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.

At a glance#

Codes are almost identical except for the import, replace import pandas with import xorbits.pandas will just work, so does numpy and so forth.

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.

All Xorbits APIs implemented or planned include:

API	Implemented version or plan
xorbits.pandas	v0.1.0
xorbits.numpy	v0.1.0
`xorbits.sklearn`	Planned in the near future
`xorbits.xgboost`	Planned in the near future
`xorbits.lightgbm`	Planned in the near future
`xorbits.xarray`	Planned in the future
`xorbits.scipy`	Planned in the future
`xorbits.statsmodels`	Planned in the future

Lightning fast speed#

Xorbits is the fastest compared to other popular frameworks according to our benchmark tests.

We did benchmarks for TPC-H at scale factor 100 (~100 GB datasets) and 1000 (~1 TB datasets). The performances are shown as below.

TPC-H SF100: Xorbits vs Dask#

https://xorbits.io/res/benchmark_dask.png

Q21 was excluded since Dask ran out of memory. Across all queries, Xorbits was found to be 7.3x faster than Dask.

TPC-H SF100: Xorbits vs Pandas API on Spark#

https://xorbits.io/res/benchmark_spark.png

Across all queries, the two systems have roughly similar performance, but Xorbits provided much better API compatibility. Pandas API on Spark failed on Q1, Q4, Q7, Q21, and ran out of memory on Q20.

TPC-H SF100: Xorbits vs Modin#

https://xorbits.io/res/benchmark_modin.png

Although Modin ran out of memory for most of the queries that involve heavy data shuffles, making the performance difference less obvious, Xorbits was still found to be 3.2x faster than Modin.

TPC-H SF1000: Xorbits#

Although Xorbits is able to pass all the queries in a row, Dask, Pandas API on Spark and Modin failed on most of the queries. Thus, we are not able to compare the performance difference now, and we plan to try again later.

For more information, see performance benchmarks.

Deployment#

Xorbits can be deployed on your local machine, largely deployed to a cluster via command lines, or deploy via Kubernetes and clouds.

Deployment	Description
Local	Run Xorbits on a local machine, e.g. your laptop
Cluster	Deploy Xorbits to existing cluster via command lines
Kubernetes	Deploy Xorbits to existing k8s cluster via python code
Cloud	Deploy Xorbits to various cloud platforms via python code
SLURM	Deploy Xorbits to SLURM

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.