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Anticipating Traffic Surges - Lessons Learned from ESPN Crash

Michelle McLean

ESPN made news headlines this past weekend – the bittersweet kind. Unfortunately, the news highlighted that ESPN's fantasy football app was crashing, on the first Sunday of the NFL season. Where's the "sweet" part? The crash likely signals a huge amount of user popularity.

We see these types of stories often during so-called "surge" events, like when Black Friday takes down a retailer. Why? Often, it's the database that's been swamped in the process.

The application-to-database connection is fragile, because applications have to directly tie into the database and the coding of the app must match the database infrastructure. For example, if the database has multiple database servers that can all respond to an inbound request, the application needs to know which type of server to send its request to. While those changes can ensure a better response time, the work isn't trivial – a programmer must go through hundreds of thousands of lines of code to program how to handle reads vs. writes – and it can lead to errors.

Any recent changes by ESPN to increase database capacity or update the app could jeopardize that fragile connection. If ESPN recently modified the application to talk to different database servers, for example, the team might have accidentally introduced a "bad" query that the database can't handle or might have changed how the application talks to the database and broken that connection.

Organizations that are anticipating a surge in traffic have a number of best practices they should follow to ensure a smooth experience for their customers, including:

1. Freezing code early

Despite the understandable desire to make the app or site as current as possible, it's essential for engineering to force a code freeze many weeks before the "go live" date. Quality assurance (QA) and other testing require adequate time to ensure the updated site or app is working as needed.

2. Load testing

A big part of that testing work needs to come in the form of load testing. After a QA team has performed functional testing – that is, does each feature work – the next step is to see how the code performs when it's swamped with traffic. The key is to perform this load testing with traffic that's as close to production traffic as possible.

3. Increasing resiliency at the data tier

The lifeblood of any app or site is data; without it, you're down. To build in resiliency at this layer, organizations need to employ techniques such as database scale out to have multiple copies of the data available and database load balancing to ensure traffic is serviced by the fastest-responding server to the user.

4. Enabling redundancy in all network services

Beyond the data tier, organizations need to make sure the rest of the technology stack has all the redundancy built in as possible. Web server infrastructure and web load balancers are critical, as is network redundancy into both the web farms and the database server clusters. If you're hosting the app or service in the cloud, ensure a redundant version is available in an alternate cloud region.

Michelle McLean is VP of Marketing at ScaleArc.

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Anticipating Traffic Surges - Lessons Learned from ESPN Crash

Michelle McLean

ESPN made news headlines this past weekend – the bittersweet kind. Unfortunately, the news highlighted that ESPN's fantasy football app was crashing, on the first Sunday of the NFL season. Where's the "sweet" part? The crash likely signals a huge amount of user popularity.

We see these types of stories often during so-called "surge" events, like when Black Friday takes down a retailer. Why? Often, it's the database that's been swamped in the process.

The application-to-database connection is fragile, because applications have to directly tie into the database and the coding of the app must match the database infrastructure. For example, if the database has multiple database servers that can all respond to an inbound request, the application needs to know which type of server to send its request to. While those changes can ensure a better response time, the work isn't trivial – a programmer must go through hundreds of thousands of lines of code to program how to handle reads vs. writes – and it can lead to errors.

Any recent changes by ESPN to increase database capacity or update the app could jeopardize that fragile connection. If ESPN recently modified the application to talk to different database servers, for example, the team might have accidentally introduced a "bad" query that the database can't handle or might have changed how the application talks to the database and broken that connection.

Organizations that are anticipating a surge in traffic have a number of best practices they should follow to ensure a smooth experience for their customers, including:

1. Freezing code early

Despite the understandable desire to make the app or site as current as possible, it's essential for engineering to force a code freeze many weeks before the "go live" date. Quality assurance (QA) and other testing require adequate time to ensure the updated site or app is working as needed.

2. Load testing

A big part of that testing work needs to come in the form of load testing. After a QA team has performed functional testing – that is, does each feature work – the next step is to see how the code performs when it's swamped with traffic. The key is to perform this load testing with traffic that's as close to production traffic as possible.

3. Increasing resiliency at the data tier

The lifeblood of any app or site is data; without it, you're down. To build in resiliency at this layer, organizations need to employ techniques such as database scale out to have multiple copies of the data available and database load balancing to ensure traffic is serviced by the fastest-responding server to the user.

4. Enabling redundancy in all network services

Beyond the data tier, organizations need to make sure the rest of the technology stack has all the redundancy built in as possible. Web server infrastructure and web load balancers are critical, as is network redundancy into both the web farms and the database server clusters. If you're hosting the app or service in the cloud, ensure a redundant version is available in an alternate cloud region.

Michelle McLean is VP of Marketing at ScaleArc.

Hot Topics

The Latest

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 ...

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 ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

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 ...

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 ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

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 ...

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 ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

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.