Why You Should Occasionally Write Non-Scalable Applications

Breaking the Scalability Obsession

Let’s start with heresy: sometimes writing deliberately non-scalable code is the professional choice. I know, I know – it feels like suggesting a chef should occasionally undercook chicken. But hear me out before you grab the pitchforks. That prototype needed yesterday? That internal tool used by three people? That experimental feature with a 5% adoption chance? Sacrificing scalability here isn’t laziness – it’s strategic triage. Think of scalability debt like financial debt: sometimes taking a small, intentional loan lets you seize an opportunity. The key is knowing when to pay it back… or knowing the loan will self-destruct before maturity. Remember Dave’s Law: “A program that must be changed is harder to change than one that never worked.”

When Non-Scalable Wins: The Sweet Spots

Proof-of-Concept Prototypes

# Database? Who needs databases! 
results = {}
for file in glob.glob("*.csv"):
    with open(file) as f:
        results[file] = process(f.read())  # Load entire file? YOLO!

Here’s the brutal truth: if I’d built a Kafka pipeline for that PoC, the business would’ve moved on before I finished. Sometimes you need to answer “Is this possible?” before “Can it handle 10k RPS?” Tactical Tools

# Internal deployment script for team of 3
for server in $(cat servers.txt); do
    scp build.zip $server:/tmp && ssh $server "unzip /tmp/build.zip"
done

Adding Kubernetes orchestration here would be like using a flamethrower to light a birthday candle. When your user count fits in a minivan, optimize for readability, not horizontal scaling. Experimental Features

// Temporary analytics hack
window.tempTracker = (event) => {
    const blob = new Blob([JSON.stringify(event)], ...);
    navigator.sendBeacon("/analytics-dump", blob); // Just firehose it!
};

When testing whether users even want a feature, collecting messy data fast beats elegant collection of nothing.

The Art of Intentional Technical Debt

Writing non-scalable code responsibly requires discipline:

1. Fence the Debt
   Wrap non-scalable components in modules with clear interfaces. Pretend it’s radioactive – contain it!
2. Document the Grenade Pin
   Comments like this save careers:

   // NON-SCALABLE: Global cache for <1k items. 
   // BREAKS at ~1500 users. See ticket #PAYME-42
   var userCache = make(map[string]User)
   

3. Schedule the Bomb Disposal
   Link non-scalable code to a specific trigger:

   - [ ] Refactor inventory sync when:
         * Daily active users > 500 OR 
         * Support tickets about sync > 3/week
   

4. Build the Scaffolding First
   Even in quick hacks, leave extension points:

   def process_data(data):
       # Currently uses simple loop 
       # Replace with Ray when needed
       return [transform(item) for item in data]
   

When Non-Scalable Becomes Unforgivable

The litmus test: Will scaling needs likely emerge before we can refactor? If yes, you’re not being pragmatic – you’re planting time bombs.

The Psychology of Strategic Scaling

This workflow acknowledges reality: 60% of ideas fail. Spending three weeks building scalable infrastructure for a feature killed after launch isn’t engineering rigor – it’s professional wastefulness.

The Payoff: Controlled Burn Development

Last quarter, our team shipped 4x more experiments by embracing tactical non-scalability. The secret? We had a technical debt SWAT team that followed behind successful prototypes like literary editors cleaning up a first draft. As my teammate Sarah says: “Scalability is like punctuation – essential for published works, optional for brainstorming notes.” Now if you’ll excuse me, I need to go violently refactor that CSV processor from last year… before it meets real users.