pyperformance
ga-extractor
pyperformance | ga-extractor | |
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6 | 3 | |
817 | 45 | |
0.9% | - | |
6.6 | 0.0 | |
19 days ago | over 1 year ago | |
Python | Python | |
MIT License | - |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pyperformance
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Phoronix: PyPerformance benchmark is on average 32% faster on Python 3.11 compared to 3.10 (on a Ryzen 9 5950X)
PyPerformance benchmark: https://github.com/python/pyperformance
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Faster CPython 3.12 Plan
25% number is from pyperformance benchmark suite, which you can replicate. Whether pyperformance is representative benchmark suite is another question.
https://github.com/python/pyperformance
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The Performance Benchmarks Comparing various combinations of GCC and Python
For each combination, We launch a GCC container and build Python with the GCC. Then run benchmarks using pyperformance and export to a JSON file.
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This Week In Python
pyperformance – Python Performance Benchmark Suite
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Hello, I created a interpreted dynamic programming language in C#. I use a bytecode compiler and a vm for interpretation. Right now I'm trying to optimise it. Any help would be great!
There are some standard benchmarks like fannkuch, deltablue, and so on (see a bunch for Python here) that you can port to your VM. They have adjustable values that you can raise or lower to increase or decrease the amount of time you take.
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Why is python so much slower on MacOS?
So I decided to run some actual benchmark suite. I found pyperformance which would seem to do the trick.
ga-extractor
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This Week In Python
ga-extractor – Tool for extracting Google Analytics data suitable for migrating to other platforms/databases
- ga-extractor - CLI tool for extracting Google Analytics data
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Goodbye, Google Analytics - Why and How You Should Leave The Platform
There's also no need to be scared of self-hosting the analytics engine yourself. Many of the open-source solutions can be spun up in matter of minutes and require very little resources to run. Even data migration can be quite simple as you've seen earlier in this article. If that's the route you want to go, but the extractor tool presented here doesn't support the target platform you'd like to migrate to, or you have some feedback to share, then feel free to create and an issue in GitHub repository and I will definitely try to help out.
What are some alternatives?
pybench - Python benchmark tool inspired by Geekbench.
AWS Data Wrangler - pandas on AWS - Easy integration with Athena, Glue, Redshift, Timestream, Neptune, OpenSearch, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL).
asv - Airspeed Velocity: A simple Python benchmarking tool with web-based reporting
Optimus - :truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
pyperf - Toolkit to run Python benchmarks
zef - Toolkit for graph-relational data across space and time
Nuitka - Nuitka is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11. You feed it your Python app, it does a lot of clever things, and spits out an executable or extension module.
python-live-gui
pyeventbus - Python Eventbus
GoAccess - GoAccess is a real-time web log analyzer and interactive viewer that runs in a terminal in *nix systems or through your browser.
Cython - The most widely used Python to C compiler
Killed by Google - Part guillotine, part graveyard for Google's doomed apps, services, and hardware.