Xorbits: scalable Python data science, familiar & fast.#

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.

Where to get it?#

The source code is currently hosted on GitHub at: xprobe-inc/xorbits

Binary installers for the latest released version are available at the Python Package Index (PyPI)

# PyPI
pip install xorbits

API compatibility#

As 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.

Lightning fast speed#

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.

Deployment#

Local#

On a single machine e.g. your laptop, optionally, you can initialize Xorbits on your own:

import xorbits
xorbits.init()

Or Xorbits will try to init for you when the first time some computation is triggered.

Bare metal#

Deploy to bare metal machines is dead easy as well. Ensure xorbits is installed on each machine, then:

For supervisor which schedules tasks to workers.

# for supervisor
python -m xorbits.supervisor -H <host_name> -p <supervisor_port> -w <web_port>

For workers which run actual computations.

# for worker
python -m xorbits.worker -H <host_name> -p <worker_port> -s <supervisor_ip>:<supervisor_port>

Then connect to the supervisor anywhere that can run Python code.

import xorbits
xorbits.init("http://<supervisor_ip>:<supervisor_web_port>")

Replace the <supervisor_ip> with the supervisor host name that you just specified and <supervisor_web_port> with the supervisor web port.

Getting involved#

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.
Twitter	Staying up-to-date on new features.