“Let’s use Kubernetes.” Now you have eight problems
For smaller teams, Kubernetes is usually the wrong solution: too complex, too complicated, and with too much work to keep it running.
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For smaller teams, Kubernetes is usually the wrong solution: too complex, too complicated, and with too much work to keep it running.
You can process data that doesn’t fit in memory by using four basic techniques: spending money, compression, chunking, and indexing.
Most Dockerfile examples for Python you’ll find on the Web are broken. And that’s a problem.
Alpine Linux is often recommended as a smaller, faster Docker base image. But if you’re using Python, it will slow down your build and make your image larger.
Copying data wastes memory, and modifying/mutating data can lead to bugs. Learn how to implement a compromise between the two in Python: hidden mutability.
The “official” Python Docker image is useful, but to actually understand why, and to use it correctly, it’s worth understanding how exactly it’s constructed.
Figuring out how much parallelism your program can use is surprisingly tricky.
Python-based calculations, especially those that use NumPy, can run much faster by using the Numba library.
Pydantic’s JSON loading uses a huge amount of memory; here’s how to reduce it.
Learn how to load larger-than-memory NumPy arrays from disk using either mmap() (using numpy.memmap), or the very similar Zarr and HDF5 file formats.
Measuring your Python program’s memory usage is not as straightforward as you might think. Learn two techniques, and the tradeoffs between them.
Compilers have a hard time optimizing floating point math. Rust has a new API to fix this.
If you’ve ever been alarmed by how many security vulnerabilities your Docker image has, even after you’ve installed security updates, here’s what’s going on.