Stop Building Spring Apps Without Understanding These Three Things

Last month, I shipped a caching layer to production that tanked performance instead of improving it. The irony? I'd implemented it "correctly" by the documentation. I added @Cacheable annotations, configured Redis, and waited for the magic. What I didn't understand was why Spring was doing what it was doing under the hood. That's when I realized I'd been cargo-culting Spring for years, knowing how to use it, but not understanding how it works.

Reading about someone else's intentional deep dive into Spring made me realize something: most of us skip the foundational work. We learn Spring through tutorials and Stack Overflow answers, never pausing to understand the architectural patterns it's built on. We treat the framework like a black box and wonder why things break in production.

The Pattern You're Already Using Without Knowing It

The article covers something I wish someone had forced me to understand earlier: AOP (Aspect-Oriented Programming). Spring doesn't magically apply caching behavior to your methods. It's not magic at all. Spring uses the Proxy Pattern to intercept your method calls and wrap them with additional behavior, in this case, checking the cache before executing the real method.

This isn't unique to caching. You're already using AOP when you add @Transactional, @Secured, @Logged, or any other cross-cutting concern. The framework intercepts your method, does something before it runs, and optionally does something after. Understanding this single concept, that Spring is basically wrapping your methods, changes how you think about what's possible and what's expensive.

The challenge is that AOP feels abstract until you've debugged a transaction that didn't rollback the way you expected, or wondered why your security annotation didn't trigger. Then it clicks.

Caching: Simpler Than You Think, Harder Than It Seems

I respect that the article spends real time on caching strategy. Too many developers think caching is just "store this, retrieve that." The actual problem is harder: what should you cache, for how long, and how do you invalidate it?

I once cached user profiles by user ID. Seemed obvious. Then a user changed their email and three instances in my cluster disagreed on what the email was. @CacheEvict fixed it, but only after I'd learned that caching strategy matters more than caching technology.

The article mentions @Cacheable, @CachePut, and @CacheEvict. These are three different problems:

• @Cacheable: "Check cache first, if miss, execute and cache"
• @CachePut: "Always execute, then update cache"
• @CacheEvict: "Remove from cache"

Pick the wrong one and you're either stale, slow, or both.

The Reactive Stuff: Where Production Reality Gets Real

The second half of this learning journey covers Reactive Programming. This is where I part ways slightly with the framing. Understanding Mono/Flux/WebClient is useful, but it's also dangerous if you don't understand when to use it.

Non-blocking doesn't automatically mean faster. It means your threads aren't sitting idle waiting for I/O. If you're building an API that calls a database and returns JSON, blocking Spring MVC is probably fine. If you're building something that handles thousands of WebSocket connections or streams massive datasets, reactive becomes necessary.

I've seen teams adopt reactive frameworks because it sounded modern, then struggle with debugging thread-pool issues they never had with blocking code. Reactive is more powerful but also more unforgiving.

What I'd Do Differently

If I were doing a deep Spring learning sprint like this, I'd add one more piece: understanding when NOT to use Spring features. Caching is powerful but adds operational complexity. Reactive is efficient but harder to reason about. The best architecture is sometimes the simplest one that still works.

I'd also spend time with actual production debugging. Read source code. See what actually happens when @Cacheable runs. Use a profiler. Theory is good, but feeling your code execute teaches differently.

The Question Worth Asking

Here's what stuck with me: if you don't understand the patterns and mechanisms Spring uses, can you really design systems with it responsibly?

Taking time to go deeper, really deeper, not just reading more tutorials, is how you stop relying on luck. It's how you make intentional decisions instead of hoping the framework does the right thing.

Are you building Spring applications based on understanding, or based on patterns you've copy-pasted enough times to feel familiar?

Source: This post was inspired by "Week 9 of #100DaysOfCode: A Week of Deep Spring Learning" by Dev.to. Read the original article