Blind Driven Development: When Technology Choices Go Wrong

In the fast-paced world of software development, companies often make technology decisions based on trends rather than carefully evaluating their actual needs. This approach, which I call Blind Driven Development, leads to serious inefficiencies, increased maintenance costs, and even complete project failures. Below, we explore four common cases where companies blindly implement solutions without proper understanding, ultimately making things worse.

Case 1: The Load Balancer Misuse Disaster

A tech company facing performance issues decides to implement a load balancer without fully understanding what it does. The assumption? Distributing traffic across multiple servers should magically improve performance. However, the real bottleneck wasn't the web servers but the database connection limit. Here’s how things went south:

• SSL issues: The team had no experience handling SSL termination at the load balancer level. This led to security misconfigurations, broken HTTPS redirections, and certificate renewal headaches.
• Session management nightmares: Since the web application relied on server-side sessions, users experienced frequent logouts because requests were routed to different servers without a shared session mechanism.
• Performance issues persisted: After all the effort and investment, the original performance issue remained because the real problem—the database connection limit being hit—was never addressed.

What Should Have Been Done?

Before implementing a load balancer, the company should have:

• Conducted performance profiling to identify actual bottlenecks.
• Optimized database queries and increased connection pooling.
• Considered caching solutions to reduce database load before scaling horizontally.

Case 2: The Microservices That Should Have Been a Monolith

Another tech company, eager to follow modern trends, adopts microservices architecture for better scalability and flexibility. However, they fail to recognize that their application consists of basic CRUD operations, leading to a fragmented system that’s more complex than necessary. Here’s what went wrong:

• Interdependent services: Each microservice was tightly coupled, meaning none could function independently. If any one microservice went down, the entire system became non-functional due to its dependencies.
• Excessive domain requirements: Each microservice required its own domain name binding to function correctly. This created an operational nightmare for system administrators who had to maintain dozens of domain mappings.
• Overhead with no real benefits: Instead of gaining flexibility, the team faced increased latency, complex deployments, and an explosion of API calls between services.

What Should Have Been Done?

Instead of forcing microservices, the company should have:

• Started with a modular monolith that keeps concerns separate while remaining manageable.
• Ensured clear service boundaries before splitting into microservices.
• Avoided domain name dependencies by using internal service discovery mechanisms like service meshes or container orchestration tools.

Case 3: The Environment Variable Mess

A company decides to use environment variables to handle configuration differences between development, staging, and production environments. While the intent is good, the execution is deeply flawed. Here’s what went wrong:

• Failure to differentiate build-time and runtime variables: All variables were treated as build-time variables, meaning changes required rebuilding the entire application instead of being applied dynamically at runtime.
• CI/CD nightmares: Since every change in environment configuration required a rebuild, the CI/CD process became cumbersome, slowing down deployments and increasing operational overhead.
• Exposing sensitive data in Git: Instead of keeping environment-specific values in a secure vault, all values for all three environments were committed to Git, increasing the risk of credential leaks and security breaches.

What Should Have Been Done?

To avoid these pitfalls, the company should have:

• Separated build-time and runtime variables, ensuring runtime variables could be updated without requiring a rebuild.
• Used secure environment management tools like HashiCorp Vault, AWS Secrets Manager, or Kubernetes Secrets instead of storing values in Git.
• Configured CI/CD pipelines properly, allowing different environments to pull the correct configurations dynamically during deployment.

Case 4: The Developer Who Creates Tech Debt

Some developers become sources of persistent technical debt due to poor practices and resistance to learning. Here’s how one such developer causes chaos:

• Refuses to adapt to modern tools: While the industry has largely moved to Vite for frontend builds, this developer insists on using Webpack for every new project, even when teammates have already set up Vite. Worse, he modifies projects to revert back to Webpack.
• Mixes package management methods: Instead of maintaining a clean dependency structure, he copies and pastes library code into the codebase while also using a package manager, leading to conflicts (e.g., multiple versions of jQuery in the same project).
• Hardcodes configurations: Instead of making settings configurable, he hardcodes an error report function that sends reports to a specific Telegram channel, making it difficult for others to maintain or adapt.
• Has over 10 years of experience but ignores best practices: Despite years in the field, he avoids best practices, creating a maintenance nightmare for the team.

What Should Have Been Done?

To prevent one developer from accumulating technical debt:

• Set coding standards: Enforce project-wide guidelines on dependency management, configuration handling, and best practices.
• Conduct code reviews: Regularly review commits to catch unnecessary tech debt before it becomes a problem.
• Encourage continuous learning: Developers should stay updated on modern tools but also know when to use older, stable technologies appropriately.

Conclusion

Blindly adopting technologies without understanding them leads to technical debt, wasted effort, and frustration. Before jumping into a new architecture or tool, always:

1. Analyze the problem thoroughly – Understand what you’re solving before choosing a solution.
2. Educate your team – Ensure your developers, DevOps, and system admins are on the same page.
3. Test and validate – Start small and measure improvements before committing to full-scale implementation.

By avoiding Blind Driven Development, companies can make informed, strategic decisions that lead to scalable, maintainable, and high-performing systems.