How to Tell If Your Project’s Tech Stack Is Reasonable (and Avoid Interview Disses)

Many job seekers fill their resumes with impressive‑sounding technologies, but experienced interviewers can quickly spot unreasonable designs, especially for fresh graduates.

The core question is: How can you judge whether the technical solution in your project is reasonable?

The author recommends actively reading real‑world case studies shared by large‑tech teams or knowledgeable bloggers, because relying solely on AI may produce hallucinated answers.

Most online tutorials aim to demonstrate a technology or achieve a feature, while real production environments care about stability, scalability, and cost, which dictate when and how a technology should be used.

Studying authentic practice cases brings three concrete benefits:

You can recognize solutions that look natural in tutorials but are over‑designed or wrong in production, helping you avoid the “use technology for technology’s sake” trap.

Articles often detail the reasoning behind a technology choice, comparing alternatives, listing pros and cons, and explaining why option A was selected over option B, giving you deeper insight and multiple backup options for interview discussions.

When other candidates only say “I used X technology,” you can articulate the context—"I chose X in scenario Y because of considerations Z and solved problem Q"—making your experience stand out.

Typical examples of unnecessary technology usage include:

Force‑installing a full micro‑service stack : applying service discovery, config center, gateway, distributed transaction, and tracing to a simple system like campus food ordering, where a clean monolith would be optimal.

Using Redis as a universal solution : stuffing ranking, sign‑in, UV stats, friend follow, etc., into Redis without evaluating whether an external cache is truly needed.

Deploying a Redis cluster for low‑traffic internal tools : adding unnecessary complexity and maintenance cost when a single‑node Redis (or none) would suffice.

Introducing a message queue for trivial tasks : using Kafka or RocketMQ for simple actions like sending a welcome email, where a SpringBoot @Async annotation and a custom thread pool are more efficient.

Premature sharding and splitting databases : assuming large tables require sharding without first optimizing SQL, adding indexes, using caching, or applying read/write separation.