A Lean Decision-Making Approach for Choosing Java Frameworks with Architecture Decision Records

Technology changes fast. But there's still business value that needs to be created and software development teams and architects can't do proof-of-concepts and research all day. Even more important to make sure you are choosing frameworks that fulfill both current needs and future expansion requirements. Management often demands a solution that is lightweight, durable, and efficient for distributed systems. This is especially true for edge deployments, where resources can be constrained yet still require robust performance and low latency.

To more easily assess the suitability of frameworks, tools and potentially languages, a structured and transparent decision-making process is key. This article proposes a lightweight approach to assess both functional and non-functional requirements. I will cover:

1. Why Architecture Decision Records (ADRs) are useful
2. A lightweight methodology to capture decisions and evaluations
3. A comparison of Spring Boot and Quarkus in the context of large-scale distributed applications, including edge nodes

What are Architecture Decision Records and why are they useful?

Architecture Decision Records (ADRs) are short, concise documents that capture an important architectural decision, its context, the choices considered, and the rationale behind the final selection. They improve maintainability and transparency by documenting:

1. Context: The problem, constraints, and stakeholders
2. Decision: The choice made, including trade-offs and alternatives
3. Consequences: Positive and negative implications of implementing the chosen approach

By using ADRs, decisions can be easily tracked over time, the rationale is clear to new team members or future maintenance teams, and continuous improvement is better supported by enabling decisions to be revisited with historical context.

A Lightweight Methodology for Framework Evaluation

Let's look at a hypothetical example. Let's say you need to evaluate Java frameworks for a highly distributed application with edge deployments:

Identify Requirements

• Functional Requirements: Business and technical capabilities the framework must support (e.g., REST endpoints, messaging, data processing, long running transactions, specific UI elements for the frontend, etc).
• Non-Functional Requirements (NFRs): Performance, scalability, resource usage, security, observability, maintainability, developer productivity, and so forth. Explicitly include constraints that are specific to your use case. In this edge deployment example, it can be things like memory footprint and startup times—these.

Define Decision Criteria

List the relevant criteria for your organization. Examples:

• Performance & Startup Time
• Memory & Resource Footprint
• Developer Experience & Community Support
• Cloud-Native & Container Readiness
• Edge Readiness (offline operations, small resource footprint, resilience)
• Ecosystem Integration (libraries, frameworks, tools)

Evaluate Candidates

• Review each framework against the criteria above.
• Where possible, collect empirical data (e.g., minimal examples, memory usage snapshots) and weigh any anecdotal or community feedback.

Create an ADR for the Decision

An Architecture Decision Record (ADR) generally follows a structured format that includes a title, date, status, context, decision, and consequences. The title captures the essence of the choice at hand, and the date indicates when the record was created or updated. The status reflects whether the decision is proposed, accepted, or superseded by another record. The context section describes the problem or situation prompting the decision, highlighting relevant factors. The decision section documents the chosen approach and briefly notes alternatives, illustrating why other paths were not selected. Lastly, the consequences section addresses anticipated outcomes and potential impacts, helping teams understand trade-offs and benefits.

• Record the context: Why you need a particular framework now.
• List alternatives: Summarize each potential option.
• Document the decision: Include a summary of trade-offs.
• Record consequences: Document any known issues and future considerations.

Validate

In order to validate the chosen framework, it is advisable to construct a minimal Proof of Concept (PoC) and test all critical functionality or workflows to ensure feasibility, performance, and reliability. If the PoC reveals significant shortcomings, the ADR should be revisited and updated accordingly to integrate newly acquired insights. Documenting the initial experiences in setting up development configurations in detail will help subsequent teams get a head start, avoiding repetitive tasks and setup challenges. In addition, other best practices for small PoCs include outlining the environment configurations, collecting performance metrics early on, and seeking timely feedback from the development team, allowing for rapid adjustments before the project becomes too large or complex. By following these measures, the rationale and practical considerations remain clear as the project grows and evolves.

Comparing Spring Boot and Quarkus for a Highly Distributed Edge-Enabled Application

For this example walk through, I will evaluate Spring Boot and Quarkus using the criteria described above. Both are excellent Java frameworks, though each has strengths that may make it a better fit depending on the circumstances. Let's find out.

