The Outage That Made Us Stronger
How chaos engineering saved our payment system before the next disaster
Hi, I'm Priya.
Monday, October 20th started at 12:18 AM with my phone buzzing on my nightstand.
I grabbed it, squinting at the screen. Slack notifications. Dozens of them. Our CEO in #general:
AWS us-east-1 is DOWN. All hands. NOW.
I threw on clothes and rushed to the office. By the time I got there at 1:30 AM, most of the engineering team was already at their desks. Someone had made a pot of bad coffee. No one had touched it.
For the next six hours, we watched our dashboards bleed red. Payment processing frozen. Customer transactions queued but not completing. Support tickets piling up faster than we could respond.
Being on the West Coast, we lived through every single minute of it.
By the time AWS declared it resolved Tuesday morning, we'd logged over $200K in delayed transactions and countless hours of customer frustration.
The Tiger Team
Wednesday morning, our CTO Marcus called an emergency all-hands.
Marcus had joined DataFlow a year ago after spending nearly a decade at AWS—first as a developer, then as a DevOps engineer working on some of their core infrastructure services. When he spoke about distributed systems, people listened.
"We got lucky," he said, his voice steady but serious. "AWS fixed it before we lost customers. But what happens next time?"
That's when he announced the tiger team. Five engineers. One week. Mission: audit our entire AWS infrastructure and eliminate single points of failure.
I got assigned payment processing—the system I'd inherited when I joined eight months ago. It had been running smoothly for three years. No one had touched it much. If it ain't broke, don't fix it, right?
Except now I needed to find out how broke it actually was.
My teammate Jake was reviewing our database failover strategy. Sarah was auditing API gateway redundancy. Chen was checking our monitoring and alerting systems. And Mika was reviewing our deployment pipeline resilience.
We had five days.
The Nervous Days
Here's what nobody talks about: even after AWS said everything was fixed, the internet felt... wrong.
Page loads were sluggish. API calls that used to take 200ms were randomly spiking to 2 seconds. Nothing was officially broken, but everything felt fragile.
Thursday afternoon, Marcus stopped by my desk. "How's it going?"
I'd been staring at the payment processing codebase for hours. My mind kept drifting to my friend Maya from college—brilliant engineer, worked at a promising fintech startup in Boston. Last year, their payment system went down during Black Friday. The company folded three months later. Maya's still looking for work.
Another friend, Dev, had been at a startup that lost a major client after a multi-hour outage. The company survived, but they laid off half the engineering team. Dev included.
I shook off the thoughts. "Found some things that worry me," I said, pulling up the code. "Hard-coded us-east-1 regions. No retry logic. Synchronous calls everywhere."
Marcus leaned in, studying the screen. His expression darkened. "Show me."
# Payment processor initialization
s3_client = boto3.client('s3', region_name='us-east-1')
dynamodb = boto3.resource('dynamodb', region_name='us-east-1')
sqs = boto3.client('sqs', region_name='us-east-1')
"Yeah," he said quietly. "That's what I was afraid of. I've seen this pattern before at AWS. Systems that work perfectly until they catastrophically don't."
"How bad is it?" I asked.
Marcus pulled up a chair. "Upper management is nervous. The board called an emergency meeting yesterday. The lingering latency—nobody's talking about it publicly, but everyone feels it. They're worried we're one hiccup away from another disaster." He paused. "We need this tiger team to find everything."
That's when the weight of it hit me. This wasn't just a code review. This was our company's survival plan.
The Chaos Experiment
Friday morning, I installed AWS Fault Injection Simulator and configured my first experiment: simulate a 60-second us-east-1 API latency spike.
I messaged Sarah on Slack: "Running chaos tests in staging. Want to watch?"
She pulled up a chair as I clicked "Start Experiment."
Within 15 seconds, our payment dashboard went red. Transactions timing out. Error rates spiking to 100%. The system didn't gracefully degrade—it just died.
"Oh no," Sarah said quietly.
"Yeah."
I ran a second experiment: complete us-east-1 unavailability for 2 minutes.
Same result. Total system failure.
The system that had run "smoothly" for three years was a house of cards. It had just never been tested against real-world failure scenarios.
The Weekend
I spent Saturday rebuilding the resilience layer. Multi-region support. Exponential backoff. Circuit breakers.
My desk was a disaster—three empty coffee cups, scattered notes, a whiteboard filled with system diagrams that I'd drawn and erased and redrawn.
By Saturday night, around 11 PM, I thought I had it. I ran the chaos test.
It failed. Worse than before.
My "fix" had created a race condition in the failover logic. The circuit breaker timing was wrong. Requests were getting trapped in a loop between regions.
I stared at the error logs, my hands actually shaking. This was exactly how it started for Maya. One bad architectural decision, then another trying to fix the first, then a cascading failure in production.
I reached for my phone to call Marcus. My finger hovered over his number.
Then I stopped. Put the phone down.
I stood up. Walked to the kitchen. Filled a water glass. Stood at the window overlooking downtown Seattle, watching the city lights blur through my exhausted eyes.
You can do this. You know how to do this.
I came back to my desk with fresh eyes.
