Python⇒Speed | 极客分享

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Finding performance problems: profiling or logging?

If your software is running slowly in production, do you need profiling or logging?

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The best way to find performance bottlenecks: observing production

Performance bottlenecks causes vary widely, from network latency to software bugs. Observation in production may therefore be the only way to find them.

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The limits of Python vectorization as a performance technique

Vectorization is a great way to speed up your Python code, but you’re limited to specific operations on bulk data. Learn how to get pass these limitations.

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Finding performance bottlenecks in Celery tasks

Learn how to speed up Celery tasks by figuring out what’s causing them to be slow.

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Why new Macs break your Docker build, and how to fix it

New Macs can break your Docker image build in unexpected ways; learn why, and how to fix it.

#Python⇒Speed #docker #python

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Pandas vectorization: faster code, slower code, bloated memory

Vectorization in Pandas can make your code faster—except when it will make your code slower.

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Making pip installs a little less slow

Installing packages with pip, Poetry, and Pipenv can be slow. Learn how to ensure it’s not even slower, and a potential speed-up.

#Python⇒Speed #pip #python

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CPUs, cloud VMs, and noisy neighbors: the limits of parallelism

Learn how your computer (or virtual machine’s) CPU cores and how they’re configured limit the parallelism of your computations.

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Faster, more memory-efficient Python JSON parsing with msgspec

msgspec is a schema-based JSON encoder/decoder, which allows you to process large files with lower memory and CPU usage.

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When Python can't thread: a deep-dive into the GIL's impact

Python’s Global Interpreter Lock (GIL) stops threads from running in parallel or concurrently. Learn how to determine impact of the GIL on your code.

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Speeding up software with faster hardware: tradeoffs and alternatives

Throwing hardware at a software performance is often an easy solution, and sometimes the right one. Learn how to approach the decision, and some alternatives.

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Please stop writing shell scripts

It is quite difficult to write correct shell scripts; you’re much better off just using Python.

#Python⇒Speed #shell #python

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Processing large JSON files in Python without running out of memory

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Docker builds in CircleCI: go faster, and support newer Linux versions

CircleCI’s default version of Docker is quite old; make sure you choose a newer version if you want faster builds and support for newer Linux distributions.

#Python⇒Speed #docker #Linux

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Faster Python calculations with Numba: 2 lines of code, 13× speed-up

Python-based calculations, especially those that use NumPy, can run much faster by using the Numba library.

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Finding performance problems: profiling or logging?

The best way to find performance bottlenecks: observing production

The limits of Python vectorization as a performance technique

Finding performance bottlenecks in Celery tasks

Why new Macs break your Docker build, and how to fix it

Pandas vectorization: faster code, slower code, bloated memory

Making pip installs a little less slow

CPUs, cloud VMs, and noisy neighbors: the limits of parallelism

Faster, more memory-efficient Python JSON parsing with msgspec

When Python can't thread: a deep-dive into the GIL's impact

Speeding up software with faster hardware: tradeoffs and alternatives

Please stop writing shell scripts

Processing large JSON files in Python without running out of memory

Docker builds in CircleCI: go faster, and support newer Linux versions

Faster Python calculations with Numba: 2 lines of code, 13× speed-up

其他热门订阅