Chapter 7. Compiling to C
The easiest way to get your code to run faster is to make it do less work. Assuming you’ve already chosen good algorithms and you’ve reduced the amount of data you’re processing, the easiest way to execute fewer instructions is to compile your code down to machine code.
Python offers a number of options for this, including pure C-based compiling approaches like Cython; LLVM-based compiling via Numba; and the replacement virtual machine PyPy, which includes a built-in just-in-time (JIT) compiler. You need to balance the requirements of code adaptability and team velocity when deciding which route to take.
Each of these tools adds a new dependency to your toolchain, and Cython requires you to write in a new language type (a hybrid of Python and C), which means you need a new skill. Cython’s new language may hurt your team’s velocity, as team members without knowledge of C may have trouble supporting this code; in practice, though, this is probably a minor concern, as you’ll use Cython only in well-chosen, small regions of your code.
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