If I were the IT lead for Black Friday:
Prepping for peak season e-commerce catastrophes
Category: General, Digital Experience monitoring
Published on: Nov 21, 2025
7 minutes
If I were an IT professional heading into Black Friday 2025, I wouldn't just be monitoring my systems, I'd be thinking like a disaster prevention specialist. We all know Black Friday has changed. It used to just be a single crazy morning of doorbusters, but now? It's stretched out into this big, weeks-long online event. And trust me, if your website's tech breaks down during that stretched-out rush, the consequences are bigger and more painful than ever.
Why Digital Experience Monitoring (DEM) and risk analysis would be my top priority
With global online spending hitting $74.4 billion in 2024 and US e-commerce sales reaching $10.8 billion, the tolerance for tech glitches is close to zero. A single minute of downtime could cause a loss in revenue by anywhere from $10,000 to over $23,000! For giant enterprises, that number is even scarier, easily blowing past $16,000 every 60 seconds.
Forget predictable traffic volume. The real problem is the sudden, unpredictable surge. Last Black Friday saw spending hitting $11.3 million per minute between 10 a.m. and 2 p.m. If I were an IT engineer responsible for ensuring availability of the apps, this entropy of surge is what would keep me up at night. When peak spending jumps to $11.3 million per minute in a single hour, that's not a gentle climb, it’s an abrupt burst. You need systems that can see those shockwaves coming, because any infrastructure that isn't running proactive monitoring and predictive analytics will simply get crushed.
Before diving into solutions, let us try to understand what can actually go wrong. Looking back at history, we can see patterns. Let's do a quick check on the Black Friday numbers:
Quick reality check (numbers you can't ignore)
The holiday season is fun but stressful. Here are some fun stats to add more stress to your plate - the real numbers that define the risk(financial and security) for your infrastructure.
- Adobe reported $10.8B in U.S. online Black Friday sales (2024) and measured peak spending of about $11.3 million per minute during the busiest hours. That means minutes of downtime can translate to tens of millions in lost top line. Source: Blackfriday Statistics 2024
- The cost of downtime varies by company size but is substancial: Atlassian’s quick model suggests thousands of dollars per minute for medium/large businesses, and industry analyses put per-minute costs into the hundreds to many thousands of dollars per minute range depending on scale. Use your org’s per-minute revenue to compute real exposure.
- Weapons-grade traffic: You have to assume the worst. Last year, Cloudflare reported handling trillions of requests daily, confirming that a good chunk of that was malicious traffic and sophisticated attacks.
- Every year, history shows the ecosystem suffers outages across platforms, payment gateways, and CDNs. Meaning, if your critical third-party services like Shopify or a payment processor break, your revenue breaks. Monitoring your own infrastructure isn't enough, you need visibility into those external risk vectors too.
That's a bit depressing, but thankfully, this isn't a problem without a solution. Here's what I'd do differently this year to prevent the mishaps that have plagued retailers in the past.
Understanding the 5 Black Friday disaster scenarios
In 2022, Nike's website crashed for over 4 hours. In 2020, the websites of Adidas, Petsmart, Etsy, and PlayStation all experienced severe performance degradation. The cost wasn't just the immediate revenue loss, it was the brand damage, the customer frustration, and the long-term impact on customer retention.
