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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.

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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.

Hot Topics

The Latest

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

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AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

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