Another Black Friday, Another eCommerce Meltdown

Michelle McLean

Black Friday. Retailers know it's coming every year, and still – every year – someone has a spectacular failure. This year Macy's gets top billing – asking customers to wait to shop. Since 500 milliseconds of web delay is estimated to cost 5% of revenue, how much can we guess Macy's lost by asking EVERY shopper, for hours, to wait to shop? It's clearly in the millions of dollars. And how many of those who clicked over to Nordstrom's or Kohl's in frustration will just keep shopping on those other sites?



 
So what did Macy's get wrong? Scaling infrastructure for big traffic increases is fairly easy across most technology areas. Organizations know how to scale WAN links, network infrastructure, and web servers. So what did Macy's miss? Likely, the database.

"You have handle 5x to 15x your usual traffic on Black Friday," says Craig Thayer, CTO of Sazze, parent company to numerous eCommerce websites including Black Friday FM. "Turns out the database is the hardest part of the infrastructure to scale fast, because you have to also make application changes. You change the code, iterate, test, rinse and repeat."
 
Often, when you can't reach a site or app during a busy time, it's the database that has hit a wall. Organizations of all sizes these days are rushing to take advantage of additional capacity in modern databases. Microsoft is pushing its SQL Server 2016 launch, and the open source world is embracing MySQL 5.6. Both modern databases offer more capacity and better failover, aimed at improving application uptime.

The challenge for organizations, as Sazze's Thayer points out, is that applications have to know how to talk to those databases. That takes time – and can't be done in rapid response in the middle of a Macy's meltdown during Black Friday. It's got to be done in advance.
 
Organizations have a couple choices for how to adopt these databases. They can recode their apps – teaching those apps how to send some traffic to additional database servers to spread out the load. Or they can use technology like they have for their web server farms – load balancing technology – in front of their databases and have that software redirect the database load automatically. The benefit of using database load balancing software is that it avoids the application recoding – and subsequent "rinse and repeat" cycles that Sazze's Thayer is keen to avoid. So that option can often be implemented faster than recoding an app and provides additional benefits such as seamless failover.

Black Friday often serves as a warning for the rest of the December online shopping spree. The hope is that companies that experienced – or watched others have – a Black Friday meltdown can scale their infrastructure in time to be ready for that holiday shopping traffic.

Michelle McLean is VP of Marketing at ScaleArc.

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Another Black Friday, Another eCommerce Meltdown

Michelle McLean

Black Friday. Retailers know it's coming every year, and still – every year – someone has a spectacular failure. This year Macy's gets top billing – asking customers to wait to shop. Since 500 milliseconds of web delay is estimated to cost 5% of revenue, how much can we guess Macy's lost by asking EVERY shopper, for hours, to wait to shop? It's clearly in the millions of dollars. And how many of those who clicked over to Nordstrom's or Kohl's in frustration will just keep shopping on those other sites?



 
So what did Macy's get wrong? Scaling infrastructure for big traffic increases is fairly easy across most technology areas. Organizations know how to scale WAN links, network infrastructure, and web servers. So what did Macy's miss? Likely, the database.

"You have handle 5x to 15x your usual traffic on Black Friday," says Craig Thayer, CTO of Sazze, parent company to numerous eCommerce websites including Black Friday FM. "Turns out the database is the hardest part of the infrastructure to scale fast, because you have to also make application changes. You change the code, iterate, test, rinse and repeat."
 
Often, when you can't reach a site or app during a busy time, it's the database that has hit a wall. Organizations of all sizes these days are rushing to take advantage of additional capacity in modern databases. Microsoft is pushing its SQL Server 2016 launch, and the open source world is embracing MySQL 5.6. Both modern databases offer more capacity and better failover, aimed at improving application uptime.

The challenge for organizations, as Sazze's Thayer points out, is that applications have to know how to talk to those databases. That takes time – and can't be done in rapid response in the middle of a Macy's meltdown during Black Friday. It's got to be done in advance.
 
Organizations have a couple choices for how to adopt these databases. They can recode their apps – teaching those apps how to send some traffic to additional database servers to spread out the load. Or they can use technology like they have for their web server farms – load balancing technology – in front of their databases and have that software redirect the database load automatically. The benefit of using database load balancing software is that it avoids the application recoding – and subsequent "rinse and repeat" cycles that Sazze's Thayer is keen to avoid. So that option can often be implemented faster than recoding an app and provides additional benefits such as seamless failover.

Black Friday often serves as a warning for the rest of the December online shopping spree. The hope is that companies that experienced – or watched others have – a Black Friday meltdown can scale their infrastructure in time to be ready for that holiday shopping traffic.

Michelle McLean is VP of Marketing at ScaleArc.

Related Links

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Escaping Pilot Purgatory: How AI Becomes an Operational Advantage

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

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The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

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The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

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AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.