"When I manage infrastructure for major events (whether it is the Olympics, a Premier League match or a season finale) I am dealing with a "thundering herd" problem that few systems ever face. Millions of users log in, browse and hit "play" within the same three-minute window. But this challenge isn't unique to media. It is the same nightmare that keeps e-commerce CTOs awake before Black Friday or financial systems architects up during a market crash. The fundamental problem is always the same: How do you survive when demand exceeds capacity by an order of magnitude?"
"Most engineering teams rely on auto-scaling to save them. But at the "Super Bowl standard" of scale, auto-scaling is a lie. It is too reactive. By the time your cloud provider spins up new instances, your latency has already spiked, your database connection pool is exhausted and your users are staring at a 500 error. Here are the four architectural patterns we use to survive massive concurrency. These apply whether you are streaming touchdowns or processing checkout queues for a limited-edition sneaker drop."
"The biggest mistake engineers make is trying to process every request that hits the load balancer. In a high-concurrency event, this is suicide. If your system capacity is 100,000 requests per second (RPS) and you receive 120,000 RPS, trying to serve everyone usually results in the database locking up and zero people getting served. We implement load shedding based on business priority. It is better to serve 100,000 users perfectly and tell 20,000 users to "please wait" than to crash the site for all 120,000."
Massive real-time events create thundering herd scenarios where millions of users act within minutes, driving demand well beyond capacity. Auto-scaling reacts too slowly; provisioning lag causes latency spikes, exhausted database connections, and 500 errors. Aggressive load shedding by business priority preserves service for core users and prevents total system collapse; serving capacity perfectly for high-priority requests is preferable to attempting to serve everyone and failing. Surviving extreme concurrency requires isolation of critical paths, capacity planning or reservation for peak events, and frequent, brutal game-day drills to validate behavior under real load.
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