Best Buy's Black Friday 2025 crash: what every retail team can learn about seasonal demand spikes

Black Friday is not a surprise. Retailers know it is coming, they know roughly when traffic will peak, and most engineering teams have been preparing for months. And yet, on November 28, 2025, Best Buy’s website and app went down for the better part of ninety minutes during one of the most commercially important windows of the year.

No official root-cause analysis was published. But the pattern itself — a predictable calendar peak overwhelming infrastructure that was not tested at the right scale — is one of the most common and most preventable categories of outage in e-commerce.

What happened

On the morning of Black Friday, November 28, 2025, Best Buy’s site and mobile app became unavailable to a significant portion of shoppers. The disruption lasted approximately 80 to 90 minutes, beginning around 9:40 a.m. ET and largely resolving by 11:50 a.m. ET according to reporting from the time.

Downdetector logged more than 1,800 problem reports at peak, with roughly 80 percent of reports pointing to the desktop website. Best Buy did not publicly release an explanation or postmortem. Reporting at the time attributed the disruption to the volume of Black Friday morning traffic, though no official confirmation of the cause was provided.

The timeline

• ~9:40 a.m. ET, November 28, 2025: Best Buy site and app reports begin surging on Downdetector.
• Peak outage window: Downdetector reaches over 1,800 reports; ~80% of complaints are desktop web.
• ~11:50 a.m. ET: Service largely restores — approximately 80–90 minutes after onset.
• No postmortem: Best Buy did not publish a public root-cause statement.

Why it happened

Because no official postmortem was released, any specific technical cause would be speculation. What reporting supports is that the disruption coincided with peak Black Friday morning traffic — the window when promotional deals go live and millions of shoppers simultaneously open the site.

That timing matters. Black Friday morning traffic does not ramp gradually. Deals are announced in advance, shoppers pre-load pages, and the moment deals go live — often at midnight or at a posted morning hour — traffic spikes sharply. If the production system was sized for sustained high load but not for that ramp rate, the spike itself could saturate connection pools, session stores, or upstream dependencies faster than auto-scaling can respond.

The reported concentration of failures on the desktop web path (versus app or mobile) suggests the failure may have been partial — possibly tied to web-tier infrastructure, a CDN routing issue, or a path that desktop traffic exercises differently from the app. Without a postmortem, these remain inferences.

The lesson for any retail team is that the absence of a known cause does not reduce the risk. Predictable calendar peaks create predictable opportunities to find your ceiling before shoppers find it for you.

The failure pattern

This is a seasonal demand spike — the most addressable category of load failure because the date, rough magnitude, and traffic shape are all known in advance. Unlike an unexpected viral moment, Black Friday has a scheduled start time and years of comparable historical data.

The pattern generalizes beyond retail: tax filing deadlines, enrollment windows, payday Fridays, scheduled results days, product launches with announced times. Any system that serves a predictable annual or cyclical peak is exposed if that peak has never been tested at realistic scale.

What makes seasonal spikes treacherous is the false confidence that comes from surviving previous years. Each year brings more users, more devices, new code paths, and changed infrastructure. Last year’s success does not mean this year’s capacity is sufficient.

How it could have been prevented

Load-test the actual calendar peak, not just an average day. Historical Black Friday traffic data (or a conservative estimate above it) should drive the test scenario. Testing at 120–150% of last year’s peak adds reasonable headroom for growth.

Test the full user journey under concurrent load. Storefront reads are usually the easiest path to scale. The login, cart-add, and checkout flows — which require session state, write operations, and often third-party calls (payment processors, loyalty APIs) — saturate earlier. Include them in every Black Friday test.

Test the ramp rate, not just the peak. A system can sustain 100,000 concurrent users at steady state but fail if 80,000 new connections arrive in 90 seconds. Spike tests that jump to peak in minutes reveal this; gradual ramp tests do not.

Pre-scale before deals go live. Auto-scaling triggered by the actual traffic spike may lag behind the ramp by minutes — long enough to cause visible degradation. Provision excess capacity an hour before the event and scale down afterward.

Have a rollback plan. If a new feature shipped for the Black Friday window is contributing to the overload, the fastest mitigation is usually feature rollback, not infrastructure scaling.

How to test for this with MaxoPerf

A seasonal demand spike calls for a spike test scaled to your expected Black Friday peak, run weeks before the event — not days.

Engine: k6 or Taurus (JMeter)

Profile: open-model spike — ramp from your current daily peak to your Black Friday peak concurrency target over 5–10 minutes (matching the real morning ramp), hold for 20–30 minutes, then check whether requests per second throughput stays proportional to the load or plateaus (a plateau means you’ve hit a ceiling). Add a secondary spike at a higher level to test your headroom.

Target: your own staging or pre-production environment — running the full sequence: homepage load → category page → product search → product detail → login/account check → add to cart → begin checkout. Do not omit the login path even if your main concerns are storefront pages — session creation is frequently the first bottleneck under concurrent load.

Locations: use at least two managed cloud regions to model shoppers from different geographies arriving simultaneously. If your storefront sits behind a private edge or your staging environment is not internet-reachable, add a private/BYOC location.

What to watch in results:

• Requests per second on the login and cart-add labels — if these plateau while virtual user count climbs, your session or write path is saturating.
• p95 latency across all labeled flows — a checkout p95 that stays flat until 60,000 virtual users then doubles tells you where your knee is.
• Error rate by endpoint — sudden 5xx or timeout spikes on a specific path narrow the investigation immediately.
• Run artifacts for downstream dependency errors (payment tokenization, loyalty lookups, inventory checks).

Failure criteria: set thresholds before the run — for example, error rate must stay below your tolerance and p95 checkout latency must remain under your agreed limit. MaxoPerf records the pass/fail result so you have a documented decision, not just a chart.

Schedule this test six to eight weeks before Black Friday. If the result reveals a ceiling, you have time to address it — whether that means capacity provisioning, query optimization, or architectural changes to the bottleneck path. Re-run two weeks out to confirm the fix held.

For integrating performance checks into your release pipeline in the weeks leading up to Black Friday, see the CI/CD performance gates pattern.

Key takeaways

• Seasonal spikes are the most preventable category of load failure — the date, scale, and traffic shape are all known ahead of time.
• Surviving last year is not evidence you’ll survive this year; infrastructure and code change continuously.
• Test the ramp rate as well as the peak — fast arrival under a new deal window saturates systems differently than a gradual ramp.
• Login and session paths saturate before read-only storefront paths; include them in every Black Friday test scenario.
• Pre-scaling before the event window is more reliable than relying on auto-scaling triggered by the spike itself.

Ready to test your storefront before the next seasonal peak? Run a Black Friday spike simulation on MaxoPerf using your own environment — before your shoppers find your ceiling for you.