Performance & Startup Time

Performance and startup time are crucial in any system design, directly affecting how quickly services can respond to traffic and scale to meet demand. From a software architecture perspective, frameworks that minimize startup overhead and memory usage can enable more efficient resource allocation, especially in environments where services are frequently deployed, stopped, and restarted. This is particularly relevant in serverless contexts or highly distributed microservice architectures, where swift initialization and consistent performance can reduce costs, improve responsiveness, and ultimately enhance the user experience.

• Spring Boot

  • Known for rapid developer setup and a robust ecosystem, but startup times can be longer.
  • Resource footprint can be higher if you add many Spring starters.

• Quarkus

  • Designed to reduce startup time and memory usage through approaches such as ahead-of-time (AOT) compilation with GraalVM and build-time metadata processing.
  • Attractive for serverless or edge scenarios where quick start and small footprint are crucial.

Verdict: Quarkus has an advantage in ultra-fast startup and minimal resource use. Spring Boot can be optimized but may require more effort to match Quarkus in this category.

Memory & Resource Footprint

Memory and resource footprint are essential considerations for software architects, as they influence scalability, cost, and hardware demands. Efficient use of resources means an application can serve more requests with fewer or smaller nodes, which is key in edge or constrained environments. Striking a balance between feature richness and lightweight operations helps ensure that microservices or containerized workloads perform optimally while remaining flexible and portable across different deployment scenarios.

• Spring Boot

  • Generally requires more memory at runtime—though many microservices can handle the overhead.
  • Community knowledge on optimizing Spring Boot and containerizing effectively is extensive.

• Quarkus

  • Focused on minimizing resource usage.
  • Well-suited for smaller devices or large numbers of microservices that need to scale quickly.

Verdict: Quarkus stands out for lean resource use; Spring Boot is often enough for typical cloud environments but may be less optimal for tight edge constraints.

Developer Experience & Community Support

Developer Experience and Community Support are significant factors in framework selection, as they influence productivity, onboarding speed, and long-term maintainability. From a software architecture standpoint, a large, active community often translates to a broader ecosystem of plugins, libraries, and shared expertise, which can reduce development hurdles. Meanwhile, robust vendor support options offered by entities such as Red Hat can be critical for enterprises needing guaranteed service-level agreements, professional troubleshooting, and enhanced security features. The availability of commercial backing also shapes cost considerations, as organizations may pay for these vendor services in return for assured updates, patches, and expert assistance. Ultimately, weighing these factors ensures the selected framework aligns with both your development culture and your enterprise-level reliability needs.

• Spring Boot

  • A mature, vast community offering extensive documentation and support.
  • Popular tools like Spring Initializr and Spring Cloud facilitate rapid development.
  • Many enterprises already have experience with Spring.

• Quarkus

  • A newer ecosystem (supported by Red Hat), though growing steadily in community size and features.
  • Offers modern tooling, but not yet as many add-ons as Spring’s longtime ecosystem.

Verdict: Spring Boot is an industry giant with a broad support base. Quarkus is advancing quickly but still smaller in terms of available plugins and community presence.

Cloud-Native & Container Readiness

Cloud-native and container readiness are important factors in modern software architectures, where services commonly run in containers orchestrated by platforms such as Kubernetes. A framework that integrates cleanly with container workflows, supports standardized health checks and metrics, and offers quick startup times can simplify the deployment process. Lightweight container images reduce overhead and costs, while also making it easier to scale services dynamically. Evaluating these capabilities early ensures the chosen framework will adapt well across development, testing, and production environments, ultimately helping maintain efficiency and consistency in large, distributed systems.

• Spring Boot

  • Integrates seamlessly with Spring Cloud tools (Config Server, Service Discovery, Circuit Breakers, etc.).
  • Well-tested in containers and on Kubernetes, though image sizes and resource usage can grow.
  • Spring Native is a recent effort to reduce footprint but remains in progress.

• Quarkus

  • Built with containers in mind, using MicroProfile standards like Health, Metrics, and Config.
  • Produces lean, native executables that are ideal for orchestrated environments where rapid scale-up/-down is key.

