The Future Landscape of Serverless Technology
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Chapter 1: Serverless Technology Today
The rise of serverless architecture continues to gain momentum, prompting questions about its future trajectory. As a cloud architect, my role has shifted from hands-on coding to strategic planning, creating 1, 3, and 5-year roadmaps that guide our cloud initiatives using current best practices.
This theoretical approach is invigorating. For years, I have immersed myself in serverless technology—experimenting, crafting proof of concepts, and curating community insights for my newsletter.
From this exploration, it is evident that we possess a solid understanding of serverless capabilities as they stand. However, as architects, we must also ponder the future: Where does serverless go from here? Has it reached its peak?
I believe the answer is no. The future promises a serverless ecosystem that differs significantly from what we experience today.
Let's delve into some potential disruptors on the horizon.
Section 1.1: Deep Infrastructure Analytics
In serverless applications that are production-ready, effective monitoring is crucial for success. Understanding event drops, identifying bottlenecks, and tracking items in dead letter queues is essential. Moreover, we must be able to trace transactions from start to finish.
This area is finally gaining traction. As more serverless workloads come online, it becomes increasingly clear that there is a significant gap to fill.
Companies like DataDog, Lumigo, and Thundra are making strides to address this challenge, but there is room for improvement.
In the future, we will require tools that not only offer what these vendors provide but also come with built-in optimization and insights akin to AWS Trusted Advisor. When we speak of application monitoring, we should expect more than mere service graphs and queue counts.
Monitoring will evolve beyond simple dashboards and alerts to provide actionable insights based on workload, including recommendations for optimizing infrastructure based on observed traffic patterns.
There is limitless potential in this realm, but our goal should be to standardize infrastructure decisions based on workload demands. Contrary to popular belief, most developers are addressing similar challenges across various domains.
To achieve this, monitoring services must develop to comprehend infrastructure deeply, recognizing traffic patterns to suggest optimizations for cost, performance, or sustainability—or all three.
Subsection 1.1.1: Infrastructure as Code
Serverless applications are increasingly constructed using a combination of business logic and Infrastructure as Code (IaC). The business logic represents the unique solutions your application provides to specific problems, while IaC defines the cloud resources necessary to execute that logic.
Advancements in tooling are making IaC more accessible. Tools such as AWS SAM and CDK simplify the complexities of CloudFormation, allowing developers to link resources more intuitively. Similarly, Terraform and Serverless Framework enable deployment across different cloud providers with a consistent IaC approach.
As our understanding of technology evolves, abstractions continue to rise, making development progressively easier.
We are currently experiencing a paradigm shift that elevates us to unprecedented levels of abstraction. Serverless Cloud is pioneering the concept of Infrastructure from Code, where the infrastructure requirements are inferred from the business logic written by developers.
This innovative approach means that developers can focus solely on crafting solutions for business challenges while the infrastructure management is automated.
Infrastructure from Code is poised to be a transformative force in the serverless domain. Once Serverless Cloud establishes a strong precedent for various use cases, other innovators will follow, introducing even higher levels of abstraction. Best practices will be seamlessly integrated into straightforward transformations based on your business logic.
By removing the complexities of infrastructure decisions, serverless will transition from a chaotic landscape to a standardized practice reinforced by established patterns and protocols.
With a complete commitment to Infrastructure from Code, we eliminate the traditionally challenging aspects of serverless development—issues like missing permissions or misfiring triggers.
Serverless will become increasingly streamlined, allowing for rapid transitions from concept to production.
Chapter 2: The Best of Both Worlds
Once we have intelligent monitoring and automated infrastructure generation, what lies ahead?
This is where things get truly exciting. Picture this: your development teams create a serverless application that is initially deployed with infrastructure derived from the code.
After a month of operation, analytics determine traffic patterns and optimize the distributed transactions across microservices. If the originally inferred resources fall short of the application's scaling needs, monitoring will detect the inefficiencies and automatically reconfigure the resources to better align with actual usage.
In this scenario, the infrastructure would not only scale to meet demand but also reorganize itself to maximize cost-efficiency, performance, and sustainability based on real-time data.
Your application could continuously refine its infrastructure. It might begin with an API Gateway integration to a Lambda Function and DynamoDB, but as usage analytics develop, it could seamlessly transition to a direct connection from API Gateway to DynamoDB, eliminating unnecessary components.
Transforming application monitoring alongside Infrastructure from Code is vital for achieving this vision.
Section 2.1: What Remains Constant
Even in a future characterized by self-provisioning infrastructure, certain elements of serverless development will remain unchanged. Data modeling and API modeling will still require manual intervention.
But why is that?
When we discuss abstraction, it often pertains to “domain-free” complexities—elements that can be generalized. However, once domain-specific data enters the equation, nuances arise that complicate generalization.
Data models are inherently influenced by access patterns, which are unique to each domain. They are shaped by how you approach problem-solving within your specific context. Consequently, there is no one-size-fits-all abstraction that can automatically generate a data model tailored to your data access needs.
While improvements are ongoing, we may never reach a fully automated production-ready solution.
API modeling follows a similar trajectory. When designing a REST API, the endpoints are crafted to navigate through data entities. If data modeling requires manual effort, it stands to reason that API modeling will as well.
Creating APIs that enhance developer experience often necessitates a personal touch, even if it means deviating from standard practices. Sometimes, a workaround that sacrifices strict adherence to REST guidelines for the sake of developer efficiency is a compromise worth making.
Conclusion
The future of serverless technology is brimming with potential. As abstractions continue to rise, our jobs will become increasingly streamlined.
Applications may evolve to become "self-aware," capable of determining the optimal infrastructure based on their usage and traffic patterns. Progress in Infrastructure from Code is already evident, and it will undoubtedly improve over time.
While theoretical, the journey ahead is anything but impossible. The revolutionary tools being developed today integrate flawlessly into existing cloud vendor environments. It is entirely conceivable that these tools could analyze usage and adjust infrastructure dynamically.
We have much to anticipate in the realm of serverless technology in the coming years. Keep learning, experimenting, and innovating.
Happy coding!