Developing applications natively for the cloud involves architecting them to maximize the benefits of cloud platforms like scalability, reliability, and efficiency. Two popular architectural paradigms emerging for cloud-native apps are containers and serverless. Both aim to simplify application deployment and management in the cloud but take different approaches. In this article, we first examine what containers and serverless architectures entail. We then compare their relative strengths and weaknesses for different use cases to help guide the choice between them.
Introduction to Containers
Containers allow packaging an application with all its dependencies and configurations into a standardized unit that can run seamlessly in different environments. This enables portability between on-premise servers and cloud platforms. Key features of containers:
- Lightweight: Containers share the host operating system kernel instead of virtualizing hardware, making them streamlined.
- Self-contained: All application components like libraries, frameworks, and settings are enclosed within the container image.
- Isolated: Containers are segregated from each other and the underlying infrastructure through filesystem and process isolation.
- Portable: Container images based on standard formats like Docker run consistently across environments.
- Scalable: Containerized apps can be easily replicated to scale horizontally across clusters of hosts to meet demand.
Leading platforms like Kubernetes provide orchestration capabilities to deploy, manage and scale container-based applications. Next, let’s understand the serverless architecture.
Introducing Serverless Computing
Serverless computing allows running application code in stateless containers that are event-triggered and ephemeral. The cloud provider transparently handles provisioning and managing servers. Key attributes:
- No server management: Infrastructure capacity is auto-scaled based on usage. Developers are abstracted from servers.
- Granular scaling: Scaling happens at a per-function level enabling consumption-based costs.
- Event-driven: Functions execute in response to triggers like HTTP requests, database changes, API events, etc.
- Ephemeral: Containers hosting functions exist only during execution before being shut down and recycled. The state is externalized.
- Usage-based pricing: Pay only for compute time consumed by functions rather than having continually running servers.
With no overhead of managing backends, serverless enables focus on writing application logic. It is suitable for event-based applications. Next we compare both architectures.
Key Differences Between Containers and Serverless
While containers and serverless have some similarities like lightweight execution environments, they differ in various aspects:
- Resource management: Containers allow granular control over computing resources allocated. Serverless abstracts away direct control over resources.
- Persistent vs ephemeral hosts: Containers run on persistently available hosts like EC2 instances while serverless function containers are ephemeral.
- State management: A state can be stored within containers using volumes whereas a serverless state must reside externally in a database.
- Cold start latency: Container deployments minimize cold starts whereas serverless functions incur cold starts each invocation due to initializing resources.
- Execution duration: Containers support long-running execution but serverless functions have execution duration limits, typically around 15 minutes.
- Skill sets: Containers require DevOps skills for infrastructure and cluster management. Serverless shifts focus to application code.
- Cost model: Container costs are steady based on provisioned capacity while serverless costs dynamically scale with usage.
Now that we have compared both paradigms, let’s examine which use cases they are most suitable for.
Guidelines for Choosing Between Containers and Serverless
Here are some key considerations when deciding between containers and serverless for cloud-native application architecture:
- Application workload patterns: Serverless suits sporadic, bursty workloads with idle periods. Containers work better for steady predictable workloads using persistent hosts.
- Team skills: If the team is proficient at infrastructure management, containers provide more control. For teams focused on application code, serverless reduces overhead.
- Performance needs: Containers offer faster cold start times crucial for latency-sensitive applications. Serverless cold starts may impact response times.
- State management needs: Applications with substantial states are easier to run in containers with access to storage. Stateless applications fit the serverless model.
- Cost efficiency: For workloads with variable traffic, serverless provisioning based on usage may reduce costs. Steady traffic favors containers amortizing provisioned capacity.
- Runtime durations: Applications with long-running processes like simulations or complex calculations are better suited to containers than serverless duration limits.
By evaluating these aspects, suitable architecture choices can be made per application or workload. Hybrid models combining both are also possible.
Container and Serverless Platforms
Here are some leading platforms providing enterprise-grade container and serverless solutions:
- Kubernetes is the most popular open-source container orchestration platform, with managed services available on AWS EKS, Azure AKS, and GCP GKE.
- AWS Fargate provides serverless containers that abstract away orchestration complexities. AWS Lambda is a prominent serverless functions platform.
- Azure Container Instances and Azure Functions offer container hosting and serverless computing respectively on Microsoft Azure.
- Google Cloud Run allows deploying stateless containers as serverless functions that scale automatically without a separate orchestrator.
Conclusion
Containers and serverless both enable simplifying deployments and reducing overhead for cloud-native apps in different ways. Containers excel at predictable workloads and stateful applications while serverless shines for sporadic, event-driven scenarios. By understanding their complementary strengths, architects can choose the right paradigm based on application characteristics, team skills, and business needs. Blending them also provides benefits like containers for core processing with serverless for fluctuating peripheral tasks. As cloud-native adoption grows, both architectures will play crucial roles in empowering the next generation of applications.