节前的某天，数据集预览服务出现了一次 OOM（内存溢出）问题。这类问题放在过去，其实是比较消耗时间的。 数据集预览涉及多种格式解析：jsonl、csv、parquet、json 等，每种格式的读取方式、内存占用模型都不一样。要逐个排查内存增长点，分析数据加载策略、对象生命周期以及是否存在全量读入等问题，通常至少需要 1 天时间。
What follows is a proof of concept — not a finished standard, not a production-ready library, not even necessarily a concrete proposal for something new, but a starting point for discussion that demonstrates the problems with Web streams aren't inherent to streaming itself; they're consequences of specific design choices that could be made differently. Whether this exact API is the right answer is less important than whether it sparks a productive conversation about what we actually need from a streaming primitive.
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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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