Verdict: Quarkus excels in container-focused deployments from day one, while Spring Boot provides extensive enterprise components but may need extra steps for optimal container performance.

Edge Readiness

Edge readiness involves designing systems and frameworks that can effectively operate in resource-constrained environments or physically distributed locations outside traditional data centers. This consideration is particularly important when low latency, intermittent connectivity, or limited computing resources come into play. From a software architecture perspective, choosing frameworks that minimize resource overhead and support lightweight deployments can make it more feasible to push critical workloads closer to data sources. This can result in faster response times, reduced network costs, and improved reliability under fluctuating or challenging connectivity conditions.

• Spring Boot

  • Works for edge scenarios if the devices are not overly constrained.
  • Optimizations can help, but the memory overhead might still be significant for very small devices.

• Quarkus

  • Especially suitable for resource-constrained settings, thanks to smaller memory usage and quicker startup.
  • Native images reduce overhead further, making it easier to run multiple services on edge devices.

Verdict: Quarkus is often the better fit for limited-resource edge nodes. Spring Boot can still succeed if hardware is sufficiently capable or if the overhead is acceptable.

Below is the condensed table summarizing the key points about Spring Boot versus Quarkus for highly distributed, edge-enabled applications:

Category	Spring Boot	Quarkus
Performance & Startup Time	- Rapid developer setup, but longer startup times. - Often heavier memory overhead out-of-the-box.	- Designed for low memory usage and fast startup through AOT compilation. - Well-suited for serverless or short-lived workloads.
Memory & Resource Footprint	- Tends to be larger at runtime. - Extensive community knowledge on tuning and optimization.	- Emphasizes minimal resource usage from the ground up. - Ideal for environments with tight constraints (e.g., containers, edge).
Developer Experience & Community Support	- Large and mature ecosystem with extensive documentation. - Popular enterprise choice due to familiarity and available libraries.	- Newer, rapidly growing ecosystem backed by Red Hat. - Modern tooling, though fewer extensions compared to Spring’s large ecosystem.
Cloud-Native & Container Readiness	- Seamless integration with Spring Cloud (e.g., Config, Service Discovery). - Well-tested in Kubernetes, though may need optimization for smaller containers.	- Built with Kubernetes and containers in mind (MicroProfile). - Can produce compact, native executables for optimal container performance.
Edge Readiness	- Viable if edge hardware isn’t highly constrained. - Overhead might be high for very limited devices.	- Generally smaller footprint and quicker startup. - Native images enable better performance on resource-limited edge nodes.

Conclusion and Recommended Approach

Recommendation: If ultra-fast startup, minimal footprint, and container/edge efficiency are top priorities, Quarkus is a strong choice. If your teams already have deep experience in the Spring ecosystem or your hardware can handle the additional overhead, Spring Boot remains a reliable and more established option.

Regardless of the path chosen, document your decision using an Architecture Decision Record. Capture the distributed and edge requirements, evaluation criteria, trade-offs, and the final verdict. This allows your organization—and any future project contributors—to fully understand the rationale and adjust if conditions change later.

Sample ADR Outline

1. Title: Decision on Java Framework for Distributed + Edge Nodes
2. Context: Requirements (functional, non-functional, constraints)
3. Decision: Which framework was chosen, with supporting reasons
4. Alternatives: Summaries of other frameworks considered
5. Consequences: Positive and negative outcomes of this decision

Final Thoughts

A balanced approach to framework selection starts with clarity about requirements and constraints. By using Architecture Decision Records, you keep your choices transparent and documented. Spring Boot remains a leading enterprise solution, though it can have higher overhead. Quarkus is built for resource efficiency and quick startup, making it valuable for distributed systems and edge nodes with limited capacity. A systematic process that addresses your specific needs ensures your chosen framework meets both today’s objectives and tomorrow’s expansion demands.

References & Further Reading

• Architecture Decision Record (ADR)
• ADR GitHub Repo (adr-tools)
• Spring Boot Official Documentation
• Quarkus Official Documentation
• Lightweight Architecture Decision Records