Pros and Cons of Cloud Native to Consider Before Adoption
This is the second of a four-part series. Read Part 1.
Cloud native adoption isn’t something that can be done with a lift-shift migration. There’s much to learn and consider before taking the leap to ensure the cloud native environment can help with business and technical needs. For those who are early in their modernization journeys, this can mean learning the various cloud native terms, benefits, pitfalls and about how cloud native observability is essential to success.
To help, we’ve created a four-part primer around “getting started with cloud native.” These articles are designed to educate and help outline the what and why of cloud native architecture.
The previous article included a definition of cloud native, its connection to DevOps methodology and architectural elements. This article will cover the pros and cons of cloud native adoption and implementation.
Innovation Brings Complexity
A cloud native architecture speeds up application development since a large application can be broken into parts, and every part can be developed in parallel. That brings many benefits. But the complexity of cloud native apps makes it hard to see the relationship between various elements. That makes it harder to maintain performance, security and accuracy or diagnose problems in these areas when they arise.
So, let’s look at both the benefits and challenges of using a cloud native architecture.
Empowering the Modern Business
Applications built using a cloud native architecture offer a faster time to market, more scalable, efficient development and improved reliability. Let’s look at the advantages in greater detail.
Faster Time to Market
A cloud native approach to developing applications speeds development times. The component nature of cloud native apps allows development to be distributed to multiple teams. And the work of these teams can be done independently. Each service owner can work on their component of the app simultaneously. One group is not dependent on another group finishing its part of the app before they can start on their own.
Additionally, cloud native apps allow components to be reused. So rather than creating a new frontend for every new app or a new “buy” capability, existing ones can be used on a new app. Reusing various elements greatly reduces the total amount of code that must be created for each new application.
Change one thing in the code for a monolithic structure, and it affects everything across the board. Microservices are independently deployed and don’t affect other services.
Efficiency
As noted, a cloud native approach lets smaller development teams work in parallel on a larger application. The idea is that a smaller team spends less time managing timetables, in meetings and keeping people up to date, and more time doing what needs to be done.
In such a work environment, these small teams access common company resources. That allows each team to benefit from cultural knowledge acquired over time throughout the organization. And naturally, the teams can work together, benefiting from each other’s best practices.
Scalability and Agility
In a cloud native environment, an organization can readily scale different functional areas of an application as needed. Specifically, running elements of a cloud native application on public clouds builds the capability to dynamically adjust compute, storage and other resources to match usage.
Adjustments can be to accommodate long-term trends or short-term changes. For instance, a retailer having a seasonal sale can increase the capacity of its shopping cart and search services to accommodate the surge in orders. Similarly, a financial institution seeing an increase in fraudulent activity may scale up machine learning fraud detection services.
If you run everything through one monolithic application, it’s hard to manage the massive scale of services and respond to changing market conditions as an application grows.
Reliability and Resiliency
Because cloud native systems are based on loosely coupled, interchangeable components, they are less vulnerable to a larger set of failures compared to the classical monolithic application. If one microservice fails, it rarely causes an application-wide outage, although it could degrade performance or functionality. Similarly, containers are designed to be ephemeral, and the failure of one node will have little to no impact on the operations of the cluster. In short, in cloud native environments, the “blast radius” is much smaller when a component fails. When something fails, a smaller set of services or functions may be affected, rather than the entire application.
Cloud Native Also Comes with Challenges
Competitive benefits notwithstanding, cloud native adoption comes with its own set of challenges. None are insurmountable thanks to modern tooling, but understanding what you’re getting into with microservices and containers will set you up for success on your cloud native journey.
Complexity Can Impede Engineer Productivity
The inherent design of microservices leads to significant complexity. Imagine a microservices architecture featuring thousands of interdependent services — it becomes much more difficult and time-consuming to isolate issues. Even visualizing these services and their connections is challenging, let alone wrapping your head around it. When microservices are so independent of each other, it’s not always easy to manage compatibility and other effects of different versions and workloads.
The infrastructure layer is not any simpler. Kubernetes is notoriously challenging to operate, in part because the ephemeral nature of containers means some may only live for a few seconds or minutes. There are many moving parts in a container orchestration system that all must be configured and maintained correctly.
All told, cloud native complexity places a new burden on engineers who are responsible for performance and reliability.
Unprecedented Observability Data Volume
With cloud native agility comes an explosion of observability data (metrics, logs, traces, events) that can slow down teams while they’re trying to solve customer-facing problems. Cloud native environments, especially as you start scaling them, emit massive amounts of observability data — somewhere between 10 and 100 times more than traditional VM-based environments. Each container emits the same volume of telemetry data as a VM, and scaling containers into the thousands and collecting more and more complex data (higher data cardinality) results in data volume becoming unmanageable.
