Async Techniques and Examples in Python Transcripts
Chapter: Parallelism in C with Cython
Lecture: C and Python are friends
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When you talk about Python in performance
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it's really interesting because
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Python code itself is kind of slow compared to languages like
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C#, Java, definitely C++, things like that.
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So, you say well this code is slow.
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But you can actually write a whole bunch more functionality
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with way fewer bugs really quickly.
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And then you can try to make it faster.
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A lot of the stuff we talked about with parallelism
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it lets us do many things faster
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and if you're waiting on other systems, perfect.
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Maybe though, you're working with something computational.
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You might bring in some library like NumPy
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for data science or numerical computations.
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And that really mixes up the performance
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because the internals in NumPy are written in C.
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And it's highly optimized.
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So, maybe you're version written in C
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would actually be slower than Python's version using NumPy.
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I don't know. This is a hypothetical, right?
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Another way to think about this is
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certain parts of your Python code are really critical
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and most of them aren't.
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So, if you think about SQLAlchemy
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there's a little tiny internal bit
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that is written in C that goes really, really fast
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and then the rest of it you'd do in Python.
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But that's not the slow part.
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The slow part is actually not in Python, its in C.
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So, this Python, C integration
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is really interesting and really important.
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Now, you may be thinking, Micheal.
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I either don't know C or C++ or I
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hate working in C and C++ that's why I'm a Python developer.
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Fine, I'm with you. It's been a long time since I been
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a professional C++ developer. A really long time.
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What we're going to talk about here
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is actually how we can take advantage of this
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performance and integration that we get with
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Python's great compatibility with C
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without actually writing any C code at all.
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Sounds perfect doesn't it?
