges, using advanced AI algorithms to understand and make sense of all the discrete events in a cloud-native microservices environment is absolutely critical for your application.

Overcoming the cloud complexity wall with Dynatrace

Dynatrace provides a unique Software Intelligence Platform built for multicloud observability. Incorporating AI, automation, and front-end monitoring, the Dynatrace Platform provides complete end-to-end visibility for modern applications enabling companies to optimize application performance and reliability while accelerating innovation.

As organizations steadily replace monolithic applications with emerging cloud-native solutions, modern cloud observability tools will provide the vital backbone. They have become the requisite tools needed to help companies overcome the cloud complexity wall and accelerate application performance and innovation.