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Premature Scaling

· 4 min read
Milad

Last year, while scaling an API service, I found myself reaching for Redis and EC2 auto-scaling groups far too early. It seemed like the “right” thing to do—after all, it was what I’d seen in tutorials, open-source projects, and most blogs. As a junior developer, I thought that using “production-grade” solutions like these was the most logical path.

At the time, it felt like progress. I was learning AWS, configuring auto-scaling groups, and managing Redis instances. But what I didn’t realize was that all of this came with significant overhead—both in terms of time and complexity.

It wasn’t until recently—today, in fact—that I stumbled across Python's built-in @lru_cache, and everything clicked. Three lines of code would’ve solved 90% of my latency issues, without the need for any external infrastructure. It was a humbling realization: I had overengineered the solution.

This is the heart of the lesson I want to share: Premature scaling can feel productive, but in reality, it often becomes a high-effort distraction. In my case, time spent wrestling with infrastructure could’ve been better spent on improving features, writing tests, or gathering user feedback. Scaling isn’t just about adding complexity—it’s about adding leverage.

Imagine a simple SaaS app during a high-traffic event like Black Friday. It crashes under the load, and you scramble for a solution. What saved the app? A simple, 3-line cache wrapper.

warning

Caching brings tradeoffs. If your data changes frequently, managing cache invalidation or setting proper TTLs (time-to-live) becomes essential to avoid serving stale data.

🔍 The Setup​

In Python, functions running in serverless environments often benefit from warm starts. During this period, decorators like @lru_cache (for function-level caching) and @cached_property (for property caching) can significantly reduce repeated computations.

🛠️ Code Examples​

Using lru_cache:

from functools import lru_cache

@lru_cache(maxsize=128)
def expensive_function(x):
    return x * x

Using cached_property :

from functools import cached_property

class MyClass:
    def __init__(self, value):
        self.value = value

    @cached_property
    def expensive_computation(self):
        print("Computing...")
        return self.value ** 2

obj = MyClass(5)
print(obj.expensive_computation)  # Output: Computing... 25
print(obj.expensive_computation)  # Output: 25 (cached result)

Start with @lru_cache to learn the habit of thinking in cache layers and minimizing redundant work.

Final Thought​

Chasing complex scaling solutions before optimizing the basics is like putting jet engines on a bicycle before oiling the chain. Always ask yourself: What’s the simplest change that buys me the most breathing room?

🧭 Beyond Python​

Premature Scaling Across Ecosystems​

While this post focuses on Python, the temptation to over-engineer caching and scaling is everywhere. Whether you're working with Node.js, Go, Rust, or Java, the same pattern emerges. Each backend ecosystem has simple, effective caching tools that are often overlooked in favor of heavier solutions like Redis.

Use @lru_cache first; Redis later, only if you need cross-process sharing.

warning

Redis is not a cache by default. It’s a shared key-value store. Using it for caching means you must manage TTLs, consistency, and eviction policies—unlike native solutions optimized for single-node performance.

Further Reading​