The problem gets worse in pipelines. When you chain multiple transforms — say, parse, transform, then serialize — each TransformStream has its own internal readable and writable buffers. If implementers follow the spec strictly, data cascades through these buffers in a push-oriented fashion: the source pushes to transform A, which pushes to transform B, which pushes to transform C, each accumulating data in intermediate buffers before the final consumer has even started pulling. With three transforms, you can have six internal buffers filling up simultaneously.
That’s a similar amount of CPU usage as when we started - but I’m running with 250 users, not 10. 25 times faster isn’t bad. With this setup, I’m able to support about 2,500 concurrent users before I start to see any stuttering.
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Web streams do provide clear mechanisms for tuning backpressure behavior in the form of the highWaterMark option and customizable size calculations, but these are just as easy to ignore as desiredSize, and many applications simply fail to pay attention to them.