Identifying the minimal viable solution before adding complexity

Identifying the minimal viable solution (MVS) is one of the most powerful techniques in software development and problem-solving. By focusing on the simplest solution that meets core requirements, teams avoid “feature bloat” and can validate core functionality quickly. This approach fosters clarity, reduces risk, and creates a more reliable foundation on which to build additional capabilities. In this guide, we’ll explore the essentials of finding your MVS and illustrate how it can set you up for success—whether you’re coding a new feature, architecting a system, or preparing for a technical interview.

Table of Contents

1. Why Focus on the Minimal Viable Solution?
2. Strategies to Identify Your MVS
3. Applying MVS Thinking to Coding Interviews
4. Real-World Example: A Simplified Approach to System Design
5. Recommended Resources to Sharpen Your Skills

1. Why Focus on the Minimal Viable Solution?

1. Speed to Validation
   By building only what’s absolutely necessary, you can quickly validate your idea. Early user or stakeholder feedback is invaluable and will inform next steps without wasting time on unproven features.

2. Reduced Complexity
   Every feature or function you add has a cost in terms of development, testing, and long-term maintenance. Minimizing scope initially helps your codebase remain more manageable and stable.

3. Lower Risk
   Complex solutions have more moving parts, which can introduce more bugs and integration issues. An MVS-based approach ensures you tackle the most critical path first, minimizing potential points of failure.

4. Easier Iteration
   Once the minimal solution is in place, you can gradually layer new features and optimizations. This incremental approach promotes better architectural decisions and smoother scaling in the future.

2. Strategies to Identify Your MVS

a) Define Core Requirements

List out what the user or system fundamentally needs to function:

• Must-Haves: If these are missing, the solution fails its primary goal.
• Nice-to-Haves: These enhance user experience or performance but aren’t strictly necessary to prove viability.

b) Think in Terms of Use Cases

Imagine the simplest, valid scenario from start to finish. Outline every step in that workflow. Anything beyond enabling this core flow is secondary and can come later.

c) Evaluate Trade-Offs

Consider the time and complexity cost of each feature:

• Ask “Why?”: Each time you introduce a function or data structure, ensure it aligns directly with your must-have use cases.
• Ask “Is There a Simpler Way?”: Sometimes a well-chosen data structure or straightforward approach eliminates the need for multiple add-on features.

d) Start With a Prototype or Mock

Create a simplified version to visualize and validate how users or other systems might interact with it. This helps surface design flaws before you invest heavily in additional capabilities.

3. Applying MVS Thinking to Coding Interviews

Minimal viable solutions aren’t just for production apps. They’re also crucial in interview scenarios where time is limited and clarity is key.

1. Outline a Clear Plan
   Before diving into code, articulate the minimal logic needed to solve the problem. This demonstrates methodical thinking and helps the interviewer follow your approach.

2. Implement the Core First
   Solve the simplest form of the problem—maybe that’s the brute force approach or a core data structure setup. Then, if you have time, optimize.

3. Explain Potential Enhancements
   Even if you don’t implement every optimization, show awareness of what could be improved. This positions you as a strategic problem-solver who understands incremental enhancement.

For more detailed guidance on balancing solution simplicity with readiness for advanced topics, check out Grokking the Coding Interview: Patterns for Coding Questions. It’s an excellent resource for practicing problem decomposition and identifying the most direct solutions.

4. Real-World Example: A Simplified Approach to System Design

Scenario: Designing a basic messaging service.

• Minimal Requirement: Two users should be able to send text messages to each other in real time.
• MVS Outline:
  1. Data Model: Store a message object (sender, receiver, content, timestamp).
  2. API: A single endpoint to send a message and another to retrieve messages.
  3. Simple Storage: A relational database or NoSQL store to hold messages.
  4. Real-Time Aspect: Use a basic WebSocket connection or polling for minimal viable real-time updates.

Only once these essentials are proven to work under basic load would we then consider:

• Advanced Scalability: Sharding for large user counts.
• Multi-Media Messages: Support for images or videos.
• Complex Delivery Status: Adding read receipts, typing indicators, etc.

This iterative approach helps ensure the messaging service’s foundational components are stable before layering on complexities. For those looking to master scalable designs step-by-step, Grokking System Design Fundamentals and Grokking the System Design Interview provide structured insights into building from the ground up.

5. Recommended Resources to Sharpen Your Skills

Below are resources from DesignGurus.io that help you adopt an MVS-first mindset for coding interviews and large-scale architectures alike:

1. Grokking the Coding Interview: Patterns for Coding Questions

   • Breaks down common coding challenges into simple, reusable patterns.
   • Great practice for learning to identify and implement a minimal initial solution.

2. Grokking the System Design Interview

   • Walks you through real-world design problems in a progressive manner, emphasizing which steps are truly essential in each stage of scaling.

3. Mock Interviews for Personalized Feedback

   • Sign up for System Design Mock Interview sessions or Coding Mock Interviews to test your MVS approach with ex-FAANG engineers.
   • Tailored feedback helps you refine how you structure solutions—and when it makes sense to add complexity.

Bonus: Watch the DesignGurus YouTube Channel

Check out the DesignGurus YouTube Channel for system design and coding tutorial videos that illustrate how experts break down and solve complex challenges by starting with minimal viable solutions.

Conclusion

Focusing on the minimal viable solution before adding complexity can transform your development approach, reduce unnecessary overhead, and accelerate the feedback loop. Whether you’re whiteboarding a new idea, solving an interview question, or architecting a global-scale system, start small. Prove the core logic, validate assumptions, and then layer on complexity in an informed, controlled manner.

By practicing this mindset with the help of resources like Grokking the Coding Interview or Grokking the System Design Interview, you’ll develop the discipline to say “no” to extraneous features when it matters most. In doing so, you’ll deliver robust, adaptable solutions that can gracefully expand—or pivot—to meet future demands.