Async Techniques and Examples in Python Transcripts
Chapter: Why async?
Lecture: Async for scalability
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Next up, let's focus on how we can use asynchronous or concurrent programming for scalability. I want to take just a moment
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to address this word, scalability. Often, people sort of think of scalability as performance and performance equaling speed and things being faster.
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And that's not exactly what it means. Let's think in terms of websites here. That's not the only time we might think of this scalability concept.
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But let's think in terms of websites. If your website can process individual requests 10 times faster, it will be more scalable
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because it won't back up as much, right? Scalability doesn't refer to how fast an individual request can be
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it refers to how many requests can your website handle or your system handle, until its performance degrades.
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And that degrade might be just really long request times before you get back to someone or the browser.
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It might mean it's so bad that requests start timing out. It might mean that your system actually crashes. There's some point where your system
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degrades in performance. And when you talk about how scalable is a system it's how much concurrent processing?
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How many requests at one time can it handle until it starts to degrade? As we add scalability to the system we might even make it a tiny bit slower
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for an individual request. There's probably a little bit more work we're doing to add this capability to scale better, maybe.
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It's not exactly talking about individual request speed because that actually might get worse. However, it means maybe we could handle
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10 times as many concurrent users or 100 times as many concurrent users on exactly the same hardware. That's what we're focused on with scalability.
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How do we get more out of the same hardware? And we'll see that Python has a couple of really interesting ways to do that.