Xorbits is a scalable Python data science framework that aims to scale the whole Python data science world, including numpy, pandas, scikit-learn and many other libraries. It can leverage multi cores or GPUs to accelerate computation on a single machine, or scale out up to thousands of machines to support processing terabytes of data. In our benchmark test, Xorbits is the fastest framework among the most popular distributed data science frameworks.
As for the name of xorbits, it has many meanings, you can treat it as X-or-bits or X-orbits or xor-bits,
just have fun to comprehend it in your own way.
The source code is currently hosted on GitHub at: https://github.com/xprobe-inc/xorbits
Binary installers for the latest released version are available at the Python Package Index (PyPI).
# PyPI
pip install xorbitsAs long as you know how to use numpy, pandas and so forth, you would probably know how to use xorbits.
Here is an example.
| pandas | Xorbits |
import pandas as pd
ratings = pd.read_csv('ratings.csv')
movies = pd.read_csv('movies.csv')
m = ratings.groupby(
'MOVIE_ID', as_index=False).agg(
{'RATING': ['mean', 'count']})
m.columns = ['MOVIE_ID', 'RATING', 'COUNT']
m = m[m['COUNT'] > 100]
top_100 = m.sort_values(
'RATING', ascending=False)[:100]
top_100 = top_100.merge(
movies[['MOVIE_ID', 'NAME']])
print(top_100) |
import xorbits.pandas as pd
ratings = pd.read_csv('ratings.csv')
movies = pd.read_csv('movies.csv')
m = ratings.groupby(
'MOVIE_ID', as_index=False).agg(
{'RATING': ['mean', 'count']})
m.columns = ['MOVIE_ID', 'RATING', 'COUNT']
m = m[m['COUNT'] > 100]
top_100 = m.sort_values(
'RATING', ascending=False)[:100]
top_100 = top_100.merge(
movies[['MOVIE_ID', 'NAME']])
print(top_100) |
Codes are almost identical except for the import,
replace import pandas with import xorbits.pandas will just work,
so does numpy and so forth.
Xorbits is the fastest compared to other popular frameworks according to our benchmark tests.
We did a benchmark for TPC-H at scale factor 100 and 1000. The performances are shown as below.
Xorbits can be deployed on your local machine, or largely deployed to a cluster via command lines.
| Deployment | Description |
|---|---|
| Local | Running Xorbits on a local machine, e.g. laptop |
| Cluster | Deploy Xorbits to existing cluster via command lines |
The official documentation is hosted on: https://doc.xorbits.io
Main goals we want to achieve in the future include:
- Transitioning from pandas native to arrow native for data storage,
it will reduce the memory cost substantially and is more friendly for compute engine. - Introducing native engines that leverage technologies like vectorization and codegen to accelerate computations.
- Scale as many libraries and algorithms as possible!
More detailed roadmaps will be revealed soon, stay tuned!
The creators of Xorbits are mainly those of Mars, we built Xorbits currently on Mars to reduce duplicated work, but the vision of Xorbits suggests that it's not appropriate to put everything into Mars, instead, we need a new project to support the roadmaps better. In the future, we will replace some core internal components with other upcoming ones we will propose, stay tuned!
| Platform | Purpose |
|---|---|
| Discourse Forum | Asking usage questions and discussing development. |
| Github Issues | Reporting bugs and filing feature requests. |
| Slack | Collaborating with other Xorbits users. |
| StackOverflow | Asking questions about how to use Xorbits. |
| Staying up-to-date on new features. |