That's when I saw it. The circuit breaker was checking health before attempting failover instead of during. A simple ordering problem, but it had cascading effects.
I rewrote the region failover logic:
# Multi-region configuration with automatic fallback
regions = ['us-west-2', 'us-east-1', 'eu-west-1']
def get_s3_client():
for region in regions:
try:
client = boto3.client('s3', region_name=region)
# Test the connection
client.list_buckets()
return client
except Exception:
continue
raise Exception("All regions unavailable")
Then implemented proper retry logic:
def process_payment(transaction_id, max_retries=3):
for attempt in range(max_retries):
try:
response = payment_api.charge(transaction_id)
return response
except Exception as e:
if attempt == max_retries - 1:
logger.error(f"Payment failed after {max_retries} attempts")
return None
wait_time = (2 ** attempt) + random.uniform(0, 1)
time.sleep(wait_time)
Sunday afternoon, Jake called me. "How's it going?"
"I think I've got it," I said. "Can you run your worst-case database failure scenario against my staging branch?"
Ten minutes later: "Priya. Your payment system stayed up while my database was completely offline. How?"
"Multi-region architecture," I explained. "Failed payments get queued in SQS—regional queues in us-west-2, us-east-1, and eu-west-1. DynamoDB Global Tables with automatic replication. If one region goes down, Route 53 fails traffic over to a healthy region. The queues process automatically when the database recovers, or immediately in the healthy region."
Jake whistled softly. "That's... really good work."
The Validation
Monday morning, I ran the chaos experiments again.
60-second latency spike? System stayed online. Transactions slowed but completed. Error rate stayed under 2%.
Complete us-east-1 outage? Automatic failover to us-west-2. The dashboard stayed green.
I called Sarah over. "Run your worst-case scenario against this."
She configured a brutal test: simultaneous API gateway degradation and regional latency. Hit "Execute."
Green across the board.
"Priya," she said, smiling, "this is really solid."
The Presentation
Tuesday morning, our tiger team presented to leadership. The entire exec team was there. The board had dialed in remotely.
Jake showed database replication improvements. Sarah demonstrated API redundancy with automatic DNS failover. Chen walked through the new alerting thresholds that would catch degradation before it became an outage. Mika proved our deployment pipeline could handle zone failures.
Then it was my turn.
I showed the before/after chaos test results. The old system failing spectacularly. The new system holding steady under every failure scenario I could simulate.
Six hours of downtime prevented. Automatic regional failover. Zero customer impact during simulated disasters.
The room was silent for a moment.
Then Marcus stood up. "I spent nine years at AWS," he said, addressing the board. "I've seen billion-dollar systems fall over from exactly these kinds of architectural gaps. What this team built in five days—this is enterprise-grade resilience."
He looked directly at me. "What Priya built here would pass an AWS Well-Architected Review. We can breathe now."
The room erupted.
Not polite applause. Roaring applause. Sarah was on her feet, grinning. Jake pumped his fist. Chen was clapping so hard his laptop nearly fell off the table. Our CEO stood, then the CFO, then the entire executive team.
Someone from the board—our lead investor—unmuted herself on the video call. "That was exceptional work. All of you."
I felt a breath leave my body that I didn't realize I'd been holding for five days. My hands were still shaking slightly, but now it was relief, not fear.
Marcus caught my eye across the room and nodded. Just once. But I knew what it meant.
You did it. You saved us.
After the meeting, the tiger team grabbed lunch at the café downstairs. Jake bought the first round of coffees. Sarah had already screenshot my chaos test results to show her team. Chen was already thinking about applying the same patterns to our monitoring stack.
"To the tiger team," Mika said, raising her coffee cup.
We clinked cups like champagne glasses.
It felt exactly like those videos you see of mission control when a spacecraft lands safely. The exhale. The celebration. The moment when you realize: We didn't just survive. We're actually stronger now.
What I Learned
The scariest systems aren't the ones that fail spectacularly. They're the ones that run smoothly for years—until they don't.
Our payment processor had worked perfectly because it had never been tested against reality. We'd never asked: What if AWS goes down? What if there's latency? What if our primary region becomes unavailable?
I thought about Maya and Dev that week. Good engineers at good companies who got caught in the blast radius of architectural decisions they didn't make. It could have been us. It could have been me updating my LinkedIn profile with "open to work" while trying to explain why our payment system catastrophically failed.
But it wasn't.
Because we asked the hard questions before production forced us to.
Chaos engineering doesn't break systems. It reveals where they're already broken. It gives you the chance to fix things in a controlled environment instead of during a customer-facing crisis.
Having Marcus's support made all the difference. When I got stuck Saturday night, I knew I could ask for help. But I also knew he trusted me to figure it out. That's what good leadership looks like—giving you space to grow while being there when you need it.
Now, before deploying any infrastructure change, I ask: What happens when this fails? Not if. When.
Because in distributed systems, everything fails eventually. The question is whether you discover it in a controlled test or during a customer-facing outage.
Monday, October 20th taught us we'd been lucky.
But we don't rely on luck anymore.
Aaron Rose is a software engineer and technology writer at tech-reader.blog and the author of Think Like a Genius.
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