Top Disadvantages to Adopting Microservices (And Why You Should Do It Anyway)

 ● 06th May 2019

5 min read

Containers are taking over the software world, changing the way and pace in which we design, write and deliver software.

The rise of containerization didn’t happen in a vacuum. Technological development is constantly accelerating and readjusting itself to achieve the desired results faster, cheaper and better than before. Microservices is a thought model that promises to bring us closer to that goal.

By breaking up an application into specialized containers designed to perform a specific task or process, microservices enable each component to operate independently. Each service is built and maintained by a unified team and can be overwritten or replaced without affecting the entire application, reducing the necessity of scheduled outages. Transitioning from monolithic to containerized infrastructure is a fundamental change in the way we develop software. It’s expected to offer significant benefits, but there’s a big upfront investment of time and money required to make the move.

In a recent post, Derek D’Alessandro looked at the two distinct modes of change that can be adopted in order to drive technological innovation. In his own words, “[incremental change] is small and easy to predict the risks and returns, [fundamental change] introduces large changes that are harder to predict.”

Choosing to implement fundamental change, like Microservices, is not easy – mainly because of the huge upfront investment and unpredictable outcome. In many cases, innovative new practices or tooling are not being adopted because the initial effort required appears to outweigh the gain. Of course, for long-lived companies, eventually new technology must be adopted in order to keep up with the competition and industry advancements.

Microservices have helped teams become more agile and accelerate feature delivery, but they’ve also brought new challenges. As Derek lays out in his post about change adoption, new skill sets, process and tooling must be implemented to support the transition to Microservices and, ultimately, to build and run containerized applications.

What Makes Microservices Hard?

As with the adoption of any new technology, there are challenges that come with the transition from an existing workflow to a new one and there are inherent challenges that come with any new workflow we will adopt.

With Microservices, there are challenges that come with transitioning a monolithic application to containers, and there are challenges that simply exist when running and maintaining a containerized application. All teams building a containerized application will face the latter, and many teams over the next few years will face both.

Transition from Monoliths

What makes Microservices hard? It’s right in the first line of the post. Containers are changing the ways in which we design, write and deliver software. New system architectures introduce brand new skills, tools and processes that need to be learned. For teams tasked with dismantling massive enterprise applications, there may be loosely defined best practices that can be leveraged, but each transition will ultimately require unique and creative approaches to be successful.

As large monoliths are being broken down into microservices, companies are also forced to address technical debt such as poor quality or legacy code in their applications. Technical debt is a single segmentation of what can be identified as poor quality software, but it cost US companies an estimated $511 billion in 2018.

We’re Not in Monolith-land Anymore (aka More Complex Data Flow)

The flow of data across new distributed systems is much more complex, especially within systems working at mass scale. Containers isolate functions in an application, making development more flexible, but they also introduce new challenges.

With so many moving pieces, finding the root cause of an issue in containerized applications is challenging (even more than in traditional, monolithic applications). Not only does a Kubernetes (or Docker) cluster typically have more servers and services each producing their own logs, meaning that there are more logs to investigate when something goes wrong. Unfortunately, because the systems creating the logs are distributed, so are the logs.

To be able to trace and identify which service fails, tracing headers can be added to each transaction. This, of course, requires code changes and will still only reveal the container where the issue manifests, it won’t show why or how.

What Can Help Us Address Those Challenges?

Core capabilities required by Dev, Ops and QA teams to efficiently identify and resolve issues need to be expanded.

The current tools that exist, such as log aggregators and APM solutions struggle to provide the depth of context needed to maintain and troubleshoot microservice applications. Although able to identify when something goes wrong, most companies are unable to correlate issues across containers, deduplicate them and find the data required to resolve them.

Microservices require a more extensive set of tools to help us address and handle the new methods and issues that come with them. Logs and APM are not enough. Correlation of issues that span multiple containers, involving both software and hardware, is increasingly difficult. Ability to access the 7 key components of True Root Cause is more important than ever.

How OverOps Can Help

OverOps focuses on the code – enabling users to see a complete picture of their application as a holistic entity, even when deployed as hundreds or thousands of individual microservices. With OverOps, teams know when errors are introduced into the code, whether existing errors are increasing or resurface, as well as when slowdowns occur.

For all such events, OverOps provides the complete state of the code and container at the moment of event, making every error and slowdown within a containerized world automatically reproducible for the first time. Using this data, users can identify issues much earlier in the SDLC, prevent them from progressing further into production, correlate their cause to either an operational or programmatic issue, reproduce immediately and take corrective action.
Interested in seeing how OverOps supplements your current monitoring stack? Watch a live demo or get in touch!

Tali is a content manager at OverOps covering topics related to software monitoring challenges. She has a degree in theoretical mathematics, and in her free time, she enjoys drawing, practicing yoga and spending time with animals.

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