These weren't random failures. They were the result of known, preventable problems that went undetected until they cascaded into full outages. Let me break down the scenarios I'd focus on preventing:
Scenario 1: The infrastructure bottleneck
Here’s why building my apps/sites for average traffic will lead to immediate failure during peak demand:
| Aspect | Description |
|---|---|
| Root cause |
|
| User impact | Total downtime / HTTP 503 errors
The site becomes unresponsive or serves error pages. If the checkout service crashes, all attempts to finalize purchases fail, leading to immediate revenue loss and severe brand damage. |
| Monitoring blind spot | Lack of predictive capacity planning
The monitoring system tracks current utilization (reactive) instead of using historical data and machine learning to forecast capacity needs (proactive). |
Scenario 2: The database lethargies
Slow database queries don't typically cause immediate outages, they cause gradual performance degradation. Here's a scenario of how a slow, internal breakdown often caused by inefficient data access under heavy load, can make a site feel broken:
| Aspect | Description |
|---|---|
| Root cause |
Slow database queries, high table contention, or an exhausted connection pool, often triggered by atypical peak-season search queries or complex reporting running during peak hours. This consumes excessive CPU and I/O resources across the cluster. |
| User impact |
Severe latency / Transaction failure
Users experience timeouts, shopping carts fail to update, and the checkout process hangs for extended periods (eg:., P99 latency hits 5,000ms). The site appears crashed to the user. |
| Monitoring blind spot | Inadequate distributed tracing
The monitoring system confirms the application tier is slow but fails to pinpoint the exact line of inefficient SQL or the specific database lock causing the system-wide slowdown. |
Scenario 3: The third-party plugin mishaps
Many e-commerce platforms rely on third-party plugins for payment processing, inventory management, reviews, and analytics. If one of these plugins fails or becomes a bottleneck, the entire platform can suffer. I've seen scenarios where a single broken plugin consumed so much bandwidth or processing power that legitimate traffic couldn't get through. Here is how external services can unintentionally cripple your entire operation during peak hours:
| Aspect | Description |
|---|---|
| Root cause | Failure of external services (payment processing, inventory sync, reviews, analytics) or an unoptimized plugin that consumes excessive local resources. When this happens, the application waits indefinitely for the external response, which leads to excessive consumption of threads and prevents legitimate traffic flow. |
| User impact | Checkout failure / Partial service degradation
Customers cannot complete payment, see outdated inventory levels, or experience extreme slowdowns during page loading due to external script delays. Revenue stalls at the most critical point. |
| Monitoring blind spot | Poor external SLI monitoring
The platform lacks synthetic transaction checks and real-time monitoring of external dependency response times. The issue is discovered only when internal errors spike, long after the external service began failing. |
Scenario 4: The network choke points
Even if servers have capacity, insufficient bandwidth or network misconfiguration can prevent traffic from flowing efficiently. This scenario details how server health can be misleading when the network itself is the bottleneck:
| Aspect | Description |
|---|---|
| Root cause |
Network inefficiencies that hide the true bottleneck, such as unoptimized DNS lookups, poorly configured Content Delivery Networks (CDNs), insufficient load balancer capacity, or network card limits. These issues do not show up as high CPU on the application hosts. |
| User impact | Intermittent page load errors / High latency
Pages load very slowly or fail to load intermittently across different regions, impacting all users regardless of server resource availability. |
| Monitoring blind spot | CDN origin request spikes
The team monitors the servers but ignores the traffic pattern between the CDN and the origin servers, failing to catch sudden spikes in cache misses or abusive client patterns that overload the core network layer. |
Scenario 5: The security attacks
And finally, the security attacks. During Black Friday, the site attracts not just legitimate shoppers but malicious actors. What appears to be an infrastructure failure can actually be a security incident.
| Aspect | Description |
|---|---|
| Root cause |
Malicious actors using Distributed Denial-of-Service (DDoS) attacks, sophisticated bot traffic, or brute force attempts consume connection pools, bandwidth, and application resources, exhausting the Web Application Firewall (WAF) or other defenses. |
| User impact | Resource exhaustion / Denial of service
Legitimate customers are unable to connect or complete transactions because all available resources are dedicated to handling illegitimate traffic. The application is effectively shut down by external threat actors. |
| Monitoring blind spot | Lack of security context in APM
The team diagnoses the failure as a simple capacity problem (CPU spike) rather than recognizing the specific signature of the bot traffic or DDoS attack, leading to scaling up the problem instead of mitigating the threat. |
Phew, the repercussions are many, but the solution is one — monitoring your user's digital experience. But first we need to cover what to monitor in these scenarios. Check out part 2 of this series to find out how I'd assemble my monitoring strategy for Black Friday!
