🧨 What I Broke Wednesday: The Great ElastiCache Miss-tery

Picture this: It's 2 PM on a Tuesday. The system is humming along perfectly. Users are happy. Dashboards are green. I'm feeling pretty good about life.

Then I deployed what I thought was a "small optimization."

Spoiler alert: It wasn't small.

The Scene of the Crime

Our API was handling user sessions and frequently accessed data through ElastiCache (Redis). Everything was working beautifully - sub-50ms response times, happy users, happy boss.

Then I had a "brilliant" idea.

The "Optimization" That Broke Everything

I noticed our cache keys looked messy:

user:12345:profile
user:12345:preferences  
user:12345:settings

My brain: "This could be cleaner! Let's namespace everything properly!"

So I "improved" the key structure to:

v2:user:12345:profile
v2:user:12345:preferences
v2:user:12345:settings

The logic: Better organization, easier to manage, more professional looking.

The reality: I just invalidated EVERY SINGLE CACHE ENTRY in production.

When Everything Went Sideways

2:15 PM: Deploy goes live

2:16 PM: Response times jump from 50ms to 800ms

2:17 PM: Slack starts exploding with "is the site slow for anyone else?"

2:18 PM: I'm frantically checking logs

2:19 PM: Cache hit rate: 0.02% (it's usually 94%)

2:20 PM: internal screaming

The Domino Effect From Hell

1. Cache misses everywhere: Every request hit the database
2. Database gets hammered: Connection pool exhausted
3. API timeouts: Users can't load their profiles
4. Mobile app crashes: It wasn't handling timeouts gracefully
5. Support tickets flooding in: "Your app is broken!"
6. CEO asking questions: Never a good sign

The kicker? Our monitoring showed ElastiCache was "healthy" - it was responding perfectly to requests that were missing every single time.

The Frantic Fix

Option 1: Rollback (requires 15-minute deployment process)

Option 2: Warm the cache manually (could take hours)

Option 3: Scale up database temporarily while cache rebuilds

I went with Option 3 + partial rollback:

1. Scaled RDS from t3.medium to r5.xlarge (ouch, my AWS bill)
2. Deployed a hotfix that fell back to old key format for critical paths
3. Gradually warmed the cache over the next 2 hours

What I Learned (The Hard Way)

Cache migrations need strategy: You can't just change keys and hope for the best

Always have a warming strategy:

# What I should have done
def migrate_cache_key(old_key, new_key):
    value = redis.get(old_key)
    if value:
        redis.set(new_key, value)
        redis.expire(new_key, redis.ttl(old_key))

Gradual rollouts exist for a reason: Even "simple" changes can have massive impact

Monitor cache hit rates: This should have been in my deployment checklist

The Real Damage Report

• 2 hours of degraded performance: Users were not happy
• $200 in extra AWS costs: Emergency database scaling
• 47 support tickets: All variations of "your site is broken"
• 1 very uncomfortable conversation: With someone who signs my paychecks
• My ego: Thoroughly humbled

The Silver Lining

This incident led to:

• Better cache monitoring and alerting
• A proper cache warming strategy
• Deployment checklists that include cache considerations
• A great story for "What I Broke Wednesday"

The Moral of the Story

Just because something looks cleaner doesn't mean it's better. Sometimes "ugly" code that works is infinitely better than "beautiful" code that breaks everything.

Also, cache invalidation is still one of the two hard problems in computer science. I learned this the expensive way.

What's your most embarrassing cache/database mistake? Share your war stories in the comments - misery loves company, and we all need to learn from each other's disasters!

Tomorrow: Throwback Thursday (the time I added indexes everywhere)

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Part of the 🌈 Daily Dev Doses series - because every bug is a lesson in disguise (expensive lessons, but lessons nonetheless)