实时直播活动推荐幕后:第三部分

By: Kris Range, Ankush Gulati, Jim Isaacs, Jennifer Shin, Jeremy Kelly, Jason Tu

作者:Kris RangeAnkush GulatiJim IsaacsJennifer ShinJeremy KellyJason Tu

This is part 3 in a series called “Behind the Streams”. Check out part 1 and part 2 to learn more.

这是名为“Behind the Streams”的系列的第三部分。查看 第一部分 第二部分 以了解更多信息。

Picture this: It’s seconds before the biggest fight night in Netflix history. Sixty-five million fans are waiting, devices in hand, hearts pounding. The countdown hits zero. What does it take to get everyone to the action on time, every time? At Netflix, we’re used to on-demand viewing where everyone chooses their own moment. But with live events, millions are eager to join in at once. Our job: make sure our members never miss a beat.

想象一下:在Netflix历史上最大的比赛之夜前的几秒钟。六千五百万名粉丝手握设备,心跳加速。倒计时归零。每次都能准时让每个人参与到行动中需要什么?在Netflix,我们习惯于按需观看,每个人选择自己的时刻。但在直播事件中,数百万观众渴望同时参与。我们的工作是:确保我们的会员不会错过任何精彩瞬间。

When Live events break streaming records ¹ ² ³, our infrastructure faces the ultimate stress test. Here’s how we engineered a discovery experience for a global audience excited to see a knockout.

当直播事件打破流媒体记录时¹ ² ³,我们的基础设施面临最终的压力测试。以下是我们如何为全球观众设计一个期待看到击倒的发现体验。

Why are Live Events Different?

为什么直播活动不同?

Unlike Video on Demand (VOD), members want to catch live events as they happen. There’s something uniquely exciting about being part of the moment. That means we only have a brief window to recommend a Live event at just the right time. Too early, excitement fades; too late, the moment is missed. Every second counts.

与点播视频(VOD)不同,会员希望在事件发生时观看直播活动。参与这一时刻有一种独特的兴奋感。这意味着我们只有一个短暂的窗口来在恰当的时刻推荐直播活动。太早,兴奋感消退;太晚,时刻错过。每一秒都很重要。

To capture that excitement, we enhanced our recommendation delivery systems to serve real-time suggestions, providing members richer and more compelling signals to hit play in the moment when it matters most. The challenge? Sending dynamic, timely updates concurrently to over a hundred million devices worldwide without creating a thundering herd effect that would overwhelm our cloud services. Simply scaling up linearly isn